Quality control (QC) of medical images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually at significant time and operator cost. We aim to automate the process by formulating a probabilistic network that estimates uncertainty through a heteroscedastic noise model, hence providing a proxy measure of task-specific image quality that is learnt directly from the data. By augmenting the training data with different types of simulated k-space artefacts, we propose a novel cascading CNN architecture based on a student-teacher framework to decouple sources of uncertainty related to different k-space augmentations in an entirely self-supervised manner. This enables us to predict separate uncertainty quantities for the different types of data degradation. While the uncertainty measures reflect the presence and severity of image artefacts, the network also provides the segmentation predictions given the quality of the data. We show models trained with simulated artefacts provide informative measures of uncertainty on real-world images and we validate our uncertainty predictions on problematic images identified by human-raters.
University College London（伦敦大学学院）；King’s College London（伦敦国王学院）
Quality control (QC) in medical image analysis is time-consuming and laborious, leading to increased interest in automated methods. However, what is deemed suitable quality for algorithmic processing may be different from human-perceived measures of visual quality. In this work, we pose MR image quality assessment from an image reconstruction perspective. We train Bayesian CNNs using a heteroscedastic uncertainty model to recover clean images from noisy data, providing measures of uncertainty over the predictions. This framework enables us to divide data corruption into learnable and non-learn…
Distributed learning has shown great potential in medical image analysis. It allows to use multi-center training data with privacy protection. However, data distributions in local centers can vary from each other due to different imaging vendors, and annotation protocols. Such variation degrades the performance of learning-based methods. To mitigate the influence, two groups of methods have been proposed for different aims, i.e., the global methods and the personalized methods. The former are aimed to improve the performance of a single global model for all test data from unseen centers (known…
Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually and subjectively, at significant time and operator cost. We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality. By augmenting training images with k-space artefacts, we propose a novel CNN architecture to decouple sources of uncertainty related to the task and different k-space artefa…
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；School of Computation, Information & Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；GE HealthCare（GE医疗）；Biomedical Imaging Group Rotterdam, Erasmus MC（伊拉斯姆斯大学医学中心鹿特丹生物医学成像组）；Visionlab, University of Antwerp（安特卫普大学视觉实验室）；School of Medicine & Health, Technical University of Munich（慕尼黑工业大学医学与健康学院）；Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右伊萨尔医院神经放射科）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
The University of Iowa（爱荷华大学）；University of Utah（犹他大学）
We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover dynamic magnetic resonance images from undersampled measurements. We introduce a generalized formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low rank structure. Using variable splitting, we decouple the complex optimization problem to simpler and we…
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）
Objective: Concurrent excitation and acquisition in MRI is a method to acquire MRI signal from tissues with very short transverse relaxation time. Since transmit power is many orders of magnitude larger than receive signal, a weak coupling dominates the MR signal during CEA. Thus, appropriate decoupling between transmit and receive coils is required. In this study, two controllable decoupling designs are investigated for achieving isolation between coils. Methods: A modified version of isolation concept used in the full-duplex radios in communication systems is applied to acquire MRI signal us…
Revisiting Global Token Mixing in Task-Dependent MRI Restoration: Insights from Minimal Gated CNN Baselines
Dept. of Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, USA（放射学与成像科学系，犹他大学，盐湖城，UT，USA）；Dept. of Electrical & Computer Engineering, University of Utah, Salt Lake City, UT, USA（电气与计算机工程系，犹他大学，盐湖城，UT，USA）；Dept. of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（计算机视觉系，莫扎德人工智能大学，阿布扎赫，EUA）；Dept. of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA（生物医学工程系，犹他大学，盐湖城，UT，USA）；Dept. of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA（电气与计算机工程系，华盛顿大学，西雅图，WA，USA）
BrainG3N: A Dual-Purpose Tokenizer for Controllable 3D Brain MRI Generation
Department of Biomedical Data Science, Stanford University School of Medicine（斯坦福大学医学院生物医学数据科学系）；Department of Mathematical Modelling, Statistics & Bioinformatics, Ghent University（根特大学数学建模、统计与生物信息学系）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）
Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China.（自动化与智能感知学院，上海交通大学，中国）；School of Computer Science, The University of Sydney, Sydney, Australia.（计算机科学学院，悉尼大学，澳大利亚）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.（放射科，仁济医院，医学院，上海交通大学，中国）；Biomedical Engineering Department, Tulane University, New Orleans, USA.（生物医学工程系， Tulane 大学，美国新奥尔良）
Indian Institute of Technology (IIT) Bombay（印度理工学院孟买分校）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什研究学院）；Monash University（莫纳什大学）
Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, s…
Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；University of Innsbruck（因斯布鲁克大学）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院）；Technical University of Berlin（柏林工业大学）；German Cancer Research Center（德国癌症研究中心）
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks and cascaded neural networks have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volu…
Reutlingen University（罗伊特林根大学）；Menoufia University（米努夫大学）；University of Ulm（乌尔姆大学）
Purpose: Gliomas are the most common and aggressive type of brain tumors due to their infiltrative nature and rapid progression. The process of distinguishing tumor boundaries from healthy cells is still a challenging task in the clinical routine. Fluid-Attenuated Inversion Recovery (FLAIR) MRI modality can provide the physician with information about tumor infiltration. Therefore, this paper proposes a new generic deep learning architecture; namely DeepSeg for fully automated detection and segmentation of the brain lesion using FLAIR MRI data. Methods: The developed DeepSeg is a modular decou…
UCL（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）；Foundation IRCCS Carlo Besta Neurological Institute（IRCCS卡洛·贝斯塔神经科学研究所基金会）；University of Nottingham（诺丁汉大学）；University of Oxford（牛津大学）；Microsoft Research Cambridge（微软剑桥研究院）
Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, little consideration has been given to uncertainty quantification over the output image. Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for $intrinsic$ uncertainty through a heteroscedastic noise model and for $parameter$ uncertainty through approximate Bayesian inference, and integrate the two to quanti…
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative app…
The Hamlyn Centre for Robotic Surgery, Imperial College London（帝国理工学院哈姆林机器人外科中心）；Department of Computing, Imperial College London（帝国理工学院计算机系）；Cardiovascular Biomedical Research Unit, Royal Brompton Hospital（皇家布朗普顿医院心血管生物医学研究中心）
Purpose: Advancements in MRI Tissue Phase Velocity Mapping (TPM) allow for the acquisition of higher quality velocity cardiac images providing better assessment of regional myocardial deformation for accurate disease diagnosis, pre-operative planning and post-operative patient surveillance. Translation of TPM velocities from the scanner's reference coordinate system to the regional cardiac coordinate system requires decoupling of translational motion and motion due to myocardial deformation. Despite existing techniques for respiratory motion compensation in TPM, there is still a remaining tran…
