Michael Brady

University of Oxford | OX · Department of Oncology

Background Long COVID is associated with multiple symptoms and impairment in multiple organs. Cross-sectional studies have reported cardiac impairment to varying degrees by varying methodologies. Using cardiac MR (CMR), we investigated a 12-month trajectory of abnormalities in Long COVID. Objectives To investigate cardiac abnormalities 1-year post...

Magnitude-based PDFF (Proton Density Fat Fraction) and R2∗ mapping with resolved water-fat ambiguity is extended to calculate field inhomogeneity (field map) using the phase images. The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2∗ from magnitude fitting may be updated using the estimated fi...

Background Optimisation of the future liver remnant (FLR) is crucial to the safety of extended liver resections. This study aimed to assess volume and quality of the FLR pre- and post-dual vein embolisation (DVE) by MRI, in patients with insufficient FLR, needing major hepatectomy. Methods Of 81 patients recruited in a clinical trial (Precision1:N...

Class imbalance in various forms is a common challenge in machine learning (ML) applied to medical imaging. One of these forms is the presence of low probability, but unsurprising, tissue abnormalities as a result of e.g. implants and surgery. Assessments from automated methods can be impeded if the ML system cannot address these abnormalities. A c...

PURPOSE: To extend magnitude-based PDFF (Proton Density Fat Fraction) and R2* mapping with resolved water-fat ambiguity to calculate a B0 field inhomogeneity map (field map) when phase images are accessible and reliable. THEORY: The estimation is formulated in matrix form, resolving the field map in a least-squares sense. PDFF and R2* from magnitud...

The UK Biobank imaging sub-study enables large-scale measurement of pancreas volume, an important biomarker in metabolic disease, including diabetes. Previous methods utilised a pancreas-specific (PS) 3D MRI UK Biobank acquisition to automatically measure pancreas volume. This may lead to a clinically significant underestimation of volume, due to p...

Multiparametric MRI of the pancreas can potentially benefit from the fusion of multiple acquisitions. However, its small, irregular structure often results in poor organ alignment between acquisitions, potentially leading to inaccurate quantification. Recent studies using UK Biobank data have proposed using pancreas segmentation from a 3D volumetri...

Prospective cardiac gating during MRI is hampered by electromagnetic induction from the rapidly switched imaging gradients into the ECG detection circuit. This is particularly challenging in small animal MRI, as higher heart rates combined with a smaller myocardial mass render routine ECG detection challenging. We have developed an open-hardware sy...

The risk of poor post-operative outcome and the benefits of surgical resection as a curative therapy require careful assessment by the clinical care team for patients with primary and secondary liver cancer. Advances in surgical techniques have improved patient outcomes but identifying which individual patients are at greatest risk of poor post-ope...

Liver cancer diagnosis and treatment response assessment typically rely on the inspection of multi-phase contrast-enhanced computed tomography (CT) or magnetic resonance (MR) images. To date, various methods were proposed to automatically segment liver lesions in single time-step CT; but limited research addressed image analysis of multiple contras...

Quantitative imaging biomarkers derived from magnetic resonance imaging of the pancreas could reveal changes in pancreas organ volume and shape manifest in chronic disease. Recent developments in machine learning facilitate pancreas segmentation and volume extraction. Machine learning methods could also help in designing a data-driven approach to p...

Magnetic resonance cholangiopancreatography (MRCP), an MRI-based technique for imaging the bile and pancreatic ducts, plays a vital role in the investigation of pancreatobiliary diseases. In current clinical practice, MRCP image interpretation remains primarily qualitative, though there is growing interest in using quantitative biomarkers, computed...

Purpose To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions. Materials and Methods...

Segmentation of medical images is typically one of the first and most critical steps in medical image analysis. Manual segmentation of volumetric images is labour-intensive and prone to error. Automated segmentation of images mitigates such issues. Here, we compare the more conventional registration-based multi-atlas segmentation technique with rec...

