C autoradiography is a well established technique for structural and metabolic analysis of cells and tissues. The most common detection medium for this application is film emulsion, which offers unbeatable spatial resolution due to its fine granularity but at the same time has some limiting drawbacks such as poor linearity and rapid saturation. In recent years several digital detectors have been developed, following the technological transition from analog to digital-based detection systems in the medical and biological field. Even so such digital systems have been greatly limited by the size of their active area (a few square centimeters), which have made them unsuitable for routine use in many biological applications where sample areas are typically
Smith R, Rahni AAA, Jones J, Tahavori F, Wells K (2014) Motion estimation for nuclear medicine: A probabilistic approach, Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9034
Accurate, Respiratory Motion Modelling of the abdominal-thoracic organs serves as a pre-requisite for motion correction of Nuclear Medicine (NM) Images. Many respiratory motion models to date build a static correspondence between a parametrized external surrogate signal and internal motion. Mean drifts in respiratory motion, changes in respiratory style and noise conditions of the external surrogate signal motivates a more adaptive approach to capture non-stationary behavior. To this effect we utilize the application of our novel Kalman model with an incorporated expectation maximization step to allow adaptive learning of model parameters with changing respiratory observations. A comparison is made with a popular total least squares (PCA) based approach. It is demonstrated that in the presence of noisy observations the Kalman framework outperforms the static PCA model, however, both methods correct for respiratory motion in the computational anthropomorphic phantom to
Mackenzie A, Dance DR, Workman A, Yip M, Wells K, Young KC (2012) Conversion of mammographic images to appear with the noise and sharpness characteristics of a different detector and x-ray system., Med Phys 39 (5) pp. 2721-2734
PURPOSE: Undertaking observer studies to compare imaging technology using clinical radiological images is challenging due to patient variability. To achieve a significant result, a large number of patients would be required to compare cancer detection rates for different image detectors and systems. The aim of this work was to create a methodology where only one set of images is collected on one particular imaging system. These images are then converted to appear as if they had been acquired on a different detector and x-ray system. Therefore, the effect of a wide range of digital detectors on cancer detection or diagnosis can be examined without the need for multiple patient exposures. METHODS: Three detectors and x-ray systems [Hologic Selenia (ASE), GE Essential (CSI), Carestream CR (CR)] were characterized in terms of signal transfer properties, noise power spectra (NPS), modulation transfer function, and grid properties. The contributions of the three noise sources (electronic, quantum, and structure noise) to the NPS were calculated by fitting a quadratic polynomial at each spatial frequency of the NPS against air kerma. A methodology was developed to degrade the images to have the characteristics of a different (target) imaging system. The simulated images were created by first linearizing the original images such that the pixel values were equivalent to the air kerma incident at the detector. The linearized image was then blurred to match the sharpness characteristics of the target detector. Noise was then added to the blurred image to correct for differences between the detectors and any required change in dose. The electronic, quantum, and structure noise were added appropriate to the air kerma selected for the simulated image and thus ensuring that the noise in the simulated image had the same magnitude and correlation as the target image. A correction was also made for differences in primary grid transmission, scatter, and veiling glare. The method was validated by acquiring images of a CDMAM contrast detail test object (Artinis, The Netherlands) at five different doses for the three systems. The ASE CDMAM images were then converted to appear with the imaging characteristics of target CR and CSI detectors. RESULTS: The measured threshold gold thicknesses of the simulated and target CDMAM images were closely matched at normal dose level and the average differences across the range of detail diameters were -4% and 0% for the CR and CSI systems, r
Rashidnasab A, Elangovan P, Dance DR, Young KC, Yip M, Diaz O, Wells K (2012) Realistic simulation of breast mass appearance using random walk, MEDICAL IMAGING 2012: PHYSICS OF MEDICAL IMAGING 8313 SPIE-INT SOC OPTICAL ENGINEERING
The continual improvement in spatial resolution of Nuclear Medicine (NM) scanners has made accurate compensation of patient motion increasingly important. A major source of corrupting motion in NM acquisition is due to respiration. Therefore a particle filter (PF) approach has been proposed as a powerful method for motion correction in NM. The probabilistic view of the system in the PF has an advantage in that it considers the complexity and uncertainties of respiratory motion. Tests using the XCAT phantom have previously shown the possibility of estimating unseen organ configurations using training data that only consist of a single respiratory cycle. This paper builds upon previous work in two ways: (i) this is the first evaluation of a PF framework using clinical 4D thoracic CT data; and, (ii) this implementation uses a kernel density estimation (KDE) representation for the transition model, thus taking advantage of the PF's ability to use a wider range of stochastic models. The results show some improvement with the use of a KDE-based transition model and indicates that the PF should be applicable to clinical data. © 2011 IEEE.
Thin tissue autoradiography is an imaging modality where ex-vivo tissue sections are placed in direct contact with autoradiographic film. These tissue sections contain a radiolabelled ligand bound to a specific biomolecule under study. This radioligand emits ² - or ²+ particles ionizing silver halide crystals in the film. High spatial resolution autoradiograms are obtained using low energy radioisotopes, such as 3H where an intrinsic 0.1-1 ½m spatial resolution can be achieved. Several digital alternatives have been presented over the past few years to replace conventional film but their spatial resolution has yet to equal film, although silicon-based imaging technologies have demonstrated higher sensitivity compared to conventional film. It will be shown in this work how pixel size is a critical parameter for achieving high spatial resolution for low energy uncollimated beta imaging. In this work we also examine the confounding factors impeding silicon-based technologies with respect to spatial resolution. The study considers charge diffusion in silicon and detector noise, and this is applied to a range of radioisotopes typically used in autoradiography. Finally an optimal detector geometry to obtain the best possible spatial resolution for a specific technology and a specific radioisotope is suggested. © 2010 Institute of Physics and Engineering in Medicine.
Elangovan P, MacKenzie A, Diaz O, Rashidnasab A, Dance DR, Young KC, Warren LM, Shaheen E, Bosmans H, Bakic PR, Wells K (2012) A modelling framework for evaluation of 2D-mammography and breast tomosynthesis systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7361 LNCS pp. 338-345
Planar 2D X-ray mammography is the most common screening technique used for breast cancer detection. Digital breast tomosynthesis (DBT) is a new and emerging technology that overcomes some of the limitations of conventional planar imaging. However, it is important to understand the impact of these two modalities on cancer detection rates and patient recall. Since it is difficult to adequately evaluate different modalities clinically, a collection of modeling tools is introduced in this paper that can be used to emulate the image acquisition process for both modalities. In this paper, we discuss image simulation chains that can be used for the evaluation of 2D-mammography and DBT systems in terms of both technical factors and observer studies. © 2012 Springer-Verlag Berlin Heidelberg.
