bluuebunny/crossref_metadata_2025 huggingface dataset:
Hybrid Motion Correction: Prospective Thresholding and Retrospective Residual Correction
Authors: Glimberg Stefan , Laustsen Malte , Slipsager Jakob , Frost Robert , Ganz Melanie , van der Kouwe André , Gaass Thomas
Published: November 2024

Motivation: Prospective Motion Correction (PMC) demands real-time tracking and may introduce artifacts due to imperfect tracking due to noise. Goal(s): To introduce hybridMC, harnessing the strengths of thresholded PMC combined with retrospective motion correction to address motion artifacts comprehensively in neurological MRI. Approach: Apply a PMC update threshold to avoid incorrect motion correction due to noisy motion tracking. Correct for residual motion artifacts using model-based RMC. Results: The hybrid motion correction approach showed superior results when compared to PMC in the presence of noisy tracking, while maintaining good performance for non-noisy tracking. Impact: To enhance neurological MRI quality, we introduce a hybrid motion correction. This innovation effectively mitigates tracking noise and residual artifacts, offering superior results compared to PMC alone. It promises improved diagnostic accuracy.
Free-breathing self-gated 5D whole-heart CMR at 0.55T: left ventricle, right ventricle, and left atrium function analysis in a single sequence
Authors: Sieber Xavier , Binzel Katherine , Varghese Juliet , Liu Yingmin , Yerly Jérôme , van Heeswijk Ruud , Simonetti Orlando , Stuber Matthias
Published: November 2024

Motivation: CMR has not seen widespread adoption beyond large urban academic centers. The reasons for this limited uptake include the cost and time-intensive nature of CMR. 5D Free-Running CMR using self-navigation (5D CMR) implemented on a low-field clinical scanner may help bridge this gap. Goal(s): Investigate the feasibility of cardiac function measurements using 5D CMR on a 0.55T system. Approach: 5D CMR data were collected in 10 adult subjects and compared to results from reference 2D cines. Right- and left-ventricular ejection fraction and left atrial volume were ascertained. Results: 5D CMR allowed for time-efficient and concordant measurements when compared to the 2D reference method. Impact: 5D Free-Running whole-heart CMR without the need for ECG, breath-holding, navigators, or complex scan plane planning enables a highly simplified and time-efficient assessment of myocardial function on a 0.55T clinical system in under 8 min.
Deep learning enhanced DWI MUSE at 0.5T
Authors: Sundaresan Rajagopalan , Reddy Ashok Kumar , Jain Nitin , Agarwal Harsh , Rajamani Sajith , Venkatesan Ramesh
Published: November 2024

Motivation: Single-shot diffusion weighted imaging at 0.5T has low SNR, increased blurring and is limited by spatial resolution. Goal(s): We want to demonstrate image quality improvement in multi-shot DWI at 0.5T with MUSE DL reconstruction. Approach: Multi-shot DWI is reconstructed using MUSE algorithm followed by ARDL. Results: The qualitative DWI results show improved image quality and less blurring with MUSE ARDL reconstruction. Impact: Using multi-shot DWI and the MUSE ARDL recon increases spatial resolution and image quality at 0.5T and provides reliable imaging for low SNR DWI acquisitions.
Evaluation of Concomitant Presence of Iron Overload and Hepatic Steatosis using Monte Carlo Simulations at 0.75 T
Authors: Shrestha Utsav , Satapathy Sanjaya , Vanatta Jason , Tipirneni-Sajja Aaryani
Published: November 2024

Motivation: Multi-spectral fat water models fail to produce reliable fat fraction(FF) estimations for severe iron overload conditions at 1.5T and 3T. Low-field MRIs(<1T) may increase the accuracy in HIC and FF estimations at high iron overload by slowing signal decay but might suffer from lower signal-to-noise ratio(SNR). Goal(s): Assess the accuracy and robustness of quantifying R2* and FF at 0.75T across various SNR conditions. Approach: Realistic virtual liver models with concomitant presence of iron overload and hepatic steatosis were used to simulate MRI signals at 0.75T and 1.5T using Monte Carlo simulations. Results: 0.75T showed improved FF and R2* estimation compared to 1.5T. Impact: Low-field MRI can increase the accuracy and precision in simultaneous quantification of R2* and FF in the presence of mild-to-severe iron overload. With low-field MRI systems being less expensive and potentially increasing MRI accessibility, they can facilitate the reliable diagnosis.
Flexible, high spatial and temporal resolution absolute thermometry of MRI phantoms
Authors: Judd Wesley , Kim Seong-Eun , Dzikiy John , Parker Dennis , Odéen Henrik
Published: November 2024

Motivation: Quantitative Magnetic Resonance Imaging (qMRI) is becoming popular and is often evaluated in phantoms. However, MRI properties (T1, T2, diffusion, etc.) depend on temperature, so accurate absolute temperature measurements of the phantom is needed. Goal(s): Being able to measure absolute temperature throughout qMRI phantoms in a practical, efficient, and accurate way. Approach: A straight forward spectroscopic imaging approach is used to image ethylene glycol-filled vials and an automatic processing pipeline reconstructs temperatures. Results: Rapid (15 s/slice) and accurate (up to 0.5 °C) absolute temperature measurements are demonstrated at two different 3T scanners. Impact: With the described approach rapid and accurate MRI-based absolute phantom temperature measurements can be acquired in a practical way. This will help researchers developing quantitative measurements of temperature-sensitive parameters such as T1,T2, and diffusion, using phantoms for validation and benchmarking.
A Time-Resolved Forward Model for MR-ARFI: Towards Acoustic Dosimetry for FUS Neuromodulation
Authors: Zarcone Kristen , Luo Huiwen , Caskey Charles , Grissom William
Published: November 2024

