Considering liver as the reference, that both fast diffusion (PF) and slow diffusion (Dslow) of the spleen are much underestimated is likely due to the MRI properties of the spleen such as the much longer T2 relaxation time. It is possible that longer T2 relaxation time partially mitigates the signal decay effect of various gradients on diffusion weighted image. This phenomenon will not be limited to the spleen. Most liver tumors have a longer T2 relaxation time than their native normal tissue and this is considered to be associated with oedema. On the other hand, most tumors are measured with lower MRI diffusion (despite being oedematous). The reason why malignant tumors have lower diffusion value [apparent diffusion coefficient (ADC) and Dslow] are poorly understood but has been proposed to be related to a combination of higher cellularity, tissue disorganization, and increased extracellular space tortuosity. These explanations may be true, but it is also possible to that many tumors have MRI properties similar to the spleen such as longer T2 (relative to the liver) and these MRI properties may also contribute to the lower MRI measured ADC and Dslow . In other words, if we could hypothetically plant a piece of spleen tissue in the liver, MRI would recognize this planted spleen tissue as being similar to a tumor and measure it to have lower diffusion than the liver.
Wall shear stress (WSS) has been suggested as a potential biomarker in various cardiovascular diseases and it can be estimated from phase-contrast Magnetic Resonance Imaging (PC-MRI) velocity measurements. We present a new parametric sequential method for MRI-based WSS quantification consisting of a geometry identification and a subsequent approximation of the velocity field. The new method was validated on well controlled, high resolution in vitro measurements of turbulent stationary flows and physiological pulsatile flows in phantoms. Basic applicability to in vivo was demonstrated using 2D …
Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation
Center for Advanced Imaging Innovation and Research (CAI2R)（先进成像创新与研究中心）；Department of Radiology（放射系）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Australian E-health Research Centre, CSIRO（澳大利亚电子健康研究中心，联邦科学与工业研究组织）；The University of Queensland（昆士兰大学）；Queensland Children's Hospital（昆士兰儿童医院）；Siemens Healthcare Pty Ltd（西门子医疗有限公司）；Siemens Healthineers AG（西门子医疗股份公司）
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinically feasible scan time necessitates significant undersampling, pushing the limits on compressed sensing and other low-dimensional techniques. During MRI scanning, one of the possible solutions is by using undersampling designs which can effectively improve the acquisition and achieve higher reconstruction accuracy. However, existing undersampling optimization methods are time-consuming and the limited performance prev…
Eindhoven University of Technology（埃因霍温理工大学）；King’s College London（伦敦国王学院）；Philips Healthcare（飞利浦医疗）
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perf…
Quantitative MRI is highly desirable in terms of intrinsic tissue parameters such as T1, T2 and proton density. This approach promises to minimize diagnostic variability and differentiate normal and pathological tissues by comparing tissue parameters to the normal ranges. Also, absolute quantification can help segment MRI tissue images with better accuracy compared to traditional qualitative segmentation methods. Currently, there are several methods proposed to quantify tissue parameters; however, all of them require excessive scan time and thus are difficult to be applied in clinical applicat…
This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose add…
Time-efficient combined morphologic and quantitative joint MRI based on clinical image contrasts -- An exploratory in-situ study of standardized cartilage defects
Purpose: Recent developments in hardware design enable the use of Fast Field-Cycling (FFC) techniques in MRI to exploit the different relaxation rates at very low field strength, achieving novel contrast. The method opens new avenues for in vivo characterisations of pathologies but at the expense of longer acquisition times. To mitigate this we propose a model-based reconstruction method that fully exploits the high information redundancy offered by FFC methods. Methods: The proposed model-based approach utilizes joint spatial information from all fields by means of a Frobenius - total general…
University of Bath（巴斯大学）；Imago7 foundation（Imago7基金会）；University of Edinburgh（爱丁堡大学）；GE Healthcare（GE医疗）；Technical University of Munich（慕尼黑工业大学）
We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training sa…
Medical College of Wisconsin（威斯康星医学院）；Marquette University（马凯特大学）
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic 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 plague QSM algorithms. To overcome these limitations, a robust deep-learning-based single-step QSM reconstruction approach is …
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Tianjin University（天津大学）
针对深度学习定量 MRI 的精度与不确定性问题，提出训练和推理均使用 Monte Carlo dropout 并平均多次预测的方法，在小数据集下显著提升 FA、MD 图精度，还能生成辅助诊断的置信图。