This is the first study of partial volume effect in quantifying renal function on dynamic contrast enhanced magnetic resonance imaging. Dynamic image data were acquired for a cohort of 10 healthy volunteers. Following respiratory motion correction, each voxel location was assigned a mixing vector representing the 'overspilling' contributions of each tissue due to the convolution action of the imaging system's point spread function. This was used to recover the true intensities associated with each constituent tissue. Thus, non-renal contributions from liver, spleen and other surrounding tissues could be eliminated from the observed time-intensity curves derived from a typical renal cortical region of interest. This analysis produced a change in the early slope of the renal curve, which subsequently resulted in an enhanced glomerular filtration rate estimate. This effect was consistently observed in a Rutland-Patlak analysis of the time-intensity data: the volunteer cohort produced a partial volume effect corrected mean enhancement of 36% in relative glomerular filtration rate with a mean improvement of 7% in r(2) fitting of the Rutland-Patlak model compared to the same analysis undertaken without partial volume effect correction. This analysis strongly supports the notion that dynamic contrast enhanced magnetic resonance imaging of kidneys is substantially affected by the partial volume effect, and that this is a significant obfuscating factor in subsequent glomerular filtration rate estimation. (C) 2009 Elsevier Ireland Ltd. All rights reserved.
CMOS imaging technology can be applied to Chemiluminescence
Western Blotting as a potential imaging alternative
technology to using conventional film-emulsion. In this work
the authors present a through investigation on the performance
of CMOS Active Pixel Sensors for using in western blotting.
Chemiluminescence labeling is a well established technique to
detect proteins and presents several advantages compared with
the fluorescence labeling. In fact it requires neither external illumination
nor filtering optics and does not produce an inherently
label-related background to correct. In this paper the first results
of imaging a secondary antibody labeled with chemiluminescence
reagents obtained with a CMOS sensor operating at room
temperature are presented
Tomographic biomedical images are commonly affected by an imaging artefact known as the partial volume (PV) effect. The PV effect produces voxels composed of a mixture of tissues in anatomical magnetic resonance imaging (MRI) data resulting in a continuity of these tissue classes. Anatomical MRI data typically consist of a number of contiguous regions of tissues or even contiguous regions of PV voxels. Furthermore discontinuities exist between the boundaries of these contiguous image regions. The work presented here probabilistically models the PV effect using spatial regularization in the form of continuous Markov random fields (MRFs) to classify anatomical MRI brain data, simulated and real. A unique approach is used to adaptively control the amount of spatial regularization imposed by the MRF. Spatially derived image gradient magnitude is used to identify the discontinuities between image regions of contiguous tissue voxels and PV voxels, imposing variable amounts of regularization determined by simulation. Markov chain Monte Carlo (MCMC) is used to simulate the posterior distribution of the probabilistic image model. Promising quantitative results are presented for PV classification of simulated and real MRI data of the human brain. © 2008 Institute of Physics and Engineering in Medicine.
Cabello J, Bailey A, Kitchen I, Clark A, Crooks J, Halsall R, Key-Charriere M, Martin S, Prydderch M, Turchetta R, Wells K (2007) Digital autoradiography using CCD and CMOS imaging technology, 2006 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOL 1-6 pp. 2607-2612
In nuclear medicine, there is a significant research focus in developing a new approach in monitoring, tracking and compensating respiratory motion during image acquisition. We address this by attempting to model the respiratory cycle pattern and finding a method that describes the configuration of the anterior surface which then correlates with the internal position/configuration of the internal organ as a foundation for motion compensation. This paper presents novel work in parameterizing external respiratory motion using a method based on the variation of abdominal vs. thoracic surface markers to investigate inter- and intra-subject variation. The dominant mode of variation of the Abdominal and Thoracic surfaces during respiration using Principle Component Analysis (PCA) is studied. This demonstrates that pattern of TS vs AS motion appears temporally at a global level stable. Thus although breathing style is consistent within a given subject, we there observe temporal changes in the amplitude and density of the PDF in intra-subject data.
Cabello J, Wells K, Bailey A, Kitchen I (2007) Semi-automatic elastic registration applied to a beta-autoradiography brain atlas, 2007 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD, VOLS 1-11 pp. 4303-4307 IEEE
Grenier N, Mendichovszky L, De Senneville BD, Rouiol S, Desbarats P, Pedersen M, Wells K, Frokiaer J, Gordon I (2008) Measurement of glomerular filtration rate with magnetic resonance imaging: Principles, limitations, and expectations, SEMINARS IN NUCLEAR MEDICINE 38 (1) pp. 47-55 W B SAUNDERS CO-ELSEVIER INC
Recently CMOS active pixels sensors (APSs) have become a valuable alternative to amorphous silicon and selenium flat panel imagers (FPIs) in bio-medical imaging applications. CMOS APSs can now be scaled up to the standard 20cm diameter wafer size by means of a reticle stitching block process. However, despite wafer scale CMOS APS being monolithic, sources of non-uniformity of response and regional variations can persist representing a significant challenge for wafer scale sensor response. Non-uniformity of stitched sensors can arise from a number of factors related to the manufacturing process, including variation of amplification, variation between readout components, wafer defects and process variations across the wafer due to manufacturing processes. This paper reports on an investigation into the spatial non-uniformity and regional variations of a wafer scale stitched CMOS APS. For the first time a per-pixel analysis of the electro-optical performance of a wafer CMOS APS is presented, to address inhomogeneity issues arising from the stitching techniques used to manufacture wafer scale sensors. A complete model of the signal generation in the pixel array has been provided and proved capable of accounting for noise and gain variations across the pixel array. This novel analysis leads to readout noise and conversion gain being evaluated at pixel level, stitching block level and in regions of interest, resulting in a coefficient of variation 1.9%. The uniformity of the image quality performance has been further investigated in a typical x-ray application, i.e. mammography, showing a uniformity in terms of CNR among the highest when compared with mammography detectors commonly used in clinical practice. Finally, in order to compare the detection capability of this novel APS with the technology currently used (i.e. FPIs), theoretical evaluation of the detection quantum efficiency (DQE) at zero-frequency has been performed, resulting in a higher DQE for this detector compared to FPIs. Optical characterization, x-ray contrast measurements and theoretical DQE evaluation suggest that a trade off can be found between the need of a large imaging area and the requirement of a uniform imaging performance, making the DynAMITe large area CMOS APS suitable for a range of bio-medical applications. © 2014 Institute of Physics and Engineering in Medicine.
Jones J, Lewis E, Abd Rahni A, Ezhil V, Wells K (2012) Mosaics of polynomial transformations giving a patient specific registration to reduce breathing motion artefacts, IEEE Nuclear Science Symposium Conference Record pp. 3066-3072
Nuclear Medicine (NM) imaging is an important diagnostic tool, used widely in the field of oncology amongst others. However, NM images suffer from significant blurring due to inevitable patient motion, such as breathing, coughing and other voluntary or involuntary movement. Advances in detector technology and reconstruction techniques have led to a steady improvement in NM spatial resolution and the problems posed by patient motion are therefore becoming increasingly significant, particularly for PET/CT. Many correction schemes will require gated images to be aligned. This paper describes a method of registering major organs in piecewise fashion called virtual dissection. Results from processing synthetic data and one set of patient data are presented. A key feature is the possibility to use a mixture of registration techniques on a single set of data and combine the results into a single set of output images. © 2011 IEEE.
