bluuebunny/crossref_metadata_2025 huggingface dataset:
A 16-Ch Elastic Thin RF Coil Array for Whole Brain Concurrent TMS-fMRI
Authors: Duan Yunsuo , Liu Feng , Marsh Rachel , Riddle Matthew , Patel Gauraw , Kegeles Lawrence , Vaughan John , Grinband Jack
Published: November 2024

Motivation: The low TMS efficiency and complexity of the setup for whole brain concurrent TMS-fMRI remain challenging due to the lack of feasible RF coil arrays. Goal(s): We proposed a 16-channel elastic whole brain RF coil array to increase the TMS efficiency and flexibility. Approach: We developed a highly flexible close-fitting coil former with 3D printing and evenly placed the 16 coil loops made of flexible shielding sleeves on the former. Results: The measured TMS efficiency was about 80% at the inner wall of the coil array. The SNRs of experimental images were comparable with those acquired using Nova 32-ch coil array. Impact: The proposed 16-ch elastic coil array was capable of significantly improving the TMS efficiency and simplifying the setup of whole brain concurrent TMS-fMRI.
Four-Channel MM-Wave Radar Testbed for Neck vs Chest Cardiac Sensing
Authors: Hughes Lauren , Robb Fraser , Vasanawala Shreyas , Pauly John , Scott Greig
Published: November 2024

Motivation: Respiratory and cardiac gating are common in modern MRI, but require additional sensors in contact with the patient and the associated cabling. Goal(s): Our goal is to determine if CW mm-wave radar can act as a robust non-contact cardiac and respiratory sensor, with potential for gating in MRI. Approach: A 4-channel 24 GHz radar test bed is developed with a pair of neck radars and a pair of chest radars. SSA and ICA methods help extract waveform structure relative to an ECG. Results: The CW radars nicely separate breathing and heart rate with distinct features at the ECG QRS complex. Impact: MM-wave radar could provide a non-contact sensor of cardiac/breathing in MRI. Potential applications include non-contact gating, and independent vascular pulse or motion sensing for use in neuroimaging.
Ultrahigh Resolution Imaging of Zebrafish Embryos with a µMRI Insert in a Horizontal Bore Small Animal Scanner
Authors: Hüfken Thomas , Lobmeyer Tobias , Gahr Bernd , Rasche Volker
Published: November 2024

Motivation: The absence of hardware specifically tailored for horizontal bore small animal MRI systems hinders the achievement of ultrahigh-resolution imaging. Goal(s): To facilitate volumetric imaging at sub 10³ µm³ spatial resolutions in a conventional horizontal small animal scanner. Approach: A custom-made high-performance gradient system and RF-hardware was interfaced to the system RF- and gradient-amplifiers, avoiding the requirement for specialized imaging software. A constant time imaging sequence was employed to capture ultrahigh-resolution images. Results: The setup was successfully tested for imaging of zebrafish embryos at different time points post fertilization with an isotropic spatial resolution of 9³ µm³. Impact: The suggested approach enables isotropic single-digit µm ultrahigh resolution imaging in conventional horizontal bore MRI systems. This supports novel imaging applications for a wide range of tiny animals, plants, or biological tissue without dedicated microscopy MR systems.
Imaging pressure gradients and stress fields driving ISF flow in the brain by assimilating DENSE pulsatile motion data into a poroelastic model.
Authors: Mcgarry Matthew , Zwanenburg Jaco , Weaver John , Paulsen Keith
Published: November 2024

Motivation: Interstitial fluid (ISF) flow in the brain is important for brain function and therapies. Very slow ISF flow is difficult to directly measure, however, the driving forces can be estimated from pulsatile motion fields. Goal(s): We assimilate pulsatile motions from DENSE sequences into a poroelastic computational model which allows fluid pressure gradient and solid stress fields to be extracted. Approach: Generalized least squares and Galerkin weighted residual methods were used to fit a pulsatile blood pressure field to the data and compute stresses/pressure gradients. Results: Stress and pressure images show good symmetry and distributions are as expected from anatomical considerations. Impact: Imaging the pulsatile fluid pressure gradients and solid stress fields provides new insights into the forces which drive flow of interstitial fluid in the brain, which is of critical importance in Alzheimer’s disease and currently very difficult to directly measure.
Physically and Anatomically Constrained Self-Supervised Motion Correction for Free-Breathing Cardiac T1 Mapping
Authors: Hanania Eyal , Volovik Ilya , Cohen Israel , Freiman Moti
Published: November 2024