Motivation: MR-ARFI is currently used to target focused ultrasound in the brain, but as a quantitative imaging method it has the potential to provide acoustic dosimetry if the measured tissue displacement can be related back to acoustic intensity or pressure. Goal(s): Develop a method to calculate MR-ARFI images based on high-intensity acoustic simulations of focused ultrasound and finite element modeling. Approach: We converted acoustic beam simulations to acoustic force, which was input to an FEM solver to calculate time-resolved tissue displacements, and subsequently MR-ARFI images. Results: The method enabled a lookup table calculation to recover acoustic intensity from MR-ARFI measurementsImpact: MR-ARFI is used to target focused ultrasound in the brain, but as a quantitative imaging method it could also provide dosimetry. We established the ability to relate simulated acoustic pressure fields to MR-ARFI images via finite element modeling.
Towards Automated Deep Learning based MR-only Radiation Therapy Planning in <5mins MR Scan Time
Authors: Wiesinger Florian , Kaushik Sandeep , Rusko Laszlo , Capala Marta , Lauwers Iris , Szabo Eszter , Cozzini Cristina , Petit Steven , Hernandez-Tamames Juan
Published: November 2024

Motivation: Deep Learning (DL) is an enabling technology for MR-only Radiation Therapy Planning in terms of 1) MR to synthetic CT conversion and 2) automated Organ-At-Risk (OAR) segmentation. Goal(s): To investigate the feasibility of DL based MR-only RT planning using accelerated MR protocols with <5mins scan time (not including tumor depiction). Approach: Quantitative evaluation of synthetic CTs in terms of mean absolute error (MAE) and OAR segmentations in terms of Likert Score analysis. Results: Preliminary results demonstrate the feasibility of DL-based MR-only RT in <5min scan time with only minor degradation of synthetic CT and OAR segmentation quality. Impact: Deep Learning is an enabling technology for MR-only Radiation Therapy Planning. Here we demonstrate its capabilities for 1) synthetic CT conversion and 2) Organ-At-Risk (OAR) segmentation in <5min MR scan time.
Deep-ECCENTRIC: Deep Learning Reconstruction of whole-brain non-Cartesian Compressed-Sensing MR Spectroscopic Imaging
Authors: Weiser Paul , Langs Georg , Motyka Stanislav , Strasser Bernhard , Bogner Wolfgang , Goland Polina , Singh Nalini , Dietrich Jorg , Ulhmann Erik , Batchelor Tracy , Cahill Daniel , Hoffmann* Malte , Klauser* Antoine , Andronesi* Ovidiu
Published: November 2024

Motivation: Magnetic resonance spectroscopic imaging (MRSI) is a unique method for non-invasive mapping of brain neurochemistry. While the latest advancements in acquisition enable whole-brain high-resolution metabolic imaging, these methods have lengthy reconstruction times that limit the clinical use. Goal(s): To realize a fast end-to-end reconstruction pipeline for high-resolution whole-brain MRSI compatible with online processing and clinical use. Approach: We developed a rapid deep-learning reconstruction pipeline for 3D non-Cartesian Compressed-Sensing MRSI (ECCENTRIC). Results: Our approach reconstructs in a few minutes high-resolution ECCENTRIC (k,t) data. We demonstrate a 60-fold speed-up in reconstruction time, facilitating the use in clinical routine. Impact: We present Deep-ECCENTRIC: a deep-learning pipeline for end-to-end reconstruction of 3D non-Cartesian Compressed-Sensing MRSI. We showcase spatially precise reconstructions with high spectral consistency, at a 60-fold speed-up over conventional reconstructions, which facilitates the clinical use of fast high-resolution MRSI.
Enhancing CS-MRI Reconstruction Using Improved ESSGAN with Convolutional Block Attention Module
Authors: Li Xia , Shen Yihui , Caut Maeva , van Loo Hadrien , Li Tie-Qiang
Published: November 2024

Motivation: Inspired by DR-CAM-GAN's progress in CS-MRI, we embraced ESSGAN with self-attention mechanisms. Goal(s): To assess CBAM's impact on ESSGAN's ability to enhance CS-MRI reconstruction across diverse sampling rates. Approach: Implemented ESSGAN+CBAM and performed experiments using T1-weighted brain images from the MICCAI 2023 dataset. Ablation studies compared DR-CAM-GAN, ESSGAN, ESSGAN+CAM, and ESSGAN+CBAM across varying sampling rates. Results: At a 10% low sampling rate, ESSGAN and ESSGAN+CBAM demonstrated similar performance. Nevertheless, at higher sampling rates (≥20%), ESSGAN+CBAM outperformed all other models, affirming its effectiveness across evaluation metrics. Impact: The study reveals that the integration of CBAM modules significantly enhances ESSGAN's performance in CS-MRI, particularly at higher undersampling rates, making it a valuable tool for rapid and accurate image reconstruction in clinical settings.
Feasibility of High-Resolution SSFSE MR imaging using Deep Learning Reconstruction in Assessment of Ovarian Volume and Follicle Count
Authors: Yang Renjie , Zou Yujie , Liu Weiyin , Wen Zhi , Li Liang , Zha Yunfei
Published: November 2024