Loveland J, Gundogdu O, Morton E, Wells K, Bradley DA (2010) Phase contrast imaging: Effect of increased object-detector distances at X-ray diagnostic and megavoltage energies, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Alnowami M, Alnwaimi B, Tahavori F, Copland M, Wells K (2012) A Quantitative Assessment of using the Kinect for Xbox360 (TM) For Respiratory Surface Motion Tracking, MEDICAL IMAGING 2012: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING 8316 SPIE-INT SOC OPTICAL ENGINEERING
Compensation for respiratory motion has been identified as a crucial factor in achieving high resolution Nuclear Medicine (NM) imaging. Many motion correction approaches have been studied and they are seen to have advantages over simpler approaches such as respiratory gating. However, all motion correction approaches rely on an assumption or estimation of respiratory motion. This paper builds upon previous work in recursive Bayesian estimation of respiratory motion assuming a stereo camera observation of the motion of the external torso surface. This paper compares the performance of a modified autoregressive transition model against the previously presented linear transition model used when estimating motion within a 4D dataset generated from the XCAT phantom. © 2013 SPIE.
Alnafea M, Wells K, Spyrou NM, Guy M (2007) Preliminary Monte Carlo study of coded aperture imaging with a CZT gamma camera system for scintimammography, NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 573 (1-2) pp. 122-125 ELSEVIER SCIENCE BV
Smith RL, Wells K, Jones J, Dasari P, Lindsay C, King M (2013) Toward a framework for high resolution parametric respiratory motion modelling, IEEE Nuclear Science Symposium Conference Record
A framework to facilitate the realization of dynamic MRI data with simultaneously increased temporal and spatial resolution is proposed. Deformable registration to a reference frame of spatially sparse, high temporally resolved two dimensional sagittal slices acquired sequentially across a volunteers lateral dimension serve as a subset of incomplete observation of full three dimensional vector fields. Registration of an averaged, re-binned, single respiratory cycle with full spatial sampling serves as a basis for estimation of full three dimensional vector fields derived from the sparse subset. The inverse of the estimated full 3D vector fields from sparse measurements allows propagation of a high resolution, re binned static volume with breathing modes derived from the sparse dynamic data. Proof of concept experiments are undertaken with the anthropomorphic XCAT phantom. A quantitative evaluation of full vector fields derived from sparse samples in comparison to their ground truth results in a mean error of the order of 1mm. A qualitative assessment of the motion of a propagated high resolution static MRI volume is presented. © 2013 IEEE.
In recent times, the security focus for civil aviation has shifted from hijacking in the 1980s, towards deliberate sabotage. X-ray imaging provides a major tool in checked baggage inspection, with various sensitive techniques being brought to bear in determining the form, and density of items within luggage as well as other material dependent parameters. This review first examines the various challenges to X-ray technology in securing a safe system of passenger transportation. An overview is then presented of the various conventional and less conventional approaches that are available to the airline industry, leading to developments in state-of-the-art imaging technology supported by enhanced machine and observer-based decision making principles. © 2012 Elsevier Ltd.
Digital breast tomosynthesis (DBT) is suggested to have superior performance compared to 2D mammography in terms of cancer visibility, especially in the case of dense breasts. However, the overall performance of tomosynthesis for screening applications, and the manner in which tomosynthesis should be optimally used for screening remains unclear. This motivates the development of software tools that can insert user-defined synthetic pathology of realistic appearance into clinical tomosynthesis images for subsequent use in virtual clinical trials. We present a method for inserting lesions grown using Diffusion Limited Aggregation, previously validated in 2D mammograms, into clinical DBT images. A preliminary pilot study was used to validate the realism of the masses, wherein three readers each viewed 19 cases and rated the realism of the inserted masses. Each case included a simulated mass inserted in the tomosynthesis projections and the counterpart digital 2D mammogram. These results show that masses can be successfully embedded in the tomosynthesis projections and can produce visually authentic DBT images containing synthetic pathology. These results will be used to further optimize the appearance of these masses in DBT for an upcoming validation. © 2013 SPIE.
Bradley DA, Wells K (2013) Reprint of: Biomedical applications reviewed: Hot topic areas, Radiation Physics and Chemistry
Making reference to the British Journal of Radiology and competitor journal titles, we look at the general area of biomedical physics, reviewing some of the associated topics in ionising radiation research attracting interest over the past 2 years. We also reflect on early developments that have paved the way for these endeavours. The talk is illustrated by referring to a number of biomedical physics areas in which this group has been directly involved, including novel imaging techniques that address compositional and structural makeup as well as use of elastically scattered X-ray phase contrast, radiation damage linking to possible pericardial effects in radiotherapy, simulation of microvascularity and oxygenation with a focus of radiation resistant hypoxic tumours, issues of high spatial resolution dosimetry and tissue interface radiotherapy with doses enhanced through use of high atomic number photoelectron conversion media. © 2013 Elsevier Ltd. All rights reserved.
Ashrani AA, Lewis E, Wells K (2013) Development of a semi-automated segmentation framework for thoracic-abdominal organs, IEEE ICSIPA 2013 - IEEE International Conference on Signal and Image Processing Applications pp. 232-236
Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. The preliminary results of the framework is presented and discussed with proposals for future work. © 2013 IEEE.
We present a method for obtaining attenuation maps for use in emission computed tomography (ECT) using ultra low dose CT data (at 140kVp, down to 10mA). This is achieved using a recursive k-means clustering method, the output of which initializes successive parameter-less region growing procedures. The method automatically produces templates corresponding to bone, lung, soft and dense tissue (muscle and fat). The segmentation of each tissue class from k-means clustering is used to compensate for the higher statistical noise variation seen at lower dose. The use of the region grower provides local contextual information that minimizes the impact of global noise. The templates were assigned appropriate linear attenuation coefficients and then convolved with the PET/SPECT system's PSF. This approach was applied to a dataset from an experimental anthropomorphic phantom exposed to systematically reducing CT dose derived from an X-ray beam at 140kVp and varying current from 160mA (full diagnostic dose) to 10mA (ultra low dose). Preliminary results show that for the purpose of CT attenuation correction, it is possible to successfully produce attenuation maps at ultra low dose with very low error (compared to full diagnostic dose) if used with the segmentation method presented. © 2006 IEEE.
Rashidnasab A, Elangovan P, Mackenzie A, Dance DR, Young KC, Bosmans H, Wells K (2015) Virtual clinical trials using inserted pathology in clinical images: investigation of assumptions for local glandularity and noise, MEDICAL IMAGING 2015: PHYSICS OF MEDICAL IMAGING 9412 SPIE-INT SOC OPTICAL ENGINEERING
Bradley DA, Hashim S, Saripan MI, Wells K, Dunn WL (2011) Photon signature analysis using template matching, Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 652 (1) pp. 466-469
We describe an approach to detect improvised explosive devices (IEDs) by using a template matching procedure. This approach relies on the signature due to backstreaming ³ photons from various targets. In this work we have simulated cylindrical targets of aluminum, iron, copper, water and ammonium nitrate (nitrogen-rich fertilizer). We simulate 3.5 MeV source photons distributed on a plane inside a shielded area using Monte Carlo N-Particle (MCNPTM) code version 5 (V5). The 3.5 MeV source gamma rays yield 511 keV peaks due to pair production and scattered gamma rays. In this work, we simulate capture of those photons that backstream, after impinging on the target element, toward a NaI detector. The captured backstreamed photons are expected to produce a unique spectrum that will become part of a simple signal processing recognition system based on the template matching method. Different elements were simulated using different sets of random numbers in the Monte Carlo simulation. To date, the sum of absolute differences (SAD) method has been used to match the template. In the examples investigated, template matching was found to detect all elements correctly. © 2011 Elsevier B.V.