Motivation: Cardiac T1 mapping is often limited by the need for breath-holding to prevent motion artifacts, which restricts its use in patients who cannot hold their breath. Goal(s): To create a self-supervised deep learning method for motion-corrected, free-breathing cardiac T1 mapping without requiring large datasets or worrying about data variability. Approach: We present a new self-supervised model that combines a signal relaxation model with anatomical constraints and employs the voxel-morph framework for motion correction. Our model's performance was assessed using a publicly available myocardial T1 mapping dataset. Results: Our approach outperformed other state-of-the-art registration methods in terms of R2, DICE, and Hausdorff distance. Impact: Our model offers the possibility of extending cardiac T1 mapping to patients who cannot perform breath-hold MRI procedures by ensuring robust motion correction for accurate T1 mapping, all without the necessity for large training datasets or worries about data anomalies.
Highly-efficient free-breathing 3D whole-heart joint T1/T2 mapping and water/fat imaging at 0.55T
Authors: Si Dongyue , Crabb Michael , Kunze Karl , Littlewood Simon , Prieto Claudia , Botnar René
Published: November 2024

Motivation: Myocardial T1 and T2 mapping has emerged as a useful clinical tool for the diagnosis of different heart disease. However, current mapping sequences were mostly developed with 2D breathhold acquisitions and validated at 1.5T or 3T. The investigation of myocardial mapping techniques on more affordable low-field MRI systems is scarce. Goal(s): To develop a highly-efficient free-breathing 3D whole-heart joint T1/T2 mapping sequence with isotropic-resolution at 0.55T. Approach: The proposed sequence acquires 3 interleaved volumes for joint T1/T2 estimation and water/fat separation. Results: The proposed sequence shows good agreement with spin-echo reference in phantom and provides comparable results in-vivo with conventional 2D mapping sequences. Impact: The proposed sequence enables comprehensive 3D joint T1/T2 mapping and water/fat anatomical evaluation of the whole-heart with 2mm isotropic-resolution at 0.55T during a fast free-breathing scan and thus shows promise for the detection of different cardiac diseases.
Free-breathing respiratory-navigator-gated 2D radial MR fingerprinting of the transplanted heart at 3T.
Authors: Calarnou Pauline , Ogier Augustin , Abdurashidova Tamila , Ledoux Jean-Baptiste , Yerly Jérôme , Hullin Roger , Van Heeswijk Ruud
Published: November 2024

Motivation: To acquire T1, T2 and ECV maps in heart transplant recipients using a free-breathing technique that minimizes through-plane motion. Goal(s): To characterize a free-breathing 2D joint T1/T2 cardiac MR fingerprinting technique named PARMA that includes a lung-liver navigator in the patient setting. Approach: We compared the resulting maps to gold standard maps in a phantom and to clinical routine maps in 10 healthy volunteers and 9 heart transplant recipients. Results: We found high accuracy in the phantom and high precision in the volunteers and heart transplant recipients. Impact: This work demonstrates the feasibility of a free-breathing 2D joint T1/T2 MR fingerprinting in a heart transplant recipient population that cannot always perform long breath holds. The navigator allows a free breathing acquisition with limited through-plane motion.
Biophysical analysis of hepatocellular carcinoma and tumor niche in an orthotopic mouse model with diffusion MRI and MR elastography
Authors: de Moraes Pedro Dantas , Safraou Yasmine , Krehl Karolina , Meyer Tom , Häckel Akvile , Schellenberger Eyk , Kühl Anja , Braun Jürgen , Savic Lynn , Sack Ingolf , Guo Jing
Published: November 2024

Motivation: The biomechanical interplay between hepatocellular carcinoma (HCC) and the hosting liver is poorly understood. Goal(s): To characterize the development of HCC and its interactions with the surrounding liver using imaging-based biophysical properties. Approach: We investigated longitudinally HCC and the host liver in an orthotopic mouse model using MR elastography (MRE) and diffusion-weighted imaging (DWI). Results: During tumor development, the host liver became softer with reduced viscosity and restricted water diffusivity while HCC became stiffer, less viscous and restricted water diffusivity. Impact: Preclinical MRE is a useful tool to study biomechanical properties of tumors and the tumor environment. In a mouse model of hepatocellular carcinoma, we showed for the first time how liver tumors shape their biomechanical niche in the hosting liver.
Noninvasive assessment of liver fibrosis using multiple MRI parameters: an experimental study in rats
Authors: Guo Yiwan , Sun Peng , Wu Zhigang , Huang Chen , Jin Ziwei , Guo Tingting , Li Xin
Published: November 2024

