A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising
University of Cambridge（剑桥大学）；Universitat de Barcelona（巴塞罗那大学）；Haukeland University Hospital（霍克兰大学医院）；University of York（约克大学）；Manchester Metropolitan University（曼彻斯特城市大学）
Shuaiyu Yuan, Tristan Whitmarsh, Dimitri A Kessler, Otso Arponen, Mary A McLean, Gabrielle Baxter, Frank Riemer, Aneurin J Kennerley, William J Brackenbury, Fio… 展开作者
Shuaiyu Yuan, Tristan Whitmarsh, Dimitri A Kessler, Otso Arponen, Mary A McLean, Gabrielle Baxter, Frank Riemer, Aneurin J Kennerley, William J Brackenbury, Fiona J Gilbert, Joshua D Kaggie
New multinuclear MRI techniques, such as sodium MRI, generally suffer from low image quality due to an inherently low signal. Postprocessing methods, such as image denoising, have been developed for image enhancement. However, the assessment of these enhanced images is challenging especially considering when there is a lack of high resolution and high signal images as reference, such as in sodium MRI. No-reference Image Quality Assessment (NR-IQA) metrics are approaches to solve this problem. Existing learning-based NR-IQA metrics rely on labels derived from subjective human opinions or metrics like Signal-to-Noise Ratio (SNR), which are either time-consuming or lack accurate ground truths, resulting in unreliable assessment. We note that deep learning (DL) models have a unique characteristic in that they are specialized to a characteristic training set, meaning that deviations between the input testing data from the training data will reduce prediction accuracy. Therefore, we propose a novel DL-based NR-IQA metric, the Model Specialization Metric (MSM), which does not depend on ground-truth images or labels. MSM measures the difference between the input image and the model's prediction for evaluating the quality of the input image. Experiments conducted on both simulated distorted proton T1-weighted MR images and denoised sodium MR images demonstrate that MSM exhibits a superior evaluation performance on various simulated noises and distortions. MSM also has a substantial agreement with the expert evaluations, achieving an averaged Cohen's Kappa coefficient of 0.6528, outperforming the existing NR-IQA metrics.
Rician Denoising Diffusion Probabilistic Models For Sodium Breast MRI Enhancement
University of Cambridge（剑桥大学）；Universitat de Barcelona（巴塞罗那大学）；Haukeland University Hospital（豪克兰大学医院）；University of York（约克大学）；Manchester Metropolitan University（曼彻斯特城市大学）
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）；HKUST Shenzhen Research Institute（香港科技大学深圳研究院）
Politecnico di Milano（米兰理工大学）；University of Twente（特温特大学）；University of Bath（巴斯大学）；University of Cambridge（剑桥大学）
4D flow MRI is a non-invasive imaging method that can measure blood flow velocities over time. However, the velocity fields detected by this technique have limitations due to low resolution and measurement noise. Coordinate-based neural networks have been researched to improve accuracy, with SIRENs being suitable for super-resolution tasks. Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI, achieving denoising and super-resolution. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements…
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Siemens Healthcare GmbH（西门子医疗有限公司）；Fraunhofer Institute for Ceramic Technologies and Systems IKTS（弗劳恩霍夫陶瓷技术与系统研究所IKTS）；Ewha Womans University（梨花女子大学）
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured …
Fudan University（复旦大学）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
Small lesions in magnetic resonance imaging (MRI) images are crucial for clinical diagnosis of many kinds of diseases. However, the MRI quality can be easily degraded by various noise, which can greatly affect the accuracy of diagnosis of small lesion. Although some methods for denoising MR images have been proposed, task-specific denoising methods for improving the diagnosis confidence of small lesions are lacking. In this work, we propose a voxel-wise hybrid residual MLP-CNN model to denoise three-dimensional (3D) MR images with small lesions. We combine basic deep learning architecture, MLP…
MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Jiangsu Institute of Hematology（江苏血液学研究所）；The First Affiliated Hospital of Soochow University（苏州大学附属第一医院）；University Sorbonne Paris Nord（巴黎北索邦大学）；Henan University of Chinese Medicine（河南中医药大学）；German Cancer Research Center（德国癌症研究中心）；Athinoula A. Martinos Centre for Biomedical Imaging（阿西诺拉·A.马蒂诺斯生物医学成像中心）；Geneva University Hospital（日内瓦大学医院）；IDP Institute（IDP研究所）；University of Orleans（奥尔良大学）
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）
Negligible effect of brain MRI data preprocessing for tumor segmentation
Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；Skolkovo Institute of Science and Technology（斯科尔科沃科技学院）；National Medical Research Center for Neurosurgery（国家神经外科医学研究中心）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Chung-Ang University Hospital（中央大学医院）；Chung-Ang University College of Medicine（中央大学医学院）
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifact, denoising is largely studied both within the medical imaging community and beyond the community as a general subject. However, recent deep neural network-based approaches mostly rely on the minimum mean squared error (MMSE) estimates, which tend to produce a blurred output. Moreover, such models suffer when deployed in real-world sitautions: out-of-distribution data, and complex noise distributions that deviate from the usual parametric noise models. In…
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. Thus, there is great interest in denoising MRI scans, especially for the subtype of diffusion MRI scans that are severely SNR-limited. While most prior MRI denoising methods are supervised in nature, acquiring supervised training datasets for the multitude of anatomies, MRI scanners, and scan parameters proves impractical. Here, we propose Denoising…
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
St. Francis Xavier University（圣弗朗西斯泽维尔大学）；Martinos Center for Biomedical Imaging（马蒂诺生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Nova Scotia Health Authority（新斯科舍省卫生局）；King’s College London（伦敦国王学院）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
Temple University（天普大学）；University of Maryland School of Medicine（马里兰大学医学院）
Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify cerebral blood flow (CBF) but it still suffers from a low signal-to-noise-ratio (SNR). Using deep machine learning (DL), several groups have shown encouraging denoising results. Interestingly, the improvement was obtained when the deep neural network was trained using noise-contaminated surrogate reference because of the lack of golden standard high quality ASL CBF images. More strikingly, the output of these DL ASL networks (ASLDN) showed even higher SNR than the surrogate reference. This phenomenon indicates a…
We propose using a coordinate network decoder for the task of super-resolution in MRI. The continuous signal representation of coordinate networks enables this approach to be scale-agnostic, i.e. one can train over a continuous range of scales and subsequently query at arbitrary resolutions. Due to the difficulty of performing super-resolution on inherently noisy data, we analyze network behavior under multiple denoising strategies. Lastly we compare this method to a standard convolutional decoder using both quantitative metrics and a radiologist study implemented in Voxel, our newly developed…
Cycle-guided Denoising Diffusion Probability Model for 3D Cross-modality MRI Synthesis
Emory University（埃默里大学）；Winship Cancer Institute（温希普癌症研究所）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement
School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC（中国科学技术大学生命科学与医学部生物医学工程学院）；MIRACLE Center, Suzhou Institute for Advance Research, USTC（中国科学技术大学苏州研究院MIRACLE中心）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（中国科学技术大学精准与智能化学国家重点实验室）；Key Laboratory of Intelligent Information Processing of CAS, Institute of Computing Technology, CAS（中国科学院计算技术研究所中国科学院智能信息处理重点实验室）
Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学电气与计算机工程系）；Department of Medicine, University of Florida, Gainesville, FL, USA（佛罗里达大学医学系）；Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学生物医学工程系）；Research Computing, University of Florida, Gainesville, FL, USA（佛罗里达大学研究计算中心）；NVIDIA, Santa Clara, CA, USA（英伟达）
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled traini…
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for Joint MRI Reconstruction and Denoising in Low-Field MRI
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；NYU Tandon School of Engineering（纽约大学坦登工程学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Self-Supervised Joint Reconstruction and Denoising of T2-Weighted PROPELLER MRI of the Lungs at 0.55T
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）；New York University Tandon School of Engineering（纽约大学坦登工程学院）；Indiana University School of Medicine（印第安纳大学医学院）
Peking University（北京大学）；Academy for Advanced Interdisciplinary Studies（前沿交叉学科研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM. Different from previous works, MC-DDPM is defined in measurement domain (e.g. k-space in MRI reconstruction) and conditioned on under-sampling mask. We apply this method to accelerate MRI reconstruction and the experimental results show excellent performance, outperforming full supervision baseline and the state-of-the-art score-based reconstruction method. Due to its generative nature, MC-DDPM can also quantify the unce…
Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal. In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predicta…
