Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou, Gregory Lemberskiy, Eddy Solomon, Yvonne W. Lui, Daniel K. Sodickson, Els Fieremans, Dmitry S. Novikov, Florian Knoll
备注：
Pre-print prior to submission to Magnetic Resonance in Medicine
We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
Purpose: Point-of-care MRI requires operation outside of a faraday shielded room normally used to block image-degrading electromagnetic Interference (EMI). To address this, we introduce the EDITER method, an external sensor based dynamic EMI estimation and removal method to retrospectively remove time-varying external interference sources. Theory and Methods: The method acquires data from multiple EMI detectors (tuned receive coils and electrodes placed on the body) simultaneous with the primary MR coil during image data acquisition. We dynamically calculate impulse response functions that map…
The k-space data generated from magnetic resonance imaging (MRI) is only a finite sampling of underlying signals. Therefore, MRI images often suffer from low spatial resolution and Gibbs ringing artifacts. Previous studies tackled these two problems separately, where super resolution methods tend to enhance Gibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images. It is also a challenge that high resolution ground truth is hard to obtain in clinical MRI. In this paper, we propose an unsupervised learning framework for both MRI super resolution and Gibbs artifacts removal …
Facebook AI Research（Facebook人工智能研究）；Facebook New York（纽约Facebook）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Department of Radiology（放射科）
MRI images reconstructed from sub-sampled Cartesian data using deep learning techniques often show a characteristic banding (sometimes described as streaking), which is particularly strong in low signal-to-noise regions of the reconstructed image. In this work, we propose the use of an adversarial loss that penalizes banding structures without requiring any human annotation. Our technique greatly reduces the appearance of banding, without requiring any additional computation or post-processing at reconstruction time. We report the results of a blind comparison against a strong baseline by a gr…
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly based on supervised learning and therefore practical utility can be limited as data with paired artifact-free and artifact-corrupted images are typically insufficient or even non-existent. Here we show that unwanted image artifacts can be disentangled and removed from an image via an RAC neural network learned with unpaired data. This implies that our method does not require matching artifact-corrupted data to be eithe…
Recent advancements in the field of magnetic resonance imaging (MRI) have enabled large-scale collaboration among clinicians and researchers for neuroimaging tasks. However, researchers are often forced to use outdated and slow software to anonymize MRI images for publication. These programs specifically perform expensive mathematical operations over 3D images that rapidly slow down anonymization speed as an image's volume increases in size. In this paper, we introduce DeepDefacer, an application of deep learning to MRI anonymization that uses a streamlined 3D U-Net network to mask facial regi…
Purpose of this study was to evaluate the effect of a robust magnetic resonance reconstruction pipeline equipped with a deep convolutional neural network on the overall image quality, in terms of Gibbs artifact reduction, and SNR improvement. Sixteen (16) healthy volunteers enrolled in this study and were imaged at 3T. Representative images of each image series that were reconstructed through the pipeline that leverages a deep learning (DL) algorithm were retrospectively benchmarked against corresponding images reconstructed through a conventional pipeline. DL-reconstructed images showed signi…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Seoul National University College of Medicine（首尔大学医学院）
Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the quality of the scans are often unsatisfactory which can subsequently hamper the clinical utility. To overcome the limitations, here we propose a fully unsupervised quality enhancement scheme, which boosts the resolution and removes the motion artifact simultaneously. This process is done by first training the network using optimal transport driven cycleGAN with stochastic degradation block which learns to remove aliasing artifacts and enhance the resolution, then using the trained network in the test …
Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRI
Department of Electrical and Computer Engineering, University of Minnesota（电气与计算机工程系，明尼苏达大学）；Center for Magnetic Resonance Research, University of Minnesota（磁共振研究中心，明尼苏达大学）；Department of Imaging Physics, Delft University of Technology（成像物理系，代尔夫特理工大学）；HollandPTC, Delft, the Netherlands（HollandPTC，代尔夫特，荷兰）
KTH Royal Institute of Technology（KTH皇家理工学院）；Science for Life Laboratory（生命科学实验室）
Artifacts on magnetic resonance scans are a serious challenge for both radiologists and computer-aided diagnosis systems. Most commonly, artifacts are caused by motion of the patients, but can also arise from device-specific abnormalities such as noise patterns. Irrespective of the source, artifacts can not only render a scan useless, but can potentially induce misdiagnoses if left unnoticed. For instance, an artifact may masquerade as a tumor or other abnormality. Retrospective artifact correction (RAC) is concerned with removing artifacts after the scan has already been taken. In this work, …
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to estimate local tissue susceptibility, which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue. QSM requires addressing a challenging post-processing problem: filtering of image phase estimates and inversion of the phase to susceptibility relationship. A wide variety of quantification errors, robustness limitations, and artifacts constraints QSM clinical translation. To overcome these limitations, a robust deep-learning-based QSM reconstruction approach is proposed to perform backgrou…
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Xidian University（西安电子科技大学）；Guangdong Provincial People’s Hospital（广东省人民医院）；Southern Medical University（南方医科大学）；Xiangyang No. 1 People’s Hospital（襄阳市第一人民医院）；Hubei University of Medicine（湖北医药学院）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
The University of Hong Kong（香港大学）；Imperial College London（帝国理工学院）