Motivation: Transvaginal ultrasonography (TVUS) often underestimates follicle count (FC) compared to MRI. The repeatability of FC and ovarian volume (OV) assessment was still affected by motion artifact on conventional T2-weighted fast spin echo images. Goal(s): To propose a more reliable MRI technique in assessing the FC and OV. Approach: High-resolution single-shot fast spin echo (SSFSE) sequence was used to accelerate the acquisition speed, and AIRTM Recon DL was used to compensate for noise in this study. Results: Contributing to the improved time resolution and reduced noise, SSFSE-DL demonstrated better repeatability in FC and OV assessment compared to the widely used motion-robust PROPELLER technique. Impact: High-resolution SSFSE sequence with DL reconstruction can be a reliable imaging method in the assessment of ovarian morphology. It has a potential in determining the threshold value of FC for PCOM identification in future studies.
Tabletop Magnetic Particle Imaging Using Deep FPGA-based Convolutional Neural Network
Authors: Li Maofan , Zhou Yihang , Huang Kangjian , Liu Congcong , Li Nan , Li Ye , Liang Dong , Zheng Hairong , Liu Shengping , Wang Haifeng
Published: November 2024

Motivation: When using complex prior parameters for regularization in the MPI reconstruction method based on the system matrix, the process is very time-consuming and the preprocessing process is complex. Goal(s): The purpose of this study is to simplify the reconstruction process and achieve efficient real-time reconstruction of magnetic particle images. Approach: Therefore, this study uses CNN neural network for reconstruction on FPGA, and tests the neural network reconstruction effect through simulation and customized MPI system(Fig. 1). Results: The results show that it is feasible to use CNN neural network for reconstruction on FPGA, achieving high efficiency and real-time performance of magnetic particle image reconstruction. Impact: FPGA-based CNN reconstruction network can make desktop magnetic particle imaging easier and more efficient.
Renal Mesh to Perform Regional Perfusion Analysis of The Kidney
Authors: Sanmiguel Luis , De Visschere Pieter , Pullens Pim
Published: November 2024

Motivation: ASL-measured perfusion values are assumed to change homogeneously across the kidney. We believe that additional information might be retrieved by analyzing regional perfusion differences. Goal(s): Renal perfusion is assumed to decrease with aging but also in renal pathologies. Does renal perfusion decrease uniformly?Approach: Retrieve renal ASL images from 27 subjects. Create one 2D mesh per subject. Pool the results for 3 different groups. Analyze the variation of renal perfusion.Results: Patients compared to younger healthy volunteers (HVs) show lower perfusion. Patients compared to older HVs exhibit a negative variation in the kidney lateral outermost region, while other regions show higher perfusion values.Impact: Understanding kidney blood flow patterns could aid early disease detection and personalized treatments. Identifying specific regions affected in kidney patients might improve targeted therapies. This knowledge may enhance patient outcomes, potentially leading to a better life quality for kidney patients.
Comparative value of free-breathing DCE-MRI based on GRASP and conventional BH-VIBE sequence in evaluating the image quality in lung cancer
Authors: Yang Qing , Yu Jinghuo , Liu Mengxiao , Lei Junqiang
Published: November 2024

Motivation: Dynamic Contrast Enhancement-CT (DCE-CT) and Dynamic Contrast Enhancement-MRI (DCE-MRI) are among the most valuable imaging techniques for the noninvasive assessment of tumor vascular perfusion and trophoblast angiogenesis in lung cancer. Goal(s): To evaluate the image quality value of free-breathing GRASP in MRI of lung cancer. Approach: All patients underwent free-breathing DCE-MRI with breath-holding VIBE sequence before and after contrast injections. Two experienced thoracic radiologists independently evaluated the overall image quality, lesion contour, artifact level, and diagnostic confidence using the patient's simultaneous CT images as a reference. Results: Free-breathing GRASP DCE-MRI technique is a reliable method for the high-resolution imaging of pulmonary lesions. Impact: The study showcases GRASP's superiority over BH-VIBE in lung cancer imaging, offering clearer results with patient comfort. This breakthrough prompts investigation into GRASP's broader clinical applications and sets a new standard in patient-friendly MRI diagnostics.
fastMRI Breast: A publicly available radial k-space dataset of breast dynamic contrast-enhanced MRI
Authors: Solomon Eddy , Johnson Patricia , Zhengguo Tan , Knoll Florian , Moy Linda , Kim Sungheon Gene , Heacock Laura
Published: November 2024

Motivation: There is a lack of publicly available k-space data of breast dynamic contrast-enhanced (DCE) MRI that can be used for development of image reconstruction and machine learning methods for breast MRI. Goal(s): We aim to make a publicly available radial k-space dataset of breast DCE-MRI which will promote development of fast and quantitative breast imaging methods. Approach: Data of women undergoing routine diagnostic breast DCE-MRI exams have been acquired using a stack-of-stars radial imaging at 3T. Results: Our fastMRI breast dataset includes radial k-space data and case-level labels for 275 cases (70 malignant, 158 benign and 47 no-lesion cases). Impact: This work introduces the first large-scale dataset of radial k-space data for breast DCE-MRI acquired in diagnostic breast MRI exams. Having this dataset and accompanying reconstruction code publicly available, will support research and development of fast and quantitative breast DCE-MRI.
Fast Joint MR T1 and T2* Parameter Mapping with Scan Specific Unsupervised Networks
Authors: Heydari Amir , Kim Tae , Ahmadi Abbas , Wang Xiaoqing , Bilgic Berkin
Published: November 2024

Motivation: Joint MAPLE is an MR parameter mapping technique with improved results which suffers from long processing times. Goal(s): We propose a fast version of Joint MAPLE as a self-supervised, model-based multi-parameter mapping technique capable of jointly mapping T1, T2*, frequency and proton density in a whole brain volume ~50 times faster than the original version, while retaining its parameter mapping performance. Approach: A fast whole brain reconstruction, transfer learning and a rapid initialization in optimization is incorporated. Results: Results show that fast Joint MAPLE retains the mapping performance of the original version and outperforms existing methods. Impact: Fast Joint MAPLE estimates T1, T2*, frequency and proton density of a volume ~50 times faster than the original version with the same performance. A fast volume reconstruction, transfer learning and a rapid initialization is incorporated for faster mapping.
DeepEMC-T2 Mapping: Deep Learning-Enabled Echo Modulation Curve T2 Mapping
Authors: Pei Haoyang , Shepherd Timothy , Ng Michelle , Byun David , Wang Yao , Sodickson Daniel , Ben-Eliezer Noam , Feng Li
Published: November 2024