A novel method has been developed for generating quasi-realistic voxel phantoms which simulate the compressed breast in mammography and digital breast tomosynthesis (DBT). The models are suitable for use in virtual clinical trials requiring realistic anatomy which use the multiple alternative forced choice (AFC) paradigm and patches from the complete breast image. The breast models are produced by extracting features of breast tissue components from DBT clinical images including skin, adipose and fibro-glandular tissue, blood vessels and Cooper?s ligaments. A range of different breast models can then be generated by combining these components. Visual realism was validated using a receiver operating characteristic (ROC) study of patches from simulated images calculated using the breast models and from real patient images. Quantitative analysis was undertaken using fractal dimension and power spectrum analysis. The average areas under the ROC curves for 2D and DBT images were 0.51 ± 0.06 and 0.54 ± 0.09 demonstrating that simulated and real images were statistically indistinguishable by expert breast readers (7 observers); errors represented as one standard error of the mean. The average fractal dimensions (2D, DBT) for real and simulated images were (2.72 ± 0.01, 2.75 ± 0.01) and (2.77 ± 0.03, 2.82 ± 0.04) respectively; errors represented as one standard error of the mean. Excellent agreement was found between power spectrum curves of real and simulated images, with average ² values (2D, DBT) of (3.10 ± 0.17, 3.21 ± 0.11) and (3.01 ± 0.32, 3.19 ± 0.07) respectively; errors represented as one standard error of the mean. These results demonstrate that radiological images of these breast models realistically represent the complexity of real breast structures and can be used to simulate patches from mammograms and DBT images that are indistinguishable from patches from the corresponding real breast images. The method can generate about 500 radiological patches (~30mm × 30mm) per day for AFC experiments on a single workstation. This is the first study to quantitatively validate the realism of simulated radiological breast images using direct blinded comparison with real data via the ROC paradigm with expert breast readers
Digital breast tomosynthesis (DBT) is under consideration to replace or to be used in combination with 2D-mammography in breast screening. The aim of this study was the comparison of the detection of microcalcification clusters by human observers in simulated breast images using 2D-mammography, narrow angle (15°/15 projections) and wide angle (50°/25 projections) DBT. The effects of the cluster height in the breast and the dose to the breast on calcification detection were also tested. Simulated images of 6 cm thick compressed breasts were produced with and without microcalcification clusters inserted, using a set of image modelling tools for 2D-mammography and DBT. Image processing and reconstruction were performed using commercial software. A series of 4-alternative forced choice (4AFC) experiments was conducted for signal detection with the microcalcification clusters as targets. Threshold detectable calcification diameter was found for each imaging modality with standard dose: 2D-mammography: 2D-mammography (165 ± 9 µm), narrow angle DBT (211 ± 11 µm) and wide angle DBT (257 ± 14 µm). Statistically significant differences were found when using different doses, but different geometries had a greater effect. No differences were found between the threshold detectable calcification diameters at different heights in the breast. Calcification clusters may have a lower detectability using DBT than 2D imaging.
Patient respiratory motion is a major problem during external beam radiotherapy of the thoracic and abdominal regions due to the associated organ and target motion. In addition, such motion introduces uncertainty in both radiotherapy planning and delivery and may potentially vary between the planning and delivery sessions. The aim of this work is to examine subject-specific external respiratory motion and its associated drift from an assumed average cycle which is the basis for many respiratory motion compensated applications including radiotherapy treatment planning and delivery. External respiratory motion data were acquired from a group of 20 volunteers using a marker-less 3D depth camera, Kinect for Windows. The anterior surface encompassing thoracic and abdominal regions were subject to principal component analysis (PCA) to investigate dominant variations. The first principal component typically describes more than 70% of the motion data variance in the thoracic and abdominal surfaces. Across all of the subjects used in this study, 58% of subjects demonstrate largely abdominal breathing and 33% exhibited largely thoracic dominated breathing. In most cases there is observable drift in respiratory motion during the 300s capture period, which is visually demonstrated using Kernel Density Estimation. This study demonstrates that for this cohort of apparently healthy volunteers, there is significant respiratory motion drift in most cases, in terms of amplitude and relative displacement between the thoracic and abdominal respiratory components. This has implications for the development of effective motion compensation methodology. © 2014 SPIE.
Esposito M, Anaxagoras T, Fant A, Wells K, Konstantinidis A, Osmond JPF, Evans PM, Speller RD, Allinson NM (2011) DynAMITe: A wafer scale sensor for biomedical applications, Journal of Instrumentation 6 (12) C12064 IOP Publishing
In many biomedical imaging applications Flat Panel Imagers (FPIs) are currently the most common option. However, FPIs possess several key drawbacks such as large pixels, high noise, low frame rates, and excessive image artefacts. Recently Active Pixel Sensors (APS) have gained popularity overcoming such issues and are now scalable up to wafer size by appropriate reticule stitching. Detectors for biomedical imaging applications require high spatial resolution, low noise and high dynamic range. These figures of merit are related to pixel size and as the pixel size is fixed at the time of the design, spatial resolution, noise and dynamic range cannot be further optimized. The authors report on a new rad-hard monolithic APS, named DynAMITe (Dynamic range Adjustable for Medical Imaging Technology), developed by the UK MI-3 Plus consortium. This large area detector (12.8 cm × 12.8 cm) is based on the use of two different diode geometries within the same pixel array with different size pixels (50 ¼m and 100 ¼m). Hence the resulting device can possess two inherently different resolutions each with different noise and saturation performance. The small and the large pixel cameras can be reset at different voltages, resulting in different depletion widths. The larger depletion width for the small pixels allows the initial generated photo-charge to be promptly collected, which ensures an intrinsically lower noise and higher spatial resolution. After these pixels reach near saturation, the larger pixels start collecting so offering a higher dynamic range whereas the higher noise floor is not important as at higher signal levels performance is governed by the Poisson noise of the incident radiation beam. The overall architecture and detailed characterization of DynAMITe will be presented in this paper.
Motion correction of the abdominal-thoracic region is one of the main research challenges in tomographic nuclear medicine imaging. We address this issue with a flexible data-driven method of motion correction. This uses marker-less stereo tracking of the anterior abdominal-chest surface and a 'virtual dissection'-based registration approach, combined within a novel paricle filtering (PF) framework. The key advantage to this data driven approach is that we do not make gross prior assumptions on the configuration of the hidden state of the system, i.e. the configuration of the internal organs during the emission acquisition process. Instead, at some given time instance, we infer the hidden or unobserved internal organ configuration by using Monte Carlo sampling (and then filtering) of various propositions, or 'particles'. Such estimates are calculated using the previous state (of the internal organs) plus some noise or perturbation of the expected transition to the current state or configuration. We then compare estimated representations of the abdominal-chest anterior surface, derived from the particles or propositions, with an observation of the actual surface, derived from a marker-less stereo imaging system. By examining the differences between the estimated particle or proposition surfaces and actual observed surface data, we can infer the current configuration of the internal organs. After an update step, the process is then repeated for subsequent time points in the emission data. This allows the system to flexibly adopt previously unknown configurations of the internal organs, and thus allow for different modes of breathing (e.g. abdominal vs thoracic-based motion) to be represented. Preliminary results are presented based on using the XCAT phantom to demonstrate the PF approach and the 'virtual-dissection' registration process, alongside results of a parameterized anterior surface model derived from human volunteer data. ©2009 IEEE.
