Motion artifacts are a common occurrence in the Magnetic Resonance Imaging (MRI) exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. Designing a computer-aided tool for automatic motion detection and elimination can improve the diagnosis, however, it needs a deep understanding of motion characteristics. Motion artifacts in MRI have a complex nature and it is directly related to the k-space sampling scheme. In this study we investigate the effect of three conventional k-space samplers, including Cartesian, Uniform Spiral and Radial on motion induced image distortion. In this regard, various synthetic motions with different trajectories of displacement and rotation are applied to T1 and T2-weighted MRI images, and a convolutional neural network is trained to show the difficulty of motion classification. The results show that the spiral k-space sampling method get less effect of motion artifact in image space as compared to radial k-space sampled images, and radial k-space sampled images are more robust than Cartesian ones. Cartesian samplers, on the other hand, are the best in terms of deep learning motion detection because they can better reflect motion.
Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can removes such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of architecture. Therefore, it is necessary to determine the artifact removal accuracy, depending on the selected architecture. In this study, we focus on endoscopic surgical instruments as artifacts, and determine and discuss the artifact remov…
Fundus images are very useful in identifying various ophthalmic disorders. However, due to the presence of artifacts, the visibility of the retina is severely affected. This may result in misdiagnosis of the disorder which may lead to more complicated problems. Since deep learning is a powerful tool to extract patterns from data without much human intervention, they can be applied to image-to-image translation problems. An attempt has been made in this paper to automatically rectify such artifacts present in the images of the fundus. We use a CycleGAN based model which consists of residual blo…
California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural netw…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches…
In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low field and/or portable MRI, we also …
In recent years, Independent Component Analysis (ICA) has successfully been applied to remove noise and artifacts in images obtained from Three-dimensional Polarized Light Imaging (3D-PLI) at the mesoscale (i.e., 64 $μ$m). Here, we present an automatic denoising procedure for gray matter regions that allows to apply the ICA also to microscopic images, with reasonable computational effort. Apart from an automatic segmentation of gray matter regions, we applied the denoising procedure to several 3D-PLI images from a rat and a vervet monkey brain section.
Ultrasound imaging has been improving, but continues to suffer from inherent artifacts that are challenging to model, such as attenuation, shadowing, diffraction, speckle, etc. These artifacts can potentially confuse image analysis algorithms unless an attempt is made to assess the certainty of individual pixel values. Our novel confidence algorithms analyze pixel values using a directed acyclic graph based on acoustic physical properties of ultrasound imaging. We demonstrate unique capabilities of our approach and compare it against previous confidence-measurement algorithms for shadow-detect…
Fordham University（福特汉姆大学）；QS Investors, LLC（QS投资有限责任公司）；Grossman Medical School（格罗斯曼医学院）
In-scanner motion degrades the quality of magnetic resonance imaging (MRI) thereby reducing its utility in the detection of clinically relevant abnormalities. We introduce a deep learning-based MRI artifact reduction model (DMAR) to localize and correct head motion artifacts in brain MRI scans. Our approach integrates the latest advances in object detection and noise reduction in Computer Vision. Specifically, DMAR employs a two-stage approach: in the first, degraded regions are detected using the Single Shot Multibox Detector (SSD), and in the second, the artifacts within the found regions ar…
Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging
School of Electrical Engineering and Automation, Wuhan University（武汉大学电气与自动化学院）；University Hospital Heidelberg（海德堡大学医院）；Vitalent Consulting（维塔伦特咨询公司）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）；Uppsala University（乌普萨拉大学）；School of Computer Science, University of Sydney（悉尼大学计算机学院）
Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T
Centre for Translational Cardiovascular Imaging, Institute of Cardiovascular Science, University College London（转化心血管成像中心，心血管科学研究所，伦敦大学学院）；Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital（临床生理学，临床科学系，伦德大学，斯德哥尔摩大学医院）；Department of Biomedical Engineering, Faculty of Engineering, Lund University（生物医学工程系，工程学院，伦德大学）；Research & Collaborations GBI, Siemens Healthcare Ltd（研究与合作GBI，西门子医疗有限公司）