Motivation: Echo Modulation Curve T2 Mapping (EMC-T2) mapping can generate highly accurate, precise, and reproducible T2 quantification. However, the standard EMC-T2 framework requires ~10 echoes and a cumbersome post-processing step for pixel-wise dictionary matching. Goal(s): This work proposes a deep learning version of EMC-T2 mapping, called DeepEMC-T2, to enable efficient and accurate estimation of T2 maps from fewer echoes without requiring a dictionary. Approach: DeepEMC-T2 was developed using a spatiotemporal convolutional neural network, which estimates both T2 and PD maps directly from multi-echo spin-echo images. Results: DeepEMC-T2 enables efficient and accurate T2 mapping and requires only smaller number of echoes compared to standard EMC-T2. Impact: Standard EMC-T2 enables accurate T2 quantification but previously required a complicated post-processing step that made clinical translation challenging. DeepEMC-T2 enables efficient and accurate T2 quantification with fewer echoes. This could facilitate more widespread translation of this technique into clinical practice.
Novel deep learning approach combining image reconstruction and diagnostic segmentation
Authors: Saksena Vanya , Müller Christine , Kainz Bernhard , Knoll Florian
Published: November 2024

Motivation: Deep-learning-based image reconstruction methods for accelerated magnetic resonance imaging are optimized for global image quality metrics, but lack focus on diagnostically relevant features during training and reconstruction. Goal(s): A novel approach combining reconstruction and segmentation during training is investigated, incorporating feedback on clinically relevant features for reconstruction. Approach: Pretrained reconstruction (E2E-VN) and segmentation models (nnUNet) are connected. The reconstruction model is trained with a weighted combination of reconstruction and segmentation loss. Training and evaluation are performed on fastMRI+ data. Results: The proposed method resulted in improved image quality of reconstructed images at 8x acceleration compared to baseline E2E-VN, along-with improved downstream segmentation. Impact: Training deep-learning-based image reconstruction methods for accelerated MRI with additional feedback on diagnostic content improves image quality in the overall image and the region of interest, and subsequently the diagnostic utility of reconstructed images.
The use of deep learning based reconstruction in accelerating rectal cancer imaging
Authors: Yan Weijie , Xu Ziwei , Zhang Miaoqi , Zhang Bo
Published: November 2024

Motivation: Clinical routine rectal cancer imaging requires high resolution and hence increased number of excitations to achieve sufficient signal to noise ratio (SNR). Goal(s): The deep learning method is applied to improve the image quality and imaging speed of rectal magnetic resonance imaging. Approach: We investigate the use of deep learning based reconstruction in shortening the scan time of the T2-weighted TSE imaging (T2DL) sequence in rectal cancer imaging. Results: The results show that the DL reconstruction improves the SNR and CNR of the images. Also, the image acquisition time can be reduced by reconstructing images with reduced number of excitations by deep learning. Impact: Deep learning reconstruction may lead to unprecedented improvements in SNR and CNR compared to conventional reconstruction algorithms, which may be used to obtain higher quality images. In addition, deep learning methods can indirectly shorten image acquisition time.
Real-time automated assessment of image quality during MRI scanning for optimal image reconstruction
Authors: Buckup Marko , Mahajan Niraj , Rodriguez-Soto Ana , Chung Nuri , Contijoch Francisco
Published: November 2024

Motivation: MRI scans are often sensitive to subject motion, impacting image quality. One challenge is that it is difficult to detect and mitigate motion-related issues until after the scan has completed. Goal(s): To create a quantitative method for real-time evaluation of MRI scans, identifying events that may corrupt images and enabling prompt decision-making. Approach: The study used simulated data from the ACDC dataset, training a ResNET18 neural network to predict image quality using SSIM scores. Results: Our method can quickly and accurately assess MRI image quality. This could aid motion event detection. However, validation on actual data is needed. Impact: This study introduces an automated, deep-learning based method for real-time assessment of motion-related image quality for cardiac MRI. This innovation can potentially enhance the reliability and efficiency of MRI scans.
Mixed-sequence training for deep subspace learning image reconstruction of T1-T2-T2*-FF CMR Multitasking data
Authors: Hu Zheyuan , Cao Tianle , Chen Zihao , Xie Yibin , Li Debiao , Christodoulou Anthony
Published: November 2024

Motivation: Multi-parametric mapping using T1-T2-T2*-fat fraction (FF) MR Multitasking is promising but is hindered by lengthy reconstruction times. Goal(s): To improve T1-T2-T2*-FF Multitasking reconstruction time with deep subspace learning, overcoming challenges in training data scarcity and network scalability to high-dimensional spatial factors. Approach: A component-by-component (CBC) network structure was evaluated for three training strategies: 1) large T1 data, 2) limited T1-T2-T2*-FF data, and 3) multi-domain, mixed-sequence learning on both T1 and T1-T2-T2*-FF data. Results: Mixed-domain learning demonstrated superior image reconstruction quality, achieving the lowest normalized root mean squared error, displaying fewer structural artifacts, and narrowing Bland-Altman limits of agreement. Impact: Component-by-component deep-subspace-learning image reconstruction with mixed-sequence training can dramatically speed up T1-T2-T2*-fat fraction (FF) MR Multitasking image reconstruction by approximately 600 times, potentially overcoming a major barrier to clinical translation.
Deep image-pass filter for dynamic MRI reconstruction
Authors: Zhang Yinghao , Hu Yue
Published: November 2024