Motivation: Early detection and noninvasive assessment of liver fibrosis are significant in clinical practice. Goal(s): Liver biopsy have some drawbacks for staging liver fibrosis. We aimed to investigated the value of T1ρ, DKI, and IVIM in assessment of liver fibrosis and established an optimal diagnostic model. Approach: Twenty four thioacetamide (TAA)-treated rats and six control rats were included in this experimental study. All rats were scanned with 3.0T MRI machine. Corresponding imaging parameters were measured and compared among different fibrosis stages. Results: T1ρ and MD derived from DKI had better diagnostic performance than other parameters. Their combination model further improved the diagnostic efficiency. Impact: T1ρ and DKI could easily incorporated into routine liver MR imaging. The model combining T1ρ and MD derived from DKI was promising to be a noninvasive biomarker to detect and accurately stage liver fibrosis in the clinical practice.
Quantitative MRI method for characterising endometriomas
Authors: Shah Usman , Bagur Alex , Curwen Hannah , Brady Mike , Collins Sally , Herlihy Amy , Pansini Michele
Published: November 2024

Motivation: Diagnostic delay and uncertainty are common problems faced by patients suffering from endometriosis. Although laparoscopy remains the gold-standard for diagnosis, quantitative MRI (qMRI) could offer an alternative non-invasive approach. Goal(s): To use qMRI to characterise endometriotic lesions. Approach: Quantitative analysis of multiparametric MRI data (including T1 MOLLI, T2* and PDFF parametric maps) to characterise tissue metrics obtained from patients suffering with chronic pelvic pain who subsequently underwent a diagnostic laparoscopy. Results: Endometriomas have characteristic qMRI values different to other tissues in the female pelvis. Impact: The distinctive parametric characteristics of endometriomas provides a foundation for the development of a non-invasive diagnostic tool that can detect endometriotic lesions within the pelvis.
Multiparametric radiomics-based machine learning predicts consensus molecular subtype 4 of colorectal cancer: a multi-center study
Authors: Liu Zonglin , Runqi Meng , Sun Yiqun , Rong Li , Caixia Fu , Tong Tong , Dinggang Shen
Published: November 2024

Motivation: The consensus molecular subtype (CMS) is a novel classification system that reflects the genetic characteristics of the tumor. CMS4 is associated with the worst prognosis. Goal(s): To investigate whether a radiomics-based machine learning approach could predict CMS4 status in CRC patients. Approach: The sequencing data was input into the CMS classification system to generate CMS subtype outcomes. Radiomics features were extracted from baseline T2WI and contrast-enhanced MRI. Machine learning algorithms were applied to explore the best-performing and most robust model. Results: The best performing model achieved AUCs of 0.855 and 0.759 in the test set and external validation set. Impact: The genetic phenotype of CMS4 colorectal cancer may be potentially associated with morphological features. Multiparametric radiomics-based machine learning shows promising potential in distinguishing CMS4 from other subtypes of CRC.
Deep learning model based on multiparametric MRI for prediction of synchronous liver metastasis from rectal cancer: a two-center study.
Authors: Sun Jing , Wu Pu-Yeh , Zheng Dechun
Published: November 2024

Motivation: Accurate synchronous liver metastasis (SLM) risk stratification is important for treatment planning and prognosis improvement. Goal(s): Our goal is to establish a non-invasive and quantitative prediction model of synchronous liver metastases (SLM) in rectal cancer (RC) to help with accurate staging. Approach: The deep learning (DL) model was fitted based on multi-parameter MRI of primary cancer combined with Clinical features (CF) features, and 5-fold cross-validation and external validation were performed. Results: We demonstrated that the combination of CF and DL features achieved a satisfactory predictive performance for SLM, and also confirmed the generalizability of this model by external validation. Impact: The discovery of the DL model would change treatment strategies. For patients with high-risk metastasis, a more aggressive systemic examination and shorter follow-up should be considered and may contribute to improved outcomes.
Disease Classification of 129Xe Ventilation MRI using Artificial Intelligence
Authors: Matheson Alexander , Bdaiwi Abdullah , Willmering Matthew , Hysinger Erik , McCormack Francis , Walkup Laura , Cleveland Zackary , Woods Jason
Published: November 2024