Gutierrez DR, Jia B, Chiverton J, Wells K, Partidge M (2007) Partial volume correction for image-generated arterial input functions, IEEE Nuclear Science Symposium Conference Record 4 pp. 2091-2094
We propose a method for Partial Volume correction and intensity recovery that models blood vessels as small cylinders of known diameter. We use a Bayesian classifier that explicitly models the effects of the point spread function on these cylinders. Although the method requires prior knowledge of the cylinder/arterial width, there is no requirement for any registration. A further advantage is that Region Of Interest (ROI) definition can be limited to only a few axial slices, thus minimizing time averaging. Furthermore, ROI selection requires only approximate placement around the target artery, encompassing both artery and background tissue, so that recovered data values are not operator-dependent. We present results for classifier performance on simulated phantom data of hot cylindrical inserts in a warm background with different contrast to noise ratios. © 2006 IEEE.
Nuclear Medicine (NM) imaging serves as a powerful diagnostic tool for imaging of biochemical and physiological processes in vivo. The degradation in spatial image resolution caused by the often irregular respiratory motion must be corrected to achieve high resolution imaging. In order perform motion correction more accurately, it is proposed that patient motion obtained from 4D MRI can be used to analyse respiratory motion. To extract motion from the dynamic MRI dataset an organ wise intensity based affine registration framework is proposed and evaluated. Comparison of the resultant motion obtained within selected organs is made against an open source free form deformation algorithm. For validation, the correlation of the results of both techniques to a previous study of motion in 20 patients is found. Organwise affine registration correlates very well (r = 0:9) with a previous study (Segars et al., 2007)1 whilst free form deformation shows little correlation (r = 0:3). This increases the confidence of the organ wise affine registration framework being an effective tool to extract motion from dynamic anatomical datasets. © 2013 SPIE.
In external beam radiotherapy, patient misalignment during set-up and motion during treatment may result in lost dose to target tissue and increased dose to normal tissues, reducing therapeutic benefit. The most common method for initial patient setup uses room mounted lasers and surface marks on the skin. We propose to use the Microsoft Kinect which can capture a complete patient skin surface representing a multiplicity of 3D points in a fast reproducible, marker-less manner. Our first experiments quantitatively assess the technical performance of Kinect technology using a planar test object and a precision motion platform to compare the performance of Kinect for Xbox and Kinect for Windows. Further experiments were undertaken to investigate the likely performance of using the Kinect during treatment to detect respiratory motion, both in supine and prone positions. The Windows version of the Kinect produces superior performance of less than 2mm mean error at 80-100 cm distance. © 2013 IEEE.
Virtual clinical trials (VCTs) are increasingly being seen as a viable pre-clinical method for evaluation of imaging systems in breast cancer screening. The CR-UK funded OPTIMAM project is aimed at producing modelling tools for use in such VCTs. In the initial phase of the project, modelling tools were produced to simulate 2D-mammography and digital breast tomosynthesis (DBT) imaging systems. This paper elaborates on the new tools that have recently been developed for the current phase of the OPTIMAM project. These new additions to the framework include tools for simulating synthetic breast tissue, spiculated masses and variable-angle DBT systems. These tools are described in the paper along with the preliminary validation results. Four-alternative forced choice (4-AFC) type studies deploying these new tools are underway. The results of the ongoing 4AFC studies investigating minimum detectable contrast/size of masses/microcalcifications for different modalities and system designs are presented.
Although X-ray mammography is the gold standard technique for breast cancer detection, it suffers from limitations due to tissue superposition which could either obscure or mimic a breast lesion. Dedicated breast computed-tomography (BrCT) represents an alternative technology with the potential to overcome these limitations. However, this technology is still under investigation in order to study and improve certain parameters (e.g. dose, scattered radiation, etc.). In this work, an image simulation framework is proposed to generate realistic BrCT images and spectral imaging analysis is explored to enhance the contrast of breast lesions. Results illustrated an improvement in contrast between 5 and 10% when the final image is reconstructed using X-ray photons with energies between 21 and 30 keV, in comparison with the reconstructed image from the polychromatic energy spectrum recorded within the image receptor. © 2013 IEEE.
Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers? kidney DCE-MRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of 99%99% of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD.
Cabello J, Bailey A, Kitchen I, Clark A, Crooks J, Halsall R, Key-Charriere M, Martin S, Prydderch M, Turchetta R, Wells K (2006) Digital Autoradiography using CCD and CMOS Imaging Technology, 2006 IEEE Nuclear Science Symposium Conference Record 4 pp. 2607-2612
Institute of Electrical and Electronics Engineers
CCD and CMOS imaging technologies can be applied to thin tissue Autoradiography as potential imaging alternatives
to using conventional film. In this work, we compare two
particular devices; a CCD operating in slow scan mode and a
CMOS-based Active Pixel sensor, operating at near video rates. Both imaging sensors have been operated at room temperature with images produced from calibrated microscales and radiolabelled tissue samples. We also compare these digital imaging technologies with the use of conventional film. We show first comparative results obtained with 14C calibrated microscales and 35S radiolabelled tissue sections. We also present first results of 3H images produced under direct irradiation of a CCD sensor operating at room temperature. Compared to film, silicon-based imaging technologies exhibit enhanced sensitivity, dynamic range and linearity.
This work investigates the detection performance of specialist and non-specialist observers for different targets in 2D-mammography and digital breast tomosynthesis (DBT) using the OPTIMAM virtual clinical trials (VCT) Toolbox and a 4-alternative forced choice (4AFC) assessment paradigm.
Using 2D-mammography and DBT images of virtual breast phantoms, we compare the detection limits of simple uniform spherical targets and irregular solid masses. Target diameters of 4 mm and 6 mm have been chosen to represent target sizes close to the minimum detectable size found in breast screening, across a range of controlled contrast levels. The images were viewed by a set of specialist observers (five medical physicists and six experienced clinical readers) and five non-specialists.
Combined results from both observer groups indicate that DBT has a significantly lower detectable threshold contrast than 2D-mammography for small masses (4 mm: 2.1% [DBT] versus 6.9% [2D]; 6 mm: 0.7% [DBT] versus 3.9% [2D]) and spheres (4 mm: 2.9% [DBT] versus 5.3% [2D]; 6 mm: 0.3% [DBT] versus 2.2% [2D]) (p Â 0.0001). Both observer groups found spheres significantly easier to detect than irregular solid masses for both sizes and modalities (p Â 0.0001) (except 4 mm DBT). The detection performances of specialist and non-specialist observers were generally found to be comparable, where each group marginally outperformed the other in particular detection tasks. Within the specialist group, the clinical readers performed better than the medical physicists with irregular masses (p Â 0.0001).