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion artifacts, which leads to blurring and ghosting that significantly deteriorate the imaging quality. Conventional methods to reduce or eliminate these motion artifacts include breath holding, patient sedation, respiratory gating, and image post-processing, but these strategies inevitably involve extra scanning time and patient discomfort. In this paper, we propose a novel deep-le…
Department of Computer Science, University of Exeter, UK（埃克塞特大学计算机科学系，英国）；UCL Hawkes Institute, Department of Computer Science, University College London, UK（伦敦大学学院霍克斯研究所，计算机科学系，英国）
Hamburg University of Technology（汉堡工业大学）；Jung Diagnostics GmbH（荣格诊断有限公司）
The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UAD) methods have shown promising results for this task. These methods rely on training data sets that solely contain healthy samples. Compared to supervised approaches, this significantly reduces the need for an extensive amount of labeled training data. However, data labelling remains error-prone. We study how unhealthy samples within the training data affect anomaly detection performance for brain MRI-scans. For our…
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. Methods: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative $B0$-inhomogeneity-corrected $R_2^\ast$ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative …
University of Bath（巴斯大学）；Technical University of Munich（慕尼黑工业大学）；GE Healthcare（通用电气医疗集团）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neura…
Diffusion MRI preprocessing affects ADC estimation and automatic PI-RADS v2.1 classification in bi-parametric prostate MRI
Center for Image Sciences, University Medical Center Utrecht（乌得勒支大学医学中心影像科学中心）；Department of Radiotherapy, University Medical Center Utrecht（乌得勒支大学医学中心放射治疗科）；Cerebriu（Cerebriu公司）；Department of Diagnostic Radiology, Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院 Rigshospitalet 诊断放射科）；Department of Diagnostic Radiology, Copenhagen University Hospital Herlev and Gentofte（哥本哈根大学医院海尔鲁夫与根措夫特医院诊断放射科）
Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Royal Brompton Hospital（皇家布朗普顿医院）；Imperial College London（伦敦帝国理工学院）
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techniques have been proposed to reduce the acquisition time, compressed sensing in magnetic resonance imaging (CS-MRI) enables fast acquisition without compromising SNR and resolution. However, existing CS-MRI methods suffer from the challenge of aliasing artifacts. This challenge results in the noise-like textures and missing the fine details, thus leading to unsatisfactory reconstruction performance. To tackle this chal…
From Explanations to Architecture: Explainability-Driven CNN Refinement for Brain Tumor Classification in MRI
American International Brac University Faculty of Psychology University-Bangladesh（美国国际布拉大学心理学学院（孟加拉国））；Shinawatra University Department of Computer Science（辛纳瓦特大学计算机科学系）；American International North South University（美国国际北南大学）；American International University–Bangladesh Department of Computer Science（美国国际大学–孟加拉国计算机科学系）；Multimedia University Department of Computer Science（多媒体大学计算机科学系）
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
Xiamen University（厦门大学）；Fudan University（复旦大学）；Philips Healthcare（飞利浦医疗保健）；Siemens Healthineers Ltd.（西门子医疗有限公司）；United Imaging Research Institute of Intelligent Imaging（联合智能影像研究所）；Nanjing First Hospital（南京市第一医院）；Shandong Aoxin Medical Technology Company（山东奥新医疗科技有限公司）；The First Affiliated Hospital of Xiamen University（厦门大学附属第一医院）；Zhongshan Hospital Affiliated to Xiamen University（厦门大学附属中山医院）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Ruijin Hospital, Shanghai Jiaotong University School of Medicine（上海交通大学医学院附属瑞金医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；Xiamen University of Technology（厦门理工学院）；Imperial College London（帝国理工学院）
Athinoula A. Martinos Center for Biomedical Imaging（阿西努拉·A·马蒂诺斯生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术部）
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-ba…
Artifacts, blur and noise are the common distortions degrading MRI images during the acquisition process, and deep neural networks have been demonstrated to help in improving image quality. To well exploit global structural information and texture details, we propose a novel biomedical image enhancement network, named Feedback Graph Attention Convolutional Network (FB-GACN). As a key innovation, we consider the global structure of an image by building a graph network from image sub-regions that we consider to be node features, linking them non-locally according to their similarity. The propose…
Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Prior
University of Electronic Science and Technology of China（电子科技大学）；City University of Hong Kong（香港城市大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shenzhen Institute For Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳研究院）
Postoperative glioblastoma segmentation: Development of a fully automated pipeline using deep convolutional neural networks and comparison with currently available models
Río Hortega University Hospital（里奥·奥尔特加大学医院）；Universidad de Valladolid（巴利亚多利德大学）；Center for Biomedical Research in Network of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物工程、生物材料和纳米医学网络生物医学研究中心）；Albacete University Hospital（阿尔瓦塞特大学医院）；La Princesa University Hospital（拉普林塞萨大学医院）；UiT The Arctic University of Norway（挪威北极大学）；University Hospital of North Norway（北挪威大学医院）；St. Olavs University Hospital（圣奥拉夫大学医院）；Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；de Octubre University Hospital (i + 12)（10月12日大学医院）；Complutense University（马德里康普顿斯大学）；Instituto de Investigación Sanitaria, 12 de Octubre University Hospital (i + 12)（10月12日大学医院卫生研究所）；Institute for Research in Mathematics (IMUVA), University of Valladolid（巴利亚多利德大学数学研究所）
Quantitative susceptibility mapping (QSM) is a valuable MRI post-processing technique that quantifies the magnetic susceptibility of body tissue from phase data. However, the traditional QSM reconstruction pipeline involves multiple non-trivial steps, including phase unwrapping, background field removal, and dipole inversion. These intermediate steps not only increase the reconstruction time but amplify noise and errors. This study develops a large-stencil Laplacian preprocessed deep learning-based neural network for near instant quantitative field and susceptibility mapping (i.e., iQFM and iQ…
Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany（技术大学慕尼黑计算机辅助医疗程序）；Munich Center of Machine Learning, Munich, Germany（慕尼黑机器学习中心）；Department for Vascular and Endovascular Surgery, rechts der Isar University Hospital, Technical University of Munich, Munich, Germany（技术大学慕尼黑血管和内血管外科部门）
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…