Motivation: Xenon ventilation MRI shows distinct defect patterns that appear disease-specific but are difficult to measure. Deep learning, via neural networks, can generate texture features to classify images. Image classification has applications in diagnostics, phenotyping and predicting outcomes.Goal(s): To determine if neural networks could determine disease classification from xenon MRI.Approach: 2D neural networks were trained on data from eight disease states (including healthy controls) and assessed on top-1, top-3 accuracy and recall.Results: The top performing network had a 54% top-1 and 86% top-3 accuracy.Impact: Artificial intelligence can classify disease from xenon MRI alone with moderate accuracy and differentiate between similar conditions. In the future, deep learning could be used diagnostically, for phenotyping disease subgroups and predicting outcomes.
Radio-pathomic maps of complex histo-morphometric features trained with whole mount prostate histology differentiate prostate cancer on MPMRI
Authors: Duenweg Savannah , Flatley Michael , Winiarz Aleksandra , Bobholz Samuel , Lowman Allison , Nath Biprojit , Kyereme Fitzgerald , Iczkowski Kenneth , Banerjee Anjishnu , LaViolette Peter
Published: November 2024

Motivation: The motivation of this study is to develop novel methods for mapping non-invasively the underlying history-morphometric features of prostate cancer. Goal(s): The goal of this study is to develop and demonstrate radio-pathomic mapping techniques to enable noninvasive detection of prostate cancer presence and distinction from benign tissue using MRI. Approach: Our approach was to align multiparametric MRI with digitized histology slides from prostatectomy specimens, then predict quantitative histological features from MRI intensities, and use these predicted features to classify cancer versus noncancer regions. Results: Our models can distinguish cancerous from noncancerous prostate tissue with 70% accuracy. Impact: This study uses radio-pathomic mapping for noninvasive prostate cancer detection, demonstrating the potential to differentiate cancerous vs benign prostate tissue using imaging surrogates of microstructural features discernible only on histology.
Advanced Deep Learning Denoising for Accelerated 0.55T Prostate MRI
Authors: Janjusevic Nikola , Bruno Mary , Huang Yuhui , Chen Jingjia , Wang Yao , Chandarana Hersh , Feng Li
Published: November 2024

Motivation: Low-Field MR offers a great platform for low-cost high-performance screening of prostate cancer, but it suffers from low SNR. Prolonged scan times are typically needed to achieve adequate SNR at low field. Goal(s): In this work, we developed an advanced deep learning denoising method for rapid high spatial resolution prostate MRI at 0.55T. Approach: The proposed approach was tested in T2-weighted prostate MRI. Supervised training was performed to denoise images acquired with different numbers of averages, corresponding to different scan times. Results: Deep learning was able to denoise prostate images at high spatial resolution resulting acquisition time with 1-2 average. Impact: The proposed denoising technique holds significant potential to promote the use of 0.55T MRI and other types of low-field MRI for prostate imaging and screening for prostate cancer, with reduced cost and greater accessibility.
Quantitative Water Permeability Mapping using Biophysical-modeling-based Deep Learning
Authors: Hu Renjiu , Zhang Qihao , Romano Dominick , Weppner Benjemin , Spincemaille Pascal , Nguyen Thanh , Wang Yi
Published: November 2024

Motivation: In diffusion-weighted arterial spin labeling (DW-ASL) images, quantification of the water exchange rate $$$k_{w}$$$ uses a single-pass approximation (SPA) which introduces systematic error while fitting the non-linear model is difficult. Goal(s): Our goal was to reduce the blood-brain-barrier (BBB) water exchange rate ($$$k_{w}$$$) quantification errors in DW-ASL images. Approach: We introduced the biophysical-modeling-based deep learning method (QTMNet) and tested both the simulated and in vivo data. Results: On simulated data, QTMNet has 90% less normalized root mean square error (NRMSE) compared to the traditional kinetic model. Impact: The improvement in evaluation accuracy by QTMNet may benefit Alzheimer’s Disease detection where $$$k_{w}$$$ has significant reduction.
Multiomics analysis of the gut-microbiota-brain axis shows neural signatures of Crohn’s disease and its underlying mechanisms
Authors: Zhang Ruonan , WANG YANGDI , Shen Xiaodi , Huang Li , Wang Mengzhu , Zhao Chen , Mao Ren , Feng Shi-ting , Li Xuehua
Published: November 2024