Microsoft Research（微软研究院）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；University of Minnesota（明尼苏达大学）；Stanford University（斯坦福大学）；Duke University（杜克大学）；New York University（纽约大学）
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging. The fastMRI dataset, unique in that it contains large volumes of raw MRI data, has enabled significant advances in accelerating MRI using deep learning-based reconstruction methods. While the impact of the fastMRI dataset on the field of medical imaging is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks an…
Left ventricular non-compaction (LVNC) is a rare cardiomyopathy (CMP) that should be considered as a possible diagnosis because of its potential complications which are heart failure, ventricular arrhythmias, and embolic events. For analysis cardiac functionality, extracting information from the Left ventricular (LV) is already a broad field of Medical Imaging. Different algorithms and strategies ranging that is semiautomated or automated has already been developed to get useful information from such a critical structure of heart. Trabeculae in the heart undergoes difference changes like solid…
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine（贝纳德与伊蕾娜·施瓦茨生物医学成像中心，放射学系，纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（数学科学学院，纽约大学）；Center for Advanced Imaging Innovation and Research (CAI 2 R), Department of Radiology, NYU Grossman School of Medicine（先进成像创新与研究中心（CAI 2 R），放射学系，纽约大学格罗斯曼医学院）
Automatic quantification of abdominal subcutaneous and visceral adipose tissue in children, through MRI study, using total intensity maps and Convolutional Neural Networks
Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learning
Institute of Machine Learning in Biomedical Imaging , Helmholtz Munich , Germany；School of Computation, Information \& Technology , Technical University of Munich , Germany；School of Medicine \& Health, Institute for Diagnostic；Interventional Neuroradiology , Technical University of Munich , Germany；School of Medicine \& Health, TUM-Neuroimaging Center , Technical University of Munich , Germany；Philips GmbH Market DACH , Germany；School of Medicine \& Health, Clinic of Neurology , Technical University of Munich , Germany；School of Biomedical Engineering \& Imaging Sciences , King’s College London , UK
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes i…
Purpose: To assess the technical feasibility of whole-brain diffusional variance decomposition (DIVIDE) based on q-space trajectory encoding (QTE) at clinical MRI systems with varying performance. DIVIDE is used to separate diffusional heterogeneity into components that arise due to isotropic and anisotropic tissue structures. Methods: We designed imaging protocols for DIVIDE using numerically optimized gradient waveforms for diffusion encoding. Imaging was performed at systems with magnetic field strengths between 1.5 and 7 T, and gradient amplitudes between 33 and 80 mT/m. Technical feasibil…
Purpose: In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or to improve Signal to Noise Ratio (SNR). In this paper the authors present a framework for fast MRI by exploiting a reference image (FASTMER). Methods: The proposed approach utilizes the possible similarity of the reference image that exists in many clinical MRI imaging scenarios. Such scenarios include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scan and similarity between different scans of the same patie…
Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
Chandra Family Department of Electrical and Computer Engineering（查克拉家族电气与计算机工程系）；Department of Imaging Physics（成像物理系）；Dell Medical School Department of Neurology（德莱尔医学院神经学系）；Dell Medical School Department of Neurosurgery（德莱尔医学院神经外科系）；Dell Medical School Department of Diagnostic Medicine（德莱尔医学院诊断医学系）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究院）
Objective: Accurate estimation of SAR is critical to safeguarding vulnerable patients who require an MRI procedure. The increased static field strength and RF duty cycle capabilities in modern MRI scanners mean that systems can easily exceed safe SAR levels for patients. Advisory protocols routinely used to establish quality assurance protocols are not required to advise on the testing of MRI SAR levels and is not routinely measured in annual medical physics quality assurance checks. This study aims to develop a head phantom and protocol that can independently verify global SAR for MRI clinica…
Purpose: Double diffusion encoding (DDE) MRI enables the estimation of microscopic diffusion anisotropy, yielding valuable information on tissue microstructure. A recent study proposed that the acquisition of rotationally invariant DDE metrics, typically obtained using a spherical "5-design", could be greatly simplified by assuming Gaussian diffusion, facilitating reduced acquisition times that are more compatible with clinical settings. Here, we aim to validate the new minimal acquisition scheme against the standard DDE 5-design, and to quantify the proposed method's noise robustness to facil…