Motivation: For accelerating dynamic MRI, the gradient flow in the unsupervised deep image prior (DIP) methods lacks a direct pathway from the image domain to the network parameters, resulting in suboptimal performance. Goal(s): To propose a novel approch to address DIP's drawback for robust dynamic MRI reconstruction. Approach: Deep image-pass filter is proposed, replacing the random noise input of DIP with learnable image and constraint input consistent with output to establish an efficient gradient pathway from image domain. Results: Experimental results in the reconstruction of both long-axis and short-axis dynamic cardiac cine MRI demonstrate that DIPF outperforms DIP and other state-of-the-art unsupervised methods. Impact: We have introduced a new formulation for unsupervised MRI reconstruction, which will drive a series of research around this paradigm, including network architecture design, reconstuction models combining DIPF with other data priors, and more.
Attention-based two-stage network for non-cartesian multi-coil ASL MRI reconstruction
Authors: Guo Yanchen , Chen Shichun , Li Zhao , Taso Manuel , Alsop David , Dai Weiying
Published: November 2024

Motivation: High-resolution arterial spin labeling (ASL) imaging is time-consuming, limiting its clinical applications in studying small brain structures. Goal(s): To reconstruct high-resolution ASL images from 8-time accelerated ASL image acquisition, an under-sampled non-Cartesian k-space sampling. Approach: We proposed an attention-based deep learning (DL) model. Results: The proposed DL model can successfully reconstruct 8-fold under-sampled, non-cartesian, multi-coil data from k-space. Impact: Our proposed attention-based deep learning model can reconstruct under-sampled non-cartesian multi-coil data in k-space and thereby significantly decrease long MRI acquisition time required for high-resolution ASL MRI imaging, which may enable clinical applications in studying small brain structures.
Robust Magnetic Resonance Reconstruction by Alternating Deep Low-Rank Approach
Authors: Huang Yihui , Wang Zi , Zhang Xinlin , Lin Meijin , Guo Di , Qu Xiaobo
Published: November 2024

Motivation: Magnetic resonance reconstruction by deep learning is heavily compromised due to the mismatch between the training and target data, such as the sampling rate of undersampling, the organ and the contrast of imaging. Goal(s): Reliablely reconstruct magnetic resonance signal in multiple scenes by one trained deep learning modelApproach: Alternating Deep Low-Rank, which combines deep learning solvers and classic low-rank optimization solvers. Results: Compared with state-of-the-art deep learning methods HDSLR and ODLS, one ADLR trained by coronal PDw knee can provide a lower reconstruction error by about 10% in coronal PDw knees, 15% in sagittal PDw knees, and 30% in axial T2w brains. Impact: The proposed ADLR can effectively alleviate the drop in reconstruction quality due to the mismatches of attributes between training and target signals of the MR imaging or MR spectroscopy.
CAMERA-NET: Cascade Multi-Level Wavelet neural network with data consistency for MRI Reconstruction
Authors: Zhu Gaojie , Shen Xiongjie , Guo Hua
Published: November 2024

Motivation: The U-net architecture has experienced widespread application in deep learning-powered MR accelerated reconstruction. Nevertheless, the down-sampling and up-sampling operations embedded within the structure curtail the recovery capabilities of MR sparsely sampled signals. The U-net is widely used in deep learning-based MRI reconstruction. Its encoding-decoding component enlarges receptive field while the pooling and interpolation operation limits the ability to recover sparsely sampled MR signals. Goal(s): We propose a model that combines wavelet transformations and convolutional neural networks for MRI accelerated reconstruction, while incorporating data consistency constraints that represent a priori physical information. The wavelet transform and inverse wavelet transform are introduced to replace pooling and interpolation operations in order to maintain the spatial information of images during the encoding-decoding process within the neural network. Approach: We present a cascaded multi-level wavelet neural network with data consistency for accelerated MRI reconstruction framework, which we call CAMERA-NET. The CAMERA-NET introduces wavelet transform and inverse wavelet transform instead of pooling and unpooling to perform more accurate downsampling and upsampling. Simultaneously, we incorporate wavelet transform into the convolutional neural network, consequently refining its learning capability. Furthermore, to maintain a balance between high-frequency and low-frequency information, we suggest implementing the loss dropout technique to guide the neural network and preserve the important details. A cascaded multi-level wavelet neural network with data consistency, termed as CAMERA-Net, is presented for under-sampled MRI reconstruction. Results: Using the publicly available fastMRI knee datasets, it was found that upsampling and downsampling networks based on wavelet transformation produce better results compared to U-networks based on pooling and unpooling techniques. The inclusion of data consistency restrictions can additionally enhance the reconstruction performance. Reconstruction quality can be improved by increasing the number of iterations and optimising the loss dropout function. CAMERA-Net demonstrates significant enhancement in reconstructing quality with public fastMRI knee dataset. Impact: The improved reconstruction capabilities of CAMERA-Net have the potential to enhance precision and reliability when reconstructing under-sampled MRI data. This could result in more efficient clinical scans.
Revolutionizing MR Elastography: Deep Learning-Powered Stiffness Map Reconstruction from Sparse Wavefield Data.
Authors: Iftikhar Hassan , Ahmad Rizwan , Kolipaka Arunark
Published: November 2024