Motivation: Neural alterations affect intestinal conditions. However, these neural alterations and their potential formation mechanisms remain unclear. Goal(s): We integrated brain radiomics, the fecal microbiome, and blood metabolomics to investigate neural characteristics in patients with Crohn’s disease (CD) by establishing putative links between the gut microbiota, blood metabolites, and brain alterations. Approach: Multiomics data were compared between CD patients and healthy controls. Results: We developed a novel multiparameter brain MRI-based radiomics model to characterize the neural features of CD patients. Causal mediation analysis revealed significant pathways supporting the pivotal role of the gut-brain axis in neural alterations in CD patients. Impact: We developed a novel multiparameter MRI-based radiomics model to comprehensively characterize neural alterations in patients with Crohn’s disease. We presented biologically plausible evidence of the formation mechanism underlying these alterations from a gut-microbiota-brain axis perspective.
Unfolding evolving pathology in experimental cerebral malaria model treated with artesunate with in vivo MRI
Authors: Cui Min-Hui , Sachdeva Cheryl , Fleysher Roman , Branch Craig , Daily Johanna
Published: November 2024

Motivation: Artesunate is the first-line treatment of P. falciparum malaria. However, despite artesunate therapy, there is a 15% fatality rate in severe malaria and survivors can suffer long-term neurocognitive deficits. Goal(s): Describe the evolving pathology in experimental cerebral malaria (ECM) model treated with artesunate, to develop a model for testing adjunctive therapy in cerebral malaria. Approach: In vivo MRI was applied on ECM mice prior to and post artesunate treatment. Results: Despite rapid clearance of the parasite by artesunate, significantly reduced CBF and subsequent reperfusion/reoxygenation injury is detected by MRI, and may ultimately cause neurocognitive deficits in CM survivors. Impact: It is critical to understand the evolving pathology after antimalarial cure with artesunate in cerebral malaria in order to design effective adjunctive therapy, to reduce mortality and neurocognitive impairment. Reduced CBF and its rapid recovery may contribute to CM neuropathology.
Motion Correction with Combination of Disentangled Cycle-GAN and k-space Subsampling for the gadoxetic acid-enhanced liver MRI
Authors: Chen Gang , Rao Xinglong , Otikovs Martins , Lian Yang , Sun Peng , Zhou Xin , Liu Chaoyang , Bao Qingjia
Published: November 2024

Motivation: The gadoxetic acid-enhanced liver MRI is often accompanied by significant motion artifacts due to drug side effects. Goal(s): Motion correction by integrating Disentangled Cycle-GAN with the k-space Subsampling (DCGAN-kS) method. Approach: Convert motion correction to the image domain transfer problem resolved by DCGAN with the aid of the k-space subsampling strategy for reducing features and simplifying the domain transfer problem. Results: The method can effectively remove artifacts for the arterial phase imaging of the gadoxetic acid-enhanced liver MRI. Impact: The proposed scheme outperforms the other state-of-the-art methods for motion correction in the gadoxetic acid-enhanced liver MRI, which could enhance the image quality and reduce failed scanning.
Intra-Hippocampal Connectivity Differences in Cognitively Normal Subjects with/without Genetic Risk for Alzheimer’s Disease
Authors: Overson Devon , Hakhu Sasha , Beeman Scott , Song Allen , Truong Trong-Kha
Published: November 2024