Recent developments in laser scanning microscopy have greatly extended its applicability in cancer imaging beyond the visualisation of complex biology, and opened up the possibility of quantitative analysis of inherently dynamic biological processes. However, the physics of image acquisition intrinsically means that image quality is subject to a tr...

Purpose To develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐base...

Purpose Several studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of t...

Background: Accurate assessment of liver health prior to undertaking resectional liver surgery or chemoembolisation for primary and secondary cancers is essential for patient safety and optimal outcomes. LiverMultiScan™, an MRI-based technology, non-invasively quantifies hepatic fibroinflammatory disease, steatosis and iron content. We hypothesise...

Paediatric liver disease is a growing problem, which would benefit from non-invasive techniques for early detection and treatment monitoring. Multiparametric quantitative MRI has shown promise for measuring liver steatosis, inflammation and fibrosis in adults, but is likely to need modification for children. The Kids4LIFe project (NCT03198104) aims...

Differentiating between cysts and other liver lesions seen on magnetic resonance images is an important diagnostic problem. Quantitative T1 mapping enables characterisation of liver tissue in vivo, by providing an estimate of the extracellular water content, and can be used in analysis of liquid-filled cysts. This paper presents an image processing...

Liver disease affects millions of people worldwide and auto-immune disease in particular has unmet needs for improvement of non-invasive methods for risk-stratification. Especially in cases where clinical markers are inconclusive. In this study we develop novel imaging features for quantitative MRI and show that these features improve the different...

Purpose: Cardiac and respiratory motion derived image artefacts are reduced when data are acquired with cardiac and respiratory synchronisation. Where steady state imaging techniques are required in small animals, synchronisation is most commonly performed using retrospective gating techniques but these invoke an inherent time penalty. This paper...

Deformable image registration, a key component of motion correction in medical imaging, needs to be efficient and provides plausible spatial transformations that reliably approximate biological aspects of complex human organ motion. Standard approaches, such as Demons registration, mostly use Gaussian regularization for organ motion, which, though...

Tumor heterogeneity can be assessed quantitatively by analyzing dynamic contrast-enhanced imaging modalities potentially leading to improvement in the diagnosis and treatment of cancer, for example of the lung. However, the acquisition of standard lung sequences is often compromised by irregular breathing motion artefacts, resulting in unsystematic...

Computed Tomography (CT) of the lungs play a key role in clinical investigation of thoracic malignancies, as well as having the potential to increase our knowledge about pulmonary diseases including cancer. It enables longitudinal trials to monitor lung disease progression, and to inform assessment of lung damage resulting from radiation therapy. W...

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods, however, particularly in the study of tumors i...

Deformable image registration aims to deliver a plausible spatial transformation between two or more images by solving a highly dimensional, ill-posed optimization problem. Covering the complexity of physiological motion has so far been limited to either generic physical models or local motion regularization models. This paper presents an alternati...

Liver disease, especially Non-Alcoholic Fatty Liver Disease has reached high levels, and there is a need for non-invasive tests based on quantitative MRI to replace biopsy in order to better assess liver health. An automated quantitative liver segmentation approach is required to automate these tests and in this work we propose a fully convolutiona...

Tumors exhibit chaotic and leaky vasculature, which leads to variations in perfusion, and regions of edema, hypoxia and necrosis. We develop a method to extract perfusion-supervoxels, regions of locally similar perfusion, and use these regions with k-means clustering to define tumor subregions that are robust to noise and outliers. This method offe...

Objective: The aim of this study is to assess the performance of a computer-aided semi-automated algorithm we have adapted for the purpose of segmenting malignant pleural mesothelioma (MPM) on CT. Methods: Forty-five CT scans were collected from 15 patients (M:F [Formula: see text] 10:5, mean age 62.8 years) in a multi-centre clinical drug trial...