Motivation: AI has proven itself in improving MRI reconstructions, yet its potential in estimating MRE stiffness maps with rapidly acquired data remains unexplored. Goal(s): Investigating the untapped potential of AI in MR Elastography, promising advancements in diagnostic accuracy and efficiency of this modality. Approach: 3D FEM was used to create the dataset, and Deep Learning was used to reconstruct the stiffness maps from sparse wavefield dataResults: The Deep Learning model was able to effectively reconstruct the MRE-Stiffness maps at high acceleration rates. The model's performance was reported in terms of SSIM. Impact: This innovative study leverages deep learning and finite element modeling to reconstruct liver stiffness maps from under-sampled MR Elastography data. The proposed AI approach demonstrates robustness and potential for accelerating stiffness estimation, paving the way for improved tissue stiffness estimation.
One-Heartbeat Cine MRI with Implicit Neural Representations Reconstruction
Authors: Catalán Tabita , Courdurier Matias , Osses Axel , Botnar René , Sahli-Costabal Francisco , Prieto Claudia
Published: November 2024

Motivation: Cardiac cine MRI is the gold standard for cardiac functional assessment but requires acquiring several slices under multiple breath-holds, leading to limited number of cardiac phases, patient fatigue and misregistration between slices. Goal(s): To develop a novel undersampled reconstruction based on Implicit Neural Representations (INR) to enable continuous cardiac cine MRI in a single heartbeat. Approach: INRs allow implicitly regularized reconstruction of radial cardiac cine MRI without ECG gating. The proposed method is compared to a fully sampled acquisition and iterative SENSE in a healthy subject. Results: The proposed approach shows comparable results to the fully sampled images but offering higher temporal resolution. Impact: The proposed method allows implicitly regularized single heartbeat reconstruction of radial cardiac cine MRI without ECG gating, offerings potential improvements in cardiac cine acquisition efficiency and patient comfort.
Supervised Pretraining and Self-Supervised Finetuning enables Robust Reconstruction of High-Resolution 7T MP2RAGE for Multiple Sclerosis
Authors: Yu Thomas , La Rosa Francesco , Piredda Gian Franco , Nickel Marcel Dominik , Silva Jonadab Dos Santos , Dieckhaus Henry , Nair Govind , Liebig Patrick , Thiran Jean-Philippe , Kober Tobias , Beck Erin , Hilbert Tom
Published: November 2024

Motivation: The infeasibility to collect large datasets of high-resolution, fully-sampled 7T data hinders the training of deep learning reconstructions for accelerated high-resolution 7T scans. Goal(s): Demonstrate that combining pretraining on fully-sampled scans from 1.5/3T and self-supervised finetuning on a few undersampled 7T scans enables robust reconstruction. Approach: High-resolution(0.5mm isotropic), 3D, 7T MP2RAGE scans of multiple sclerosis patients are used for finetuning/testing. Results: Deep learning reconstructions from one 7T scan have higher apparent SNR than a median of GRAPPA reconstructions from three scans, currently used for assessment, with similar tissue contrast and lesion conspicuity, potentially reducing scan time by a factor of three. Impact: Very high-resolution 3D scans can require infeasible acquisition time to generate images suitable for clinical use. Our work shows the potential for deep learning reconstructions to reduce scan time by a factor of three, without fully-sampled 7T data.
A comparison of coil loading and SNR for elliptical solenoidal coil vs. a tight fitting dome helmet coil for low field neuroimaging
Authors: O'Reilly Tom , Webb Andrew
Published: November 2024

Motivation: The narrow bandwidth of RF coils for low field MRI means that coils often require re-tuning between subjects due to different loading conditions. Goal(s): We compare the performance and loading effects of an elliptical solenoid and a dome helmet coil to see which coil is more sensitive to loadingApproach: The SNR and transmit efficiency of the two coil types are compared in when lightly loaded (phantom) and normally loaded (in-vivo) settingsResults: The elliptical solenoid coil has a substantially smaller frequency shift when loaded (~1kHz vs ~13kHz) and has superior SNR in-vivo compared to a tight -fitting dome helmet coil. Impact: We compare the performance of tight-fitting dome helmet coil to an elliptical solenoid coil and find that due to reduced loading the performance of the elliptical coil is superior in-vivo while having better frequency stability with differing loads.
Rapid Zero-shot Image Denoising for Quantitative Imaging on a Point-Of-Care 46-mT-MRI System
Authors: Dong Yiming , Lena Beatrice , O’Reilly Tom , Mach Mathieu , Rao Chinmay , Li Ziyu , van Osch Matthias , Webb Andrew , Börnert Peter
Published: November 2024

Motivation: Low-field MRI holds the promise of expanding access to healthcare. The low signal-to-noise ratio (SNR) poses a significant challenge to acquiring diagnostically useful information in a reasonable scanning time. Goal(s): To overcome the challenge of low SNR in low-field MRI, achieving fast, self-supervised denoising. Approach: A rapid 4D-denoising method utilizing the Zero-Shot-Noise2Noise framework is proposed, without the need for intensive network training. Results: This method provides fast denoising in just 10-20 seconds per case and significantly boosts SNR efficiency, reducing the number of measrued TIs and TEs needed for precise, high-quality T1/T2 mapping. Impact: This study's fast 4D-denoising approach revolutionizes low-field MRI by enhancing SNR without extensive training datasets, enabling faster, more efficient imaging and broadening diagnostic accessibility in resource-limited settings.
Scan-specific deep learning-based denoising method for low-field MR images
Authors: Ayde Reina , Salameh Najat , Sarracanie Mathieu
Published: November 2024