The results indicate that using spherical targets in such studies may produce over-optimistic detection thresholds compared to more complex masses, and that the superiority of DBT for detecting masses over 2D-mammography has been quantified. The results also suggest specialist observers may be supplemented by non-specialist observers (with training) in some types of 4AFC studies.
Gilat Schmidt Taly, Lo Joseph Y., Chen Guang-Hong, Wilkinson Louise, Wallis Matthew G., Given-Wilson Rosalind M., Mihalas E., Dance David R., Wells Kevin, Young Kenneth C., Elangovan P., Alnowami Majdi R., Cooke V. (2018) Design and validation of biologically inspired spiculated breast lesion models utilizing structural tissue distortion, Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105730B (9 March 2018)
Society of Photo-optical Instrumentation Engineers (SPIE)
The use of conventional clinical trials to optimise technology and techniques in breast cancer screening carries with it issues of dose, high cost and delay. This has motivated the development of Virtual Clinical Trials (VCTs) as an alternative in-silico assessment paradigm. However, such an approach requires a set of modelling tools that can realistically represent the key biological and technical components within the imaging chain. The OPTIMAM image simulation toolbox provides a complete validated end-to-end solution for VCTs, wherein commonly-found regular and irregular lesions can be successfully and realistically simulated. As spiculated lesions are the second most common form of solid mass we report on our latest developments to produce realistic spiculated lesion models, with particular application in Alternative Forced Choice trials. We make use of sets of spicules drawn using manually annotated landmarks and interpolated by a fitted 3D spline for each spicule. Once combined with a solid core, these are inserted into 2D and tomosynthesis image segments and blended using a combination of elongation, rotational alignment with background, spicule twisting and core radial contraction effects. A mixture of real and simulated images (86 2D and 86 DBT images) with spiculated lesions were presented to an experienced radiologist in an observer study. The latest observer study results demonstrated that 88.4% of simulated images of lesions in 2D and 67.4% of simulated lesions in DBT were rated as definitely or probably real on a six-point scale. This presents a significant improvement on our previous work which did not employ any background blending algorithms to simulate spiculated lesions in clinical images.
Samuelson Frank W., Nishikawa Robert M., Patel Mishal, Elangovan Prem, Wells Kevin, Halling-Brown Mark D., Awais Muhammad, Dance David R., Young Kenneth, Mills G., Alnowami Majdi (2018) A deep learning model observer for use in alterative forced choice virtual clinical trials, Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Society of Photo-optical Instrumentation Engineers (SPIE)
Virtual clinical trials (VCTs) represent an alternative assessment paradigm that overcomes issues of dose, high cost and delay encountered in conventional clinical trials for breast cancer screening. However, to fully utilize the potential benefits of VCTs requires a machine-based observer that can rapidly and realistically process large numbers of experimental conditions. To address this, a Deep Learning Model Observer (DLMO) was developed and trained to identify lesion targets from normal tissue in small (200 x 200 pixel) image segments, as used in Alternative Forced Choice (AFC) studies. The proposed network consists of 5 convolutional layers with 2x2 kernels and ReLU (Rectified Linear Unit) activations, followed by max pooling with size equal to the size of the final feature maps and three dense layers. The class outputs weights from the final fully connected dense layer are used to consider sets of n images in an n-AFC paradigm to determine the image most likely to contain a target. To examine the DLMO performance on clinical data, a training set of 2814 normal and 2814 biopsy-confirmed malignant mass targets were used. This produced a sensitivity of 0.90 and a specificity of 0.92 when presented with a test data set of 800 previously unseen clinical images. To examine the DLMOs minimum detectable contrast, a second dataset of 630 simulated backgrounds and 630 images with simulated lesion and spherical targets (4mm and 6mm diameter), produced contrast thresholds equivalent to/better than human observer performance for spherical targets, and comparable (12 % difference) for lesion targets.
Edmunds David M., Gothard Lone, Khabra Komel, Kirby Anna, Madhale Poonam, McNair Helen, Roberts David, Tang KK, Symonds-Tayler Richard, Tahavori Fatemeh, Wells Kevin, Donovan Ellen (2018) Low-cost Kinect Version 2 imaging system for breath hold monitoring and gating: Proof of concept study for breast cancer VMAT radiotherapy, Journal of Applied Clinical Medical Physics 19 (3) pp. 71-78
American Association of Physicists in Medicine
Voluntary inspiration breath hold (VIBH) for left breast cancer patients has been shown to be a safe and effective method of reducing radiation dose to the heart. Currently, VIBH protocol compliance is monitored visually. In this work, we establish whether it is possible to gate the delivery of radiation from an Elekta linac using the Microsoft Kinect version 2 (Kinect v2) depth sensor to measure a patient breathing signal. This would allow contactless monitoring during VMAT treatment, as an alternative to equipment?assisted methods such as active breathing control (ABC). Breathing traces were acquired from six left breast radiotherapy patients during VIBH. We developed a gating interface to an Elekta linac, using the depth signal from a Kinect v2 to control radiation delivery to a programmable motion platform following patient breathing patterns. Radiation dose to a moving phantom with gating was verified using point dose measurements and a Delta4 verification phantom. 60 breathing traces were obtained with an acquisition success rate of 100%. Point dose measurements for gated deliveries to a moving phantom agreed to within 0.5% of ungated delivery to a static phantom using both a conventional and VMAT treatment plan. Dose measurements with the verification phantom showed that there was a median dose difference of better than 0.5% and a mean (3% 3 mm) gamma index of 92.6% for gated deliveries when using static phantom data as a reference. It is possible to use a Kinect v2 device to monitor voluntary breath hold protocol compliance in a cohort of left breast radiotherapy patients. Furthermore, it is possible to use the signal from a Kinect v2 to gate an Elekta linac to deliver radiation only during the peak inhale VIBH phase.
PET image degradation imposed by patient respiratory motion is a well-established problem in
clinical oncology; strategies exist to study and correct this. Some attempt to minimise or arrest
patient motion through restraining hardware; their effectiveness is subject to the comfort and compliance.
Another practice is to gate PET data based on signals acquired from an external device.
This thesis presents several contributions to the field of respiratory motion correction research in
PET imaging. First and foremost, this thesis presents a framework which allows a researcher to
process list mode data from a Siemens Biograph mCT scanner and reconstruct sinograms of which
in the open source image reconstruction package STIR. Secondly, it demonstrates the viability of a
depth camera for respiratory monitoring and gating in a clinical environment. It was demonstrated
that it was an effective device to capture anterior surface motion. Similarly, it has been shown
that it can be used to perform respiratory gating. The third contribution is the design, implementation
and validation of a novel respiring phantom. It has individually programmable degrees of
freedom and was able to reproduce realistic respiration motion derived from real volunteers. The
final contribution is a new gating algorithm which optimises the number and width of gates based
on respiratory motion data and the distribution of radioactive counts. This new gating algorithm
iterates on amplitude based gating, where gates as positioned based on respiratory pose at a given
instant. The key improvement is that it considers the distribution of counts as a consequence of
the distribution of motion in a typical PET study. The results show that different studies can be
optimised with a unique number of gates based on the maximum extent of motion present and
can take into account shifts in baseline position due to patient perturbation.