Purpose: To investigate the feasibility of myelin water content quantification using fast dual-echo steady-state (DESS) scans and machine learning with kernels. Methods: We optimized combinations of steady-state (SS) scans for precisely estimating the fast-relaxing signal fraction ff of a two-compartment signal model, subject to a scan time constraint. We estimated ff from the optimized DESS acquisition using a recently developed method for rapid parameter estimation via regression with kernels (PERK). We compared DESS PERK ff estimates to conventional myelin water fraction (MWF) estimates fro…
Quantitative Magnetic Resonance Imaging (MRI) is based on a two-steps approach: estimation of the magnetic moments distribution inside the body, followed by a voxel-by-voxel quantification of the human tissue properties. This splitting simplifies the computations but poses several constraints on the measurement process, limiting its efficiency. Here, we perform quantitative MRI as a one step process; signal localization and parameter quantification are simultaneously obtained by the solution of a large scale nonlinear inversion problem based on first-principles. As a consequence, the constrain…
We develop a novel next generation light-weight highly flexible pediatric coil array, combine it with a high-density pediatric posterior array or conventional posterior phased array, and determine feasibility of pediatric clinical use. A highly flexible 16 element MRI receiver coil was constructed with low-profile noise controlling preamplifiers that minimized reactive and resistive coupling. Element decoupling was assessed in flat and highly flexed states. With IRB approval and informed consent and assent, 24 consecutive subjects undergoing torso or extremity MRI were prospectively recruited.…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest School of Medicine（维克森林大学医学院）
Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised of patients (n=1,399) referred for MRI treatment-planning and gamma knife radiosurgery over 21 years. Contrast-enhanced T1-weighted and T2-weighted Fluid-Attenuated Inversion Recovery brain MRI exams (n=1,582) were preprocessed and input to the proposed deep learning workflow fo…
Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: applications to a large-scale, clinically heterogeneous dataset
Standard coils can cause inhomogeneities due to the standing wave patterns at ultra high field MRI. This B1 inhomogeneity can be overcome using the travelling-wave MRI (twMRI) approach. The ma- jority of twMRI research has been done with clinical MRI scanners at 7T or above. More recently, this concept has been also used with animal MRI systems, using circular-cross section waveguides and dielectric materials for both type of scanners. We have demonstrated that twMRI can be used at 3 T with clinical systems and a parallel-plate waveguide. Here, we investigated the use of a parallel- plate wave…
We present a fast field-cycling NMR relaxometer with added magnetic resonance imaging capabilities. The instrument operates at a maximum proton Larmor frequency of 5 MHz for a sample volume of 35 mL. The magnetic field homogeneity across the sample is 1400 ppm. The main field is generated with a notch-coil electromagnet of own design, fed with a current whose stability is 220 ppm. We show that images of reasonable quality can still be produced under such conditions. The machine is being designed for concept testing of the involved instrumentation and specific contrast agents aimed for field-cy…
Fiber-Resolved Microstructure Quantification from Multi-Shell Diffusion MRI using Detection Transformers
AImotion Bavaria, Technische Hochschule Ingolstadt（AImotion Bavaria，英戈尔施塔特应用技术大学）；TUM School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；TUM School of Natural Sciences, Technical University of Munich（慕尼黑工业大学自然科学学院）
Transcranial MRI-guided focused ultrasound (TcMRgFUS) thermal ablation is a noninvasive functional neurosurgery technique. Previous reports have shown that bone marrow damage in the skull can occur at high acoustic energies. While this damage is asymptomatic, it would be desirable to avoid it. Here we examined whether acoustic and thermal simulations can predict where the thermal lesions in the skull might occur. Post-treatment imaging was obtained at 3-15 months after 40 clinical TcMRgFUS procedures, and skull lesions were observed after 16/40 treatments. The presence for lesions was predicte…
OBJECTIVE: 2D Ultrasound (US) imaging has been recently investigated as a more accessible alternative to 3D Magnetic Resonance Imaging (MRI) for the estimation of soft issue motion under external mechanical loading. In the context of pressure ulcer prevention, the aim of this pilot MRI study was to design an experiment to characterize the sacral soft tissue motion under a controlled mechanical loading. Such an experiment targeted the estimation of the discrepancy between tissue motion assessed using a 2D imaging modality (echography) versus tissue motion assessed using a (reference) 3D imaging…
Karolinska Institutet（卡罗林斯卡医学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；University of Brescia（布雷西亚大学）；Masaryk University（马萨里克大学）；St. Anne’s Hospital（圣安妮医院）；University G d’Annunzio of Chieti-Pescara（基耶蒂-佩斯卡拉加布里埃莱·邓南遮大学）
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical data sets---collected with different scanners, protocols and disease populations---and whether more heterogeneous training data improves…