针对实时同步多层 bSSFP 心脏磁共振成像中采集和重建时间长的问题，提出基于 3D U-Net 的深度伪影抑制方法，实现快速在线重建，显著缩短采集和重建时间，同时保持诊断图像质量。
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Chungnam National University Hospital（忠南大学医院）；Chungnam National University College of Medicine（忠南大学医学院）
Motion artifact reduction is one of the important research topics in MR imaging, as the motion artifact degrades image quality and makes diagnosis difficult. Recently, many deep learning approaches have been studied for motion artifact reduction. Unfortunately, most existing models are trained in a supervised manner, requiring paired motion-corrupted and motion-free images, or are based on a strict motion-corruption model, which limits their use for real-world situations. To address this issue, here we present an annealed score-based diffusion model for MRI motion artifact reduction. Specifica…
Photoacoustic imaging (PAI) can image high-resolution structures of clinical interest such as vascularity in cancerous tumor monitoring. When imaging human subjects, geometric restrictions force limited-view data retrieval causing imaging artifacts. Iterative physical model based approaches reduce artifacts but require prohibitively time consuming PDE solves. Machine learning (ML) has accelerated PAI by combining physical models and learned networks. However, the depth and overall power of ML methods is limited by memory intensive training. We propose using invertible neural networks (INNs) to…
Twinkling Artifact is a valuable tool in detecting dense objects such as kidney stones, calculi etc., especially when there is no acoustic shadowing and presence of hyperechogenic tissues obstructs visualization. This phenomenon is not completely understood. Different scientific groups have contradictory findings concerning its properties: some of them observed a decrease in Twinkling intensity at elevated pulse repetition frequencies (PRF), while others found Twinkling to be independent from PRF, etc. In this paper we hypothesize that this kind of contradictions can be partially resolved on a…
Shanghai Jiao Tong University（上海交通大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Calgary（卡尔加里大学）
Eliminating ghosting artifacts due to moving objects is a challenging problem in high dynamic range (HDR) imaging. In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR images. In the encoder, a context aggregation network and non-local attention block are adopted to optimize multi-scale features and capture both global and local dependencies of multiple low dynamic range (LDR) images. The decoder based on Swin Transformer is utilized to improve the reconstruction capability of the proposed model. Motivated by the p…
In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review
Radboudumc Netherlands；KU \& Rigshospitalet Denmark；Emory University USA；Stanford University USA；RUG Netherlands；Radboudumc \& Plain Medical Netherlands；Oxford University Hospitals UK；Copenhagen University Hospital \& RAIT Denmark；KU \& Rigshospitalet；Emory University；Stanford University；Radboudumc \& Plain Medical；Oxford University Hospitals；Copenhagen University Hospital \& RAIT；Institute for Clarity in Documentation P.O. Box 1212 Dublin Ohio USA 43017-6221；Inria Paris-Rocquencourt Rocquencourt France；Rajiv Gandhi University Rono-Hills Doimukh Arunachal Pradesh India；Tsinghua University 30 Shuangqing Rd Haidian Qu Beijing Shi China；Palmer Research Laboratories 8600 Datapoint Drive San Antonio Texas USA 78229；Institute for Clarity in Documentation；Inria Paris-Rocquencourt；Rajiv Gandhi University；Tsinghua University；Palmer Research Laboratories；Institute for Clarity in Documentation Dublin Ohio USA
Purpose: Using 4D magnetic particle imaging (MPI), intravascular optical coherence tomography (IVOCT) catheters are tracked in real time in order to compensate for image artifacts related to relative motion. Our approach demonstrates the feasibility for bimodal IVOCT and MPI in-vitro experiments. Material and Methods: During IVOCT imaging of a stenosis phantom the catheter is tracked using MPI. A 4D trajectory of the catheter tip is determined from the MPI data using center of mass sub-voxel strategies. A custom built IVOCT imaging adapter is used to perform different catheter motion profiles:…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；University of California, Los Angeles（加州大学洛杉矶分校）
Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a…
Distance transforms are a central tool in shape analysis, morphometry, and curve evolution problems. This work describes and investigates an artifact present in distance maps computed from sampled signals. Namely, sampling reflects through the distance transform causing quantization in the resulting distance map. Gradients of the quantized distance map show banding, affecting the quality of subsequence processing. Furthermore, this error is independent of the sampling period of the signal and cannot be removed by modifying the number of samples across an objects width. Where needed, distance m…