Bio-medical imaging is a large umbrella term which covers a number of different imaging modalities used in healthcare today, spanning pre-clinical imaging, to diagnostic imaging and imaging to assist and plan patient treatment. This field of research is pivotal to driving advances in healthcare. This is underpinned by advances in new detector technologies which have the potential to reduce image acquisition time and dose, improving image quality and offer more accurate tools for diagnosis and treatment.
Large area CMOS Active Pixel Sensors (APSs) have the potential to deliver these advances in such demanding and continuously evolving field; large imaging area, together with low noise, low cost, fast readout, high dynamic range and potential for in-pixel intelligence have made this technology an ideal candidate to displace currently used imaging technologies in this field.
This thesis represents the first investigation into the capabilities of large area CMOS APSs to be used across a number of different imaging modalities in bio-medical science, spanning protein imaging to proton Computed Tomography (CT), using both ionising and non-ionising radiation sources. A novel characterisation of the detector performance has been carried out and set into context of commonly used detectors for bio-medical imaging. Considering the performance parameters assessed for this detector, in comparison with digital detectors commonly used in the clinical practise, this demonstrates how such large area sensor technology may be successfully employed in bio-medical imaging.
The novel large area CMOS APS, studied in this work, is proposed as a multi- modality imaging platform for use in pre-clinical science. For the first time direct ?contact print? imaging of radioactive and optical labeled biological samples on a large imaging area has been demonstrated, showing its potential application to a broad range of ionising and non-ionising imaging probes. The protein detection capability of this detector has been compared with both film emulsion and commercially available digital systems, demonstrating a higher resolution in protein detection than either film emulsion or a commonly used commercial CCD-based western blotting detection system. Also, when detection capabilities of this imaging system are compared with the state-of-the art devices for tissue autoradiography, this detector system exhibits a sensitivity comparable to that reported for its competitors, whilst offering the largest imaging area. Both these proof of concepts pave the way for large area CMOS APSs to be used as a multi- modality imaging platform in life science.
The radiation hardness of a novel large area CMOS APS, designed for medical applications and hardened-by-design, is presented. The radiation damage, produced in this sensor by X-ray and proton irradiation, has been studied as function of total ionising dose and displacement damage dose. The damage contributions from ionising and non-ionising energy deposition have been separated for the proton field and proved independent from proton energy providing a further verification of the Non Ionising Energy Loss (NIEL) scaling hypothesis. The lifetime of this detector for routine use in clinical practice has been evaluated as high as 4 years when used in a typical MegaVolt- age radiotherapy environment, demonstrating how such large area sensor technology may be successfully employed in X-ray and proton based imaging applications.
The feasibility of using CMOS APSs as energy-range detectors in proton CT has been demonstrated. Capability of single proton counting, together with potential of energy deposition measurements, have been demonstrated for CMOS APSs. Furthermore, experimental work, based on a simple stack of two CMOS sensors, as well as simulation work has been carried out to prove the capability of such a detection system for pro- ton tracking. Novel algorithms have been developed to perform proton tracking in a CMOS ene
It is estimated that over half of current adults within Great Britain under the age of 65 will be diagnosed with cancer at some point in their lifetime. Medical Imaging forms an essential part of cancer clinical protocols and is able to furnish morphological, metabolic and functional information. The imaging of molecular interactions of biological processes in vivo with Positron Emission Tomography (PET) is informative not only for disease detection but also therapeutic response. The qualitative and quantitative accuracy of imaging is thus vital in the extraction of meaningful and reproducible information from the images, allowing increased sensitivity and specificity in the diagnosis and precision of image guided treatment. Furthermore the utilization of complementary information obtained via Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) in integrated PET-CT and PET-MR devices offers the potential for the synergistic effects of hybrid imaging to provide increased detection and precision of diagnosis with reduced radiation dose in a fully comprehensive single imaging examination. With the increasing sophistication in imaging technology respiratory organ motion during imaging has demonstrated itself to be a major degrading factor of PET image resolution. A modest estimate of respiratory motion amplitude of 5mm, results in PET system resolution degrading from H 5mm to H8.5mm. This evidently has an impact on cancer lesion detectability. Therefore accurate and robust methods for respiratory motion correction are required for both clinical effectiveness and economic justification for purchasing state of the art hybrid PET scanners with high resolution capabilities. In addition the judicious use of imaging resources from hybrid imaging devices coupled with advanced image processing / acquisition protocols will allow optimization of data used for improving quantitative accuracy of PET images and those used for clinical interpretation. In essence it would prove impractical to use the MR scanner purely for monitoring respiratory motion.
Numerous methods exist to attempt to correct PET imaging for respiratory motion. As presented in this thesis many methods demonstrate themselves to be ineffective in the clinical setting where the patients breathing patterns appear irregular in comparison to the idealized situation of regular periodic motion. Advanced respiratory motion correction techniques utilize hybrid PET/CT, PET/MR scanners coupled with an external source of information which serves as a surrogate to build a static correspondence to the estimated internal respiratory motion. Static models however are unable to adapt to their external environment and do not consider time dependent changes in the state of a system. A further confounding factor in the development and assessment of motion correction schemes for medical imaging data is the inability to acquire volumetric data with high contrast and high spatial and temporal resolution which serves as a ground truth for quantifying model accuracy and confidence. This thesis addresses both problems by analysing respiratory motion correspondence modelling under a manifold learning and alignment paradigm which may be used to consolidate many of the respiratory motion estimation models that exist today. A Bayesian approach is adopted in this work to incorporate a-priori information into the model building stage for a more robust, flexible adaptive respiratory motion estimation / correction framework.
This thesis constructs and tests the first proposed adaptive motion model to correlate a surrogate signal with internal motion. This adaptive approach allows the relationship between external surrogate signal and internal motion to change dependent upon breathing pattern and system noise. The adaptive model was compared to a state-of the-art static model and allows more accurate motion estimates to be made when the patient is breathing with an irregular pattern. Testing perfo
Digital breast tomosynthesis (DBT) is currently under consideration for replacement of, or combined use with
2D-mammography in national breast screening programmes. To investigate the potential benefits that DBT can bring to screening, the threshold detectable lesion diameters were measured for different forms of DBT in comparison to 2D-mammography. The aim of this study was to compare the threshold detectable mass diameters obtained with narrow angle (15°/15 projections) and wide angle (50°/25 projections) DBT in comparison to 2Dmammography.
Simulated images of 60mm thick compressed breasts were produced with and without masses using a set of validated image modelling tools for 2D-mammography and DBT. Image processing and reconstruction were performed using commercial software. A series of 4-alternative forced choice (4AFC) experiments was conducted for signal detection with the masses as targets. The threshold detectable mass diameter was found for each imaging modality with a mean glandular dose of 2.5 mGy. The resulting values of the threshold diameter for 2D-mammography (10.2 ± 1.4 mm) were found to be larger (p
Respiratory motion degrades quantitative and qualitative
analysis of medical images. Estimation and hence correction
of motion commonly uses static correspondence models
between an external surrogate signal and internal motion. This work presents a patient specific respiratory motion model with
the ability to adapt in the presence of irregular motion via
a Kalman filter with Expectation Maximisation for parameter
estimation. The adaptive approach introduces generalizability
allowing the model to account for a broader variety of motion.