Motivation: Low SNR per unit-time in low-field MRI results in noisy images when targeting both clinically acceptable resolution and acquisition times which may limit their diagnostic effectiveness. Goal(s): We seek to improve low-field MRI SNR by means of deep-learning while overcoming the limitations of traditional supervised learning and without compromising denoising performance. Approach: We build on:1)self-supervised method enabling training without having to collect ‘noise-free’ data and 2)zero-shot concept to achieve dataset-free and scan-specific denoising.Additionally,we adopted simplified architecture for fast training times. Results: Our method showed high denoising performance for different SNR levels and contrasts within few seconds of processing time competing with well-established BM4D. Impact: Our proposed denoising method, based on self-supervised zero-shot deep-learning, enables high-performance denoising within short processing times. This approach shows promise for speedy acquisitions and enhanced imaged quality in low-field, point-of-care settings.
Longitudinal Relaxivities of MRI Contrast Agents on an Ultra Low Field, Point-of-Care MRI System
Authors: Poorman Megan , Bree Kendyl , Ragunathan Sudarshan , Nair Govind
Published: November 2024

Motivation: Point-of-care MRI systems have the potential to increase access to imaging. Contrast agents could enhance tissue differentiation at the ultra-low fields of these devices. Goal(s): To characterize the performance of Gadolinium contrast agents on an FDA cleared, point-of-care MRI system to inform sequence optimization for in vivo imaging. Approach: Longitudinal relaxivities of six FDA approved gadolinium contrast agents were characterized at 64 mT on a point-of-care MRI device and used to optimize T1 imaging of white matter and CSF. Results: Longitudinal relaxivities increased at 64 mT compared to their 3 T values. Simulations showed enhancement could be well visualized within feasible sequence parameters. Impact: Point-of-care MRI systems have the potential to impact patient care by increasing access to imaging. This work explores the feasibility of contrast-enhanced imaging with FDA-approved agents at ultra-low field on a point-of-care MRI.
Multi-tissue constrained spherical deconvolution with spherical mean density estimation
Authors: Tournier J-Donald
Published: November 2024

Motivation: Multi-tissue constrained spherical deconvolution (MT-CSD) average densities depend on the local fibre arrangement, as it relies on the non-negativity of the fibre orientation density functions (fODF). Goal(s): We investigate whether MT-CSD can be performed without influence of the orientation structure of fibre ODF. Approach: Using the spherical mean of each b-value shell to factor out the angular structure, we rely purely on the different tissue types' b-value dependence, with the angular part of the fODF estimated subsequently . Results: The method produces plausible, but subtly different results from 'regular' MT-CSD, which appear to be less affected by fibre arrangement. It is also substantially faster. Impact: The ability to decompose the signal based purely on its spherical mean b-value dependence may provide more reliable density estimates, though this needs further investigation. The computational efficiency of this approach may also make it more suitable for real-time use.
Prediction of Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Using Intravoxel Incoherent Motion Histogram Parameters
Authors: Li Yanbo , Xue Yuchen , Guo Jinxia , Xie Lizhi , Lu Hong
Published: November 2024

Motivation: The motivation behind this study lies in the need for accurate prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer. Goal(s): Our goal was to assess the potential of IVIM-derived histogram parameters as predictive markers before NAC in breast cancer patients. Approach: We conducted a comprehensive analysis of 287 breast cancer cases, utilizing IVIM model-based parameters and multivariate logistic regression. The study included voxel-wise analysis. Results: The prediction model, combining the selected IVIM parameters and PR status, exhibited strong discriminative power. Additionally, we identified the minimum of D, Skewness of D* and D as significant volumetric predictors associated with pCR. Impact: The impact of this study is significant, as it enhances breast cancer treatment by providing a reliable predictive model for neoadjuvant chemotherapy response, potentially improving patient outcomes and treatment strategies.
Potential Diagnostic Efficiency of Intravoxel Incoherent Motion-based Virtual Magnetic Resonance Elastography in Pulmonary Neoplasms
Authors: Zhang Shuo , Du Yonghao , Liang Ting , Zhang Xuyin , Guo Yinxia , Yang Jian , Li Xianjun , Niu Gang
Published: November 2024

Motivation: Exploring non-invasive techniques with the potential of replacing invasive pathological analysis of pulmonary neoplasms remains urgent. Goal(s): To investigate the feasibility of IVIM-based vMRE to provide quantitative estimates of tissue stiffness in pulmonary neoplasms and to verify the diagnostic performance in distinguishing neoplasm property. Approach: sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored. Results: Virtual stiffness values of malignant neoplasms were significantly higher than benign ones. Subsequent sub-type analyses showed adenocarcinoma and squamous cell carcinoma were mostly stiffer than non-specific benign neoplasms. Impact: IVIM-based vMRE has good feasibility in reflecting stiffness of pulmonary neoplasms. Virtual stiffness and sADC values showed significant difference between malignant and benign lesions.The application of non-invasive vMRE is promising as a new method for clinical diagnosis.
Supporting corticomedullary differentiation for kidney ADC mapping with T1-weighted EPI
Authors: Henningsson Markus , Robson Matthew , Fernandes Carolina
Published: November 2024

Motivation: Previous studies have demonstrated a correlation between kidney fibrosis and corticomedullary difference in apparent diffusion coefficient (ADC). However, separately measuring ADC in cortex and medulla is challenging due to the low contrast between the tissues. Goal(s): To facilitate measurement of corticomedullary ADC. Approach: T1-weighted, distortion-matched echo planar imaging (EPI) with high corticomedullary contrast were acquired in addition to diffusion weighted EPI. Regions-of-interest were performed in the T1-weighted images and propagated to the distortion-matched ADC maps to obtain measurements. Results: Corticomedullary ADC differences were obtained in four datasets from two healthy volunteers with values within the range of previously published values. Impact: T1-weighted, distortion-matched EPI with high corticomedullary contrast greatly facilitates measurements of cortex and medulla in diffusion weighted EPI.
Diagnostic value of modified clear cell likelihood score based on pseudocapsule for predicting the malignancy in small renal masses
Authors: Hao Yu-Wei , Bai Xu , Cui Meng-Qiu , Ning Xue-Yi , Ding Xiao-Hui , Ye Hui-Yi , Wang Hai-Yi
Published: November 2024