Motivation: Microstructural changes within the hippocampus, which may occur well before cognitive decline in Alzheimer’s disease (AD), could serve as an early diagnosis biomarker in pre-symptomatic subjects.Goal(s): We assessed differences in intra-hippocampal connectivity between cognitively normal carriers and non-carriers of the APOE4 allele, a genetic risk factor for AD. Approach: We segmented the hippocampus into 12 subfields per hemisphere and performed fiber tractography on multi-shell diffusion tensor imaging data to determine the number of streamlines connecting each subfield pair. Results: 22 subfield pairs had a significantly lower connectivity in the APOE4 carrier group compared to the APOE4 non-carrier group. Impact: Our analysis shows differences in intra-hippocampal connectivity between cognitively normal subjects with and without a genetic risk factor for Alzheimer’s disease, which could potentially serve as a more definitive biomarker for its early diagnosis and treatment in pre-symptomatic subjects.
Sex Specific metabolism of Branched Chain Amino Acids and Relation with Cerebral Glutamate in Alzheimer’s Disease: A 1H-MRS Study
Authors: Soni Narayan Datt , Swain Anshuman , Juul Halvor , Reddy Ravinder
Published: November 2024

Motivation: Amyloid-β and NFTs are considered hallmarks of Alzheimer’s disease (AD) though are often challenged. While branched chain amino acids (BCAA) are known to play role in AD pathogenesis, their role is underexplored. A non-invasive method to study this relation could benefit devising alternate strategies for early AD diagnosis. Goal(s): To monitor sex-specific changes in BCAA levels and its relationship with glutamate. Approach: Localized-1H-MRS was performed in AD and WT mice. Results: Significant reduction in hippocampal BCAA and glutamate levels in male AD mice were observed, while females remained unaffected. BCAA and glutamate levels shown a strong correlation suggesting their close association in AD. Impact: Establishing a relation between BCAA metabolism and AD pathology could be beneficial for preclinical diagnosis as clear resonances of BCAA around 0.9 ppm on 1H MR spectra is achievable even at lower field strength MRI scanners.
Comprehensive evaluation of wrist kinematics using 3D real-time MRI
Authors: Tian Ye , Chaudhari Abhijit , Nayak Krishna
Published: November 2024

Motivation: A thorough understanding of wrist kinematics and kinetics is essential for diagnosing and treating wrist pain and instability. Current MRI methods are limited to tracking just a few thick slices and therefore use hand supports to restrict motion to pre-selected, simplified orientations. Goal(s): To develop a 3D real-time MRI method at 0.55T for comprehensive evaluation of wrist kinematics. Approach: A bSSFP 3D stack-of-spiral sequence with long spiral readout was implemented for evaluating wrist kinematics during maneuvers. Results: Comprehensive 3D coverage at 10 frames/second was achieved without any restriction on wrist motion, providing measurements of ligament intervals in 3D and during active wrist motion. Impact: The proposed 3D real-time method can potentially improve diagnosis of wrist injury and dysfunction and treatment planning, by providing a unique 3D evaluation of wrist kinematics and kinetics during the performance of clinically-important maneuvers involved in activities of daily living.
Neural Shape Models Meaningfully Localize Features Relevant to Osteoarthritis Disease: Data from the Osteoarthritis Initiative
Authors: Gatti Anthony , Blankemeier Louis , Van Veen Dave , Hargreaves Brian , Delp Scott , Kogan Feliks , Gold Garry , Chaudhari Akshay
Published: November 2024

Motivation: Osteoarthritis is a whole joint disease that requires quantification, localization, and visualization of disease related features of bones and cartilage. Goal(s): To develop a novel neural shape model (NSM) that can encode and reconstruct bone and cartilage shape, while quantifying localized features of OA. Approach: We trained a NSM on 6,325 knees and compared its reconstructions to a conventional statistical shape model and its ability to predict localized disease to a convolutional neural network. Results: The NSM reconstructed tissues with cartilage thickness correlations >0.993. NSM representations accurately diagnosed OA and predicted localized severity of osteophytes and cartilage defects better than a CNN. Impact: Our NSM can reconstruct whole bone and cartilage morphology, while encoding localized pathology specific information. Research use of the NSM can unlock novel insights into OA pathophysiology. Clinical deployment would enable automated insights into whole joint health.
Temporal response of T2 and adiabatic T1ρ and T2ρ relaxation times to acute ischemic injury to the femoral head: an in vivo piglet model study
Authors: Johnson Casey , Buko Erick , Parvaze Suhail , Albrecht Douglas , Falck Alaina , Armstrong Alexandra , Laine Jennifer , Toth Ferenc
Published: November 2024