This may be required in the presence of irregular breathing and
with different sensors monitoring the external surrogate signal.
The motion model framework utilizing an adaptive Kalman filter
approach is tested on dynamic MRI data of nine volunteers and
compared to a state-of-the-art static total least squares approach.
Results demonstrate the framework is capable of reducing motion
to the order of
effective in the presence of irregular motion, assessed using the
F test for model comparison. Utilizing the total sum of squares
of estimated vector field error from the calculated ground truth,
we observe approximately a fifty percent reduction in root mean
square error and thirty percent reduction in standard deviation
utilizing the Kalman model (EKF) in comparison to a static
About 1.7 million new cases of breast cancer were estimated by the World Health Organization (WHO) in 2012, accounting for 23 percent of all female cancers. In the UK 33 percent of women aged 50 and above were diagnosed in the same year, thus positioning the UK as the 6th highest in breast cancer amongst the European countries. The national Screening programme in the UK has been focused on the procedure of early detection and to improve prognosis by timely intervention to extend the life span of patients. To this end, the National Health Service Breast Screening Programme (NHSBSP) employs 2-D planar mammography because it is considered to be the gold standard technique for early breast cancer detection worldwide. Breast tomosynthesis has shown great promise as an alternative method for removing the intrinsic overlying clutter seen in conventional 2D imaging. However, preliminary work in breast CT has provided a number of compelling aspects that motivates the work featured in this thesis. These advantages include removal of the need to mechanically compress the breast which is a source of screening non-attendances, and that it provides unique cross sectional images that removes almost all the overlying clutter seen in 2D. This renders lesions more visible and hence aids in early detection of malignancy. However work in Breast CT to date has been focused on using scaled down versions of standard clinical CT systems. By contrast, this thesis proposes using a photon counting approach. The work of this thesis focuses on investigating photoncounting detector technology and comparing it to conventional CT in terms of contrast visualization. Results presented from simulation work developed in this thesis has demonstrated the ability of photoncounting detector technology to utilize data in polychromatic beam where contrast are seen to decrease with increasing photon energy and compared to the conventional CT approach which is the standard clinical CT system.
Virtual clinical trials (VCT) are a powerful imaging tool that can be used to investigate digital breast tomosynthesis (DBT) technology. In this work, a fast and simple method is proposed to estimate the two?dimensional distribution of scattered radiation which is needed when simulating DBT geometries in VCTs.
Monte Carlo simulations are used to precalculate scatter?to?primary ratio (SPR) for a range of low?resolution homogeneous phantoms. The resulting values can be used to estimate the two?dimensional (2D) distribution of scattered radiation arising from inhomogeneous anthropomorphic phantoms used in VCTs. The method has been validated by comparing the values of the scatter thus obtained against the results of direct Monte Carlo simulation for three different types of inhomogeneous anthropomorphic phantoms.
Differences between the proposed scatter field estimation method and the ground truth data for the OPTIMAM phantom had an average modulus and standard deviation of over the projected breast area of 2.4 ± 0.9% (minimum ?17.0%, maximum 27.7%). The corresponding values for the University of Pennsylvania and Duke University breast phantoms were 1.8 ± 0.1% (minimum ?8.7%, maximum 8.0%) and 5.1 ± 0.1% (minimum ?16.2%, maximum 7.4%), respectively.
The proposed method, which has been validated using three of the most common breast models, is a useful tool for accurately estimating scattered radiation in VCT schemes used to study current designs of DBT system.
There are many applications for which sparse, or partial sampling of
dynamic image data can be used for articulating or estimating motion within the
medical imaging area. In this new work, we propose a generalized framework for
dense motion propagation from sparse samples which represents an example of transfer
learning and manifold alignment, allowing the transfer of knowledge across imaging
sources of different domains which exhibit different features. Many such examples
exist in medical imaging, from mapping 2D ultrasound or fluoroscopy to 3D or 4D
data or monitoring dynamic dose delivery from partial imaging data in radiotherapy.
To illustrate this approach we animate, or articulate, a high resolution static MR
image with 4D free breathing respiratory motion derived from low resolution sparse
planar samples of motion. In this work we demonstrate that sparse sampling of
dynamic MRI may be used as a viable approach to successfully build models of freebreathing respiratory motion by constrained articulation. Such models demonstrate
high contrast, and high temporal and spatial resolution that have so far been hitherto
unavailable with conventional imaging methods. The articulation is based on using
a propagation model, in the eigen domain, to estimate complete 4D motion vector
fields from sparsely sampled free-breathing dynamic MRI data. We demonstrate that
this approach can provide equivalent motion vector fields compared to fully sampled
4D dynamic data, whilst preserving the corresponding high resolution / high contrast
inherent in the original static volume. Validation is performed on three 4D MRI
datasets using 8 extracted slices from a fast 4D acquisition (0.7sec per volume). The
estimated deformation fields from sparse sampling are compared to the fully sampled
equivalents, resulting in an rms error of the order of 2mm across the entire image
volume. We also present exemplar 4D high contrast, high resolution articulated
volunteer datasets using this methodology. This approach facilitates greater freedom
in the acquisition of free breathing respiratory motion sequences which may be used to
inform motion modelling methods in a range of imaging modalities and demonstrates
that sparse sampling of free breathing data may be used within a manifold alignment
and transfer learning paradigm to estimate fully sampled motion. The method may also
be applied to other examples of sparse sampling to produce dense motion propagation.
Background: Chiari-like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae potentially resulting in pain and secondary syringomyelia (SM). CM associated pain can be challenging to diagnose . We propose a machine learning approach to characterize morphological changes in dogs that may/may not be apparent to human observers. This data driven approach can remove potential bias (or blindness) that may be produced by a hypothesis driven expert observer approach.
Hypothesis/Objectives: Using a novel machine learning approach to understand neuromorphological change and to identify image-based biomarkers in dogs with CM associated pain (CM-P) and symptomatic SM (SM-S), with the aim of deepening the understanding on these disorders.
Animals: 32 client owned Cavalier King Charles Spaniels (CKCS) (11 controls, 10 CM-P, 11 SM-S)
Methods: Retrospective study using T2W midsagittal DICOM anonymized images which were mapped to a images of a average clinically normal CKCS reference using Demons image registration. Key deformation features were automatically selected from the resulting deformation maps. A kernelized Support Vector Machine was used for classifying characteristic localized changes in morphology.
Results: Candidate biomarkers were identified with receiver operating characteristic (ROC) curves with area under the curve (AUC) of 0.78 (sensitivity = 82%; specificity = 69%) for the CM-P biomarkers collectively, and an AUC of 0.82 (sensitivity = 93%; specificity = 67%) for the SM biomarkers collectively.
Conclusions and clinical importance:
Machine learning techniques can assist CM/SM diagnosis and understand abnormal morphology location with the potential to be applied to a variety of breeds and conformational diseases.