Dublin City University（都柏林城市大学）；University College Dublin（都柏林大学学院）
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those…
Tencent Jarvis Lab（腾讯天衍实验室）；Beijing Information Science and Technology University（北京信息科技大学）；Xi’an Jiaotong University（西安交通大学）；Peng Cheng Laboratory（鹏城实验室）；Macau University of Science and Technology（澳门科技大学）
During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific …
Kyoto University（京都大学）；e-Growth Co., Ltd.（e-Growth 有限公司）；Nara Medical University（奈良县立医科大学）；Rakuwakai Otowa Hospital（洛和会音羽医院）
The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT imag…
George Mason University（乔治梅森大学）；The MITRE Corporation（MITRE公司）
Photoacoustic tomography (PAT) is a nonionizing imaging modality capable of acquiring high contrast and resolution images of optical absorption at depths greater than traditional optical imaging techniques. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their view of the imaging target, which result in significant image reconstruction artifacts degrading image quality. Iterative reconstruction methods can be used to reduce artifacts but are computationally expensive. In this work, we propose a novel deep learning approach termed pi…
In this work, issues in phase retrieval in the coherent diffractive imaging (CDI) technique, from discussion on parameters for setting up a CDI experiment to evaluation of the goodness of the final reconstruction, are discussed. The distribution of objects under study by CDI often cannot be cross-validated by another imaging technique. It is therefore important to make sure that the developed CDI procedure delivers an artifact-free object reconstruction. Critical issues that can lead to artifacts are presented and recipes on how to avoid them are provided.
Jinan University（暨南大学）；Guangdong Communication Polytechnic（广东通信职业技术学院）
Fourier single-pixel imaging (FSI) is a branch of single-pixel imaging techniques. It uses Fourier basis patterns as structured patterns for spatial information acquisition in the Fourier domain. However, the spatial resolution of the image reconstructed by FSI mainly depends on the number of Fourier coefficients sampled. The reconstruction of a high-resolution image typically requires a number of Fourier coefficients to be sampled, and therefore takes a long data acquisition time. Here we propose a new sampling strategy for FSI. It allows FSI to reconstruct a clear and sharp image with a redu…
Single-pixel imaging is an indirect imaging technique which utilizes simplified optical hardware and advanced computational methods. It offers novel solutions for hyper-spectral imaging, polarimetric imaging, three-dimensional imaging, holographic imaging, optical encryption and imaging through scattering media. The main limitations for its use come from relatively high measurement and reconstruction times. In this paper we propose to reduce the required signal acquisition time by using a novel sampling scheme based on a random selection of Morlet wavelets convolved with white noise. While suc…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Massachusetts Institute of Technology（麻省理工学院）
Image corruption by motion artifacts is an ingrained problem in Magnetic Resonance Imaging (MRI). In this work, we propose a neural network-based regularization term to enhance Autofocusing, a classic optimization-based method to remove motion artifacts. The method takes the best of both worlds: the optimization-based routine iteratively executes the blind demotion and deep learning-based prior penalizes for unrealistic restorations and speeds up the convergence. We validate the method on three models of motion trajectories, using synthetic and real noisy data. The method proves resilient to n…
Digital two-dimensional (2D) spatial sampling devices (such as charge-coupled device) have been widely used in various imaging systems, especially in computational imaging systems. However, the undersampling of digital sampling devices is a problem that limits the resolution of the acquired images. In this study, we present a synthetic sampling imaging (SSI) concept to solve the undersampling problem. It combines the structured illumination system and conventional 2D image detection system to simultaneously sample the specimen from the illumination and the detection sides. Then, we synthesize …
School of Information and Electronics & Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology（北京理工大学信息与电子学院及多学科科学前沿研究研究院）