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally invasive imaging technique which can be used for characterizing tumor biology and tumor response to radiotherapy. Pharmacokinetic (PK) estimation is widely used for DCE-MRI data analysis to extract quantitative parameters relating to microvascu- lature characteristics of the cancerous tissues. Unavoidable noise corruption during DCE-MRI data acquisition has a large effect on the accuracy of PK estimation. In this paper, we propose a general denoising paradigm called gather- noise attenuation and reduce (GNR) and a no…
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Digital Surgery LAB（数字外科实验室）；Champalimaud Foundation（尚帕利莫基金会）；Faculdade de Medicina, Universidade de Lisboa（里斯本大学医学院）；Instituto de Telecomunicações（电信研究所）
Robust multi-coil MRI reconstruction via self-supervised denoising
Stanford University（斯坦福大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Intel Corporation（英特尔公司）；MD Anderson Cancer Center（MD安德森癌症中心）；Dell Medical School Department of Diagnostic Medicine（德克萨斯大学戴维斯医学中心诊断医学系）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究院）
Inference Stage Denoising for Undersampled MRI Reconstruction
School of Engineering, The University of Edinburgh（爱丁堡大学工程学院）；Department of Electrical and Electronic Engineering and I-X, Imperial College London（帝国理工学院电气与电子工程系及I-X）
This paper proposes a novel method for automatic MRI denoising that exploits last advances in deep learning feature regression and self-similarity properties of the MR images. The proposed method is a two-stage approach. In the first stage, an overcomplete patch-based convolutional neural network blindly removes the noise without specific estimation of the local noise variance to produce a preliminary estimation of the noise-free image. The second stage uses this preliminary denoised image as a guide image within a rotationally invariant non-local means filter to robustly denoise the original …
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Graz University of Technology（格拉茨工业大学）；GE Healthcare（GE医疗）
Purpose: To investigate feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). Materials and Methods: Raw data from the prostate DWI scans were retrospectively gathered (between July 2018 and July 2019) from six single-vendor MRI scanners. 118 data sets were used for training and validation (age: 64.3 +- 8 years) and 37 - for testing (age: 65.1 +- 7.3 years). High b-value diffusion-weighted (hb-DW) data were reconstructed into nois…
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising
State University of New York at Buffalo（纽约州立大学布法罗分校）；Cleveland Clinic（克利夫兰诊所）；Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）
Purpose: To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages non-linear redundancy in the data to boost the SNR while preserving signal information. Methods: We exploit non-linear redundancy of the dMRI data by means of Kernel Principal Component Analysis (KPCA), a non-linear generalization of PCAto reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, better redundancy is achieved despite nonlinearities in the data thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with param…
Zhejiang University-University of Illinois Urbana-Champaign Institute（浙江大学-伊利诺伊大学厄巴纳-香槟分校联合学院）；Zhejiang University（浙江大学）
Reducing the radiation dose in computed tomography (CT) is important to mitigate radiation-induced risks. One option is to employ a well-trained model to compensate for incomplete information and map sparse-view measurements to the CT reconstruction. However, reconstruction from sparsely sampled measurements is insufficient to uniquely characterize an object in CT, and a learned prior model may be inadequate for unencountered cases. Medical modal translation from magnetic resonance imaging (MRI) to CT is an alternative but may introduce incorrect information into the synthesized CT images in a…
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿蒂努拉·A·马蒂诺斯生物医学成像中心）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）；Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology（麻省理工学院哈佛-麻省理工健康科学与技术部）
The noise in diffusion-weighted images (DWIs) decreases the accuracy and precision of diffusion tensor magnetic resonance imaging (DTI) derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the practical feasibility. We develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does n…
Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations
Department of Electrical and Computer Engineering, Seoul National University（电气电子工程系，首尔国立大学）；INMC & IPAI, Seoul National University（INMC与IPAI，首尔国立大学）；Division of Computer Engineering, Hankuk University of Foreign Studies（计算机工程系，韩文大学）；Department of Radiology, Johns Hopkins University School of Medicine（放射科，约翰霍普金斯大学医学院）
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）