We present a novel, high-resolution magnetic resonance technique, fine structure analysis (FSA) for the quantification and analysis of amorphous and quasi-amorphous biological structures. The one-dimensional technique is introduced mathematically and then applied to one simulated phantom, two physical phantoms and a set of ex vivo biological sample...

Cancer is one of the world's major healthcare challenges and, as such, an important application of medical image analysis. After a brief introduction to cancer, we summarise some of the the major developments in oncological image analysis over the past 20 years, but concentrating those in the authors’ laboratories, and then outline opportunities an...

Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or dise...

Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics – particularly in routine clinical practice. A framework is developed to automate D...

We propose a method for local, region-based matching of planar shapes, especially as those shapes that change over time. This is a problem fundamental to medical imaging, specifically the comparison over time of mammograms. The method is based on the non-emergence and non-enhancement of maxima, as well as the causality principle of integral invaria...

Discrete optimisation strategies have a number of advantages over their continuous counterparts for deformable registration of medical images. For example: it is not necessary to compute derivatives of the similarity term; dense sampling of the search space reduces the risk of becoming trapped in local optima; and (in principle) an optimum can be f...

Disclosed is a method of analyzing tissue from an image comprising providing an electronic image of tissue (100, 400, 450, 600, 800, 1100), determining a reference value from the image (1070, 1170, 1270), establishing an hint representation (500,700) of the image, and using the hint representation in analysis of the tissue to quantify the breast an...

Computer Aided Diagnostic (CAD) systems are already of proven value in healthcare, especially for surgical planning, nevertheless much remains to be done. Gliomas are the most common brain tumours (70%) in adults, with a survival time of just 2-3 months if detected at WHO grades III or higher. Such tumours are extremely variable, necessitating mult...

A signal processing method that includes inputting sample values of a signal and considering the signal to have a plurality of portions. For each portion, a predetermined function is fitted to the sample values of that portion of the signal by calculating values of coefficients for that predetermined function. At least one statistical information f...

The utility of HR-CT to study longitudinal changes in bone microarchitecture is limited by subject radiation exposure. Although MR is not subject to this limitation, it is limited both by patient movement that occurs during prolonged scanning at distal sites, and by the signal-to-noise ratio that is achievable for high-resolution images in a reason...

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to col...

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed t...

Minimising the mean glandular dose (MGD) received by the patient whilst maximising image contrast during mammographic imaging is of paramount importance due to the widespread use of the modality for screening, where subjects are for the most part healthy. The advent of digital mammography brought about a general reduction in MGD, however the introd...

Segmentation is typically the first step in computer-aided-detection (CADe). The second step is false positive reduction which usually involves computing a large number of features with thresholds set by training over excessive data set. The number of false positives can, in principle, be reduced by extensive noise removal and other forms of image...

Purpose: To consider, on a patient specific basis according to volumetric breast density (VBD) the mean glandular dose (MGD) imparted by mammography and tomosynthesis so as to determine which women might benefit from tomosynthesis in consideration with the clinical benefits. Methods: For a set of 23 Hologic combo-mode images, MGD was calculated fo...

Matching occluded and noisy shapes is a problem frequently encountered in medical image analysis and more generally in computer vision. To keep track of changes inside the breast, for example, it is important for a computer aided detection system to establish correspondences between regions of interest. Shape transformations, computed both with int...

The goal of this work is to reliably and accurately localize anatomical landmarks in 3-D computed tomography scans, particularly for the deformable registration of whole-body scans, which show huge variation in posture, and the spatial distribution of anatomical features. Parts-based graphical models (GM) have shown attractive properties for this t...

We present an update of our investigations into the potential role of quantitative measures of breast density for characterising breast changes, and, in particular, changes due to Hormone Replacement Therapy (HRT). It has been established that long-term use of HRT can increase the risk of breast cancer, a fact that enforces the belief that objectiv...