Using single-pixel detection, the end-to-end neural network that jointly optimizes both encoding and decoding enables high-precision imaging and high-level semantic sensing. However, for varied sampling rates, the large-scale network requires retraining that is laboursome and computation-consuming. In this letter, we report a weighted optimization technique for dynamic rate-adaptive single-pixel imaging and sensing, which only needs to train the network for one time that is available for any sampling rates. Specifically, we introduce a novel weighting scheme in the encoding process to characte…
We propose a method of reduction of experimental noise in single-pixel imaging by expressing the subsets of sampling patterns as linear combinations of vertices of a multidimensional regular simplex. This method may be also directly extended to complementary sampling. The modified sampling consists only of non-negative patterns. The measurement becomes theoretically independent of the ambient illumination, and in practice becomes more robust to the varying conditions of the experiment. We show how the optimal dimensionality of the simplex depends on the level of measurement noise. We present e…
Recently, deep learning-based compressive imaging (DCI) has surpassed the conventional compressive imaging in reconstruction quality and faster running time. While multi-scale has shown superior performance over single-scale, research in DCI has been limited to single-scale sampling. Despite training with single-scale images, DCI tends to favor low-frequency components similar to the conventional multi-scale sampling, especially at low subrate. From this perspective, it would be easier for the network to learn multi-scale features with a multi-scale sampling architecture. In this work, we prop…
Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction
Department of Electrical \& Computer Engineering, University of Minnesota, MN, USA Center for Magnetic Resonance Research, University of Minnesota, MN, USA
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 a…
We present a filtering procedure based on singular value decomposition to remove artifacts arising from sample motion during dynamic full field OCT acquisitions. The presented method succeeded in removing artifacts created by environmental noise from data acquired in a clinical setting, including in vivo data. Moreover, we report on a new method based on using the cumulative sum to compute dynamic images from raw signals, leading to a higher signal to noise ratio, and thus enabling dynamic imaging deeper in tissues.
Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation in sample exposure. Varies sparse sampling schemes have been studied and are ideally suited for such applications where the images can be reconstructed from a sparse set of measurements. Dynamic sparse sampling methods, particularly supervised learning based iterative sampling algorithms, have shown promising results for sampling pixel locations on the edges or boundaries during imaging. However, dynamic sampling for…
Based on photoacoustic effect, photoacoustic tomography is developing very fast in recent years, and becoming an important imaging tool for both preclinical and clinical studies. With enough ultrasound transducers placed around the biological tissue, PAT can provide both deep penetration and high image contrast by hybrid usage of light and sound. However, considering space and measurement environmental limitations, transducers are always placed in a limited-angle way, which means that the other side without transducer coverage suffers severe information loss. With conventional image reconstruc…
Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior…
To extract the maximum information about the object from a series of binary samples in ghost imaging applications, we propose and demonstrate a framework for optimizing the performance of ghost imaging with binary sampling to approach the results without binarization. The method is based on maximizing the information content of the signal arm detection, by formulating and solving the appropriate parameter estimation problem - finding the binarization threshold that would yield the reconstructed image with optimal Fisher information properties. Applying the 1-bit quantized Poisson statistics to…
ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts; A Foundation for Computerized ECG Image Digitization and Analysis
Minfrared spectroscopic imaging (MIRSI) is an emerging class of label-free, biochemically quantitative technologies targeting digital histopathology. Conventional histopathology relies on chemical stains that alter tissue color. This approach is qualitative, often making histopathologic examination subjective and difficult to quantify. MIRSI addresses these challenges through quantitative and repeatable imaging that leverages native molecular contrast. Fourier transform infrared (FTIR) imaging, the best-known MIRSI technology, has two challenges that have hindered its widespread adoption: data…