Motivation: Clear cell likelihood score version 2.0 (ccLS v2.0) is not yet able to predict malignancy in small renal masses (SRMs). Pseudocapsule may provide added value with ccLS v2.0Goal(s): To develop a modified clear cell likelihood score based on pseudocapsule (m-ccLS) for predicting malignancy in SRMsApproach: Receiver operating characteristic curve (ROC), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare diagnostic value of m-ccLS and ccLS v2.0Results: Area under curve (AUC), NRI and IDI of m-ccLS were superior to those of ccLS v2.0 for the diagnosis of malignancy. The likelihood of malignancy increased with higher m-ccLS scoresImpact: M-ccLS achieved predict malignancy of small renal masses (SRMs). M-ccLS may be a valuable tool for expanding the clinical application of this scoring system and contributing to its further promotion and dissemination
Differential diagnosis of renal tumors using dual echo time diffusion weighted imaging (dual TE-DWI)
Authors: Yamamoto Akira , Tamada Tsutomu , Ueda Yu , Higaki Atsushi , Kanki Akihiko , Yoshida Koji , Sanai Hiroyasu , Moriya Kazunori , Miyaji Yoshiyuki
Published: November 2024

Motivation: In renal tumors, it is often difficult to differentiate between benign and malignant tumors, which could reduce unnecessary biopsies and surgeries. Goal(s): To differentiate between angiomyolipoma (AML) and renal cell carcinoma (RCC). Approach: Imaging with two types of diffusion-weighted images (DWI) with different Echo times (TE) and comparing T2 signals in AML and RCC at DWI (b=0) and DWI (b=1200). Results: T2 signals at high diffusion restriction DWI (b=1200) were significantly lower in AML compared to RCC. Impact: There is potential to preoperatively differentiate AML non-invasively, which previously due to the difficulty in differentiation, led to invasive diagnostic methods such as biopsies and unnecessary surgeries.
Diagnostic and Prognostic Value of MRI-based Node-RADS for Regional Lymph Node Metastasis in Renal Cell Carcinoma
Authors: Bai Xu , Wang Haiyi
Published: November 2024

Motivation: The prognostic property of regional lymph node metastasis (RLNM) has been widely recognized, but the diagnostic workup has stagnated for renal cell carcinoma (RCC). Goal(s): This study aimed to assess the diagnostic performance of MRI-based Node Reporting and Data System (Node-RADS) for RLNM and to explore its prognostic impact on patients with RCC. Approach: A single-center retrospective comparative study. Results: MRI-based Node-RADS presented better diagnostic performance for RLNM than the size criteria and previous clinical models (AUC, 0.91 vs 0.79–0.85; all P<.05), and exhibited a substantial prognostic value for RCCs regarding progress-free survival and overall survival (both P<.001). Impact: Node-RADS, a concept that combines size, texture, margin, and shape, is a promising approach for lymph node metastasis in RCCs, which may contribute to improving clinical node staging and guiding clinical decision making.
Bladder Biomechanics and Shape Characterization: Comparison Study of Healthy vs BPH using Uro-Dynamic MRI
Authors: Gonzalez-Pereira Juan , Wells Shane , Grimes Matthew , Bushman Wade , Roldan-Alzate Alejandro
Published: November 2024

Motivation: Current standards for assessing the lower urinary tract (LUT) provide limited anatomical information. Uro-Dynamic MRI proves to be a valuable non-invasive tool for LUT biomechanics analysis. Bladder shape has been studied in static imaging but not in dynamic imaging. Goal(s): Build a comprehensive, non-invasive framework to study bladder biomechanics in healthy and diseased subjects. Approach: Uro-Dynamic MRI was implemented and used in five healthy and five BPH subjects. Novel MRI-derived bladder shape metrics were developed and quantified in all subjects. Results: We observe quantifiable differences on correlation coefficients of flowrates and calculated bladder shape metrics between healthy and patients with benign prostatic hyperplasia. Impact: Uro-Dynamic MRI allows biomechanical and shape analysis of the lower urinary tract(LUT), allowing comparisons between healthy and diseased subjects. Higher correlation values with flowrates observed on healthy subjects. Coupled Bladder biomechanical and shape analysis allows potential characterization of LUT disease.
Lung MRI Screening protocol at 0.55T: Initial Experience in Healthy Volunteers
Authors: Lee Yoo Jin , Sohn Jae Ho , Capaldi Dante , Liu Jonathan , Shridhar Shrav , Yang Yang
Published: November 2024

Motivation: The current clinical standard for screening lung imaging is chest CT, which is associated with radiation exposure and only provides static, anatomical information of the lung and concurrently imaged heart. Goal(s): We are exploring an alternative method for lung pathology screening. Approach: Using a 0.55T MRI scanner and the proposed screening Lung MRI protocol, we conducted lung MRI scans on 10 healthy volunteers. Results: Initial assessments demonstrate concurrent evaluation of the anatomy, motion, and tissue characteristics of the lung and heart in around 25 minutes. Impact: We propose a screening Lung MRI protocol as a potential alternative imaging option in screening as well as follow up imaging in particular conditions, with the benefit of eliminating radiation dose and additional respiratory and cardiac motion and tissue information