Our aim is to propose a new approach to breast pattern classification that will aid in the development of an automated mammographic density analysis procedure. Breast patterns broadly classify the mammographic density and density distribution of each mammogram in order to provide a framework for assessing the risk of breast cancer according to dens...

In this paper, we explore the idea of quantifying local breast-tissue density changes. Breast tissue density has been correlated to breast cancer incidence in numerous studies which have shown a statistical relationship between glandular density and the occurrence of cancer. In particular, postmenopausal women who take HRT run an increased risk of...

The concept of multi-modal data fusion generally involves some facet of registration. However to truly distinguish the notion of "fusion" from conventional "registration" a n und erstanding o f representation and visualisation must be incorporated into the solution. This is a particularly critical issue in the case of breast data fusion as the repr...

The goal of this work is to accurately and reliably localize anatomical landmarks in 3D Computed Tomography (CT) scans of the upper bodies of cancer patients even in the presence of pathologies and imaging artifacts that may markedly change the appearances of anatomical structures. We propose a method based on dense matching of parts-based graphica...

Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Ca...

Image-guided interventions often rely on deformable multimodal registration to align pre-treatment and intra-operative scans. There are a number of requirements for automated image registration for this task, such as a robust similarity metric for scans of different modalities with different noise distributions and contrast, an efficient optimisati...

A comprehensive framework for predicting response to therapy on the basis of heterogeneity in dceMRI parameter maps is presented. A motion-correction method for dceMRI sequences is extended to incorporate uncertainties in the pharmacokinetic parameter maps using a variational Bayes framework. Simple measures of heterogeneity (with and without uncer...

Medical images pose a major challenge for image analysis: often they have poor signal-to-noise, necessitating smoothing; yet such smoothing needs to preserve the boundaries of regions of interest and small features such as mammogram microcalcifications. We show how circular integral invariants (II) may be adapted for feature-preserving smoothing to...

Matching occluded and noisy shapes is a frequently encountered problem in vision and medical image analysis and more generally in computer vision. To keep track of changes inside breast, it is important for a computer aided diagnosis system (CAD) to establish correspondences between regions of interest. Shape transformations, computed both with int...

We develop a large deformations, Finite Elements biomechanical model of a stellate breast tumour, subject to prone to supine (MRI to US) breast deformations. Based on clinical findings, we introduce a volume of increased mammographic density/stiffness around a spiculated tumour, as well as a range of reported mechanical properties, both linear elas...

Deformable medical image registration requires the optimisation of a function with a large number of degrees of freedom. Commonly-used approaches to reduce the computational complexity, such as uniform B-splines and Gaussian image pyramids, introduce translation-invariant homogeneous smoothing, and may lead to less accurate registration in particul...

Deformable image registration is an important tool in medical image analysis. In the case of lung CT registration there are three major challenges: large motion of small features, sliding motions between organs, and changing image contrast due to compression. Recently, MRF-based discrete optimisation strategies have been proposed to overcome proble...

Suppression of neo-angiogenesis is a clinically used anti-tumor strategy with new targets such as angiopoietin-2 (Ang2) being proposed. However, the functions of Ang2 in vascular remodeling, inflammation and tumor growth are not consistent. We examined effect of depletion of host Ang2 on liver colony formation using Ang2 deficient (Ang2(-/-) ) mice...

This paper evaluates the performances of the OWL 2 reasoners HermiT, FaCT++ and Pellet in the context of an ontological clinical decision support system in lung cancer care. In the first set of experiments, we compare how the classification and realisation times of the LUCADA and LUCADA-SNOMED CT ontologies vary as we expand their TBoxes with addit...

PURPOSE To assess the capability of fineSA, a new magnetic resonance-based technique for quantifying biologic textures too fine to be resolved by conventional MR imaging. Our aim is to demonstrate its efficacy as a monitor for the cortical textural changes linked to ageing and AD. METHOD AND MATERIALS Using a small RF coil positioned directly agai...