Motivation: Relaxation time mapping may be clinically useful to inform the severity of ischemic injury to bone marrow in osteonecrosis of the femoral head. Goal(s): To compare the temporal response of T2, adiabatic T1ρ, and adiabatic T2ρ mapping to acute ischemic injury to the femoral head. Approach: 24 piglets were imaged using 3D relaxation time mapping at 3T MRI before and after surgical induction of unilateral femoral head ischemia. Corresponding cellular changes were evaluated histologically. Results: T2 and adiabatic T2ρ were the most sensitive in detecting acute injury to the femoral head, as early as 24 hours after onset of ischemia. Impact: T2 and/or adiabatic T2ρ relaxation time mapping can potentially address a clinical need for a non-contrast-enhanced imaging technique to establish the severity and extent of bone marrow necrosis in the earliest stages of osteonecrosis of the femoral head.
Simultaneous Multiparametric Quantitative MRI for Abdominal Imaging in a Single Breath-hold with MRF-EPTI
Authors: Cui Di , Wang Zhen , Li Yan , Xu Duan , Larson Peder , Liu Xiaoxi
Published: November 2024

Motivation: Multiparametric mapping is essential in the assessment of renal physiology and physiopathology, while the conventional quantitative MR methods requires long acquisition time and multiple breath-hold periods. Goal(s): To develop an efficient simultaneous multiparametric quantification method for abdomen imaging. Approach: MRF-EPTI technique was applied for quantitative abdomen imaging. The repeatability was evaluated by in-vivo scans on two healthy volunteers. Motion sensitivity was evaluated with/without breath-hold. Results: Simultaneous multiparametric quantification of T1, T2, T2*, proton density and quantitative susceptibility mapping including susceptibility source separation for abdominal imaging was achieved in a single breath-hold time (16s). Impact: We acquired simultaneous multiparametric quantification of T1, T2, T2*, proton density and quantitative susceptibility mapping including susceptibility source separation, and generated high-quality multi-contrast synthetic images for abdominal imaging in a single breath-hold with MRF-EPTI technique.
Quantification of renal oxygenation with field-insensitive T2-preparation and fast acquisition
Authors: Lin Zixuan , Jiang Dengrong , Zhang Yi , Hsu Yi-Cheng , Lu Hanzhang , Wu Dan
Published: November 2024

Motivation: T2-based method was recently extended to measure renal oxygen metabolism. However, MLEV T2-preparation can be less robust due to field inhomogeneity in abdomen. Goal(s): The goal is to propose a field-insensitive method for renal oxygenation quantification. Approach: An adiabetic T2-preparation (BIR-n) method was proposed. Simulation and phantom studies was performed to demonstrate the field-insensitivity. In-vivo experiements were conducted to examine the test-retest reproducibility. Results: A robust T2 quantification at different B0 and B1 offsets were demonstrated. In-vivo experiments showed a good test-retest reproducibility. Oxygenation was also found to be consistent between left and right renal veins, and showed significant correlation with blood flow. Impact: The proposed method has the potential to provide accurate and stable estimation of renal oxygen extraction and metabolism in a number of renal diseases.
Built-in RF safety for active implants: Harnessing impedance measurements from a commercial deep brain stimulator
Authors: Silemek Berk , Yalaz Mevlüt , Seifert Frank , Montag Reiner , Höft Michael , Ittermann Bernd , Winter Lukas
Published: November 2024

Motivation: MRI of neurostimulators is severely constrained due to RF safety concerns. Goal(s): Demonstrate that built-in sensors in commercial devices, such as a deep brain stimulator, can provide all necessary information to detect and improve RF safety. Approach: We investigated and utilized built-in impedance measurements of two commercial DBS systems for the detection and mitigation of RF-induced currents on the electrodes of a DBS lead. Results: Impedance measurements were correlated at various RF power levels. Temperature rise at the tip of DBS electrodes could be reduced to 0.02 K from 17.14 K at the same total powers (16.85±0.45 W). Impact: Our demonstration of mitigation of RF-induced heating in active implants through built-in sensor measurements from a commercial DBS system indicated up to ~850× improvement in temperature rise proving the unmet value of sensors for MR imaging patients with active implants.
Individual voxel models for head SAR estimation
Authors: Gabel Felix , Solomakha Georgiy , Bosch Dario , Glang Felix , Avdievich Nikolai , Scheffler Klaus , Bause Jonas
Published: November 2024

Motivation: Accurate human tissue models for simulation of RF power absorption are a key safety requirement for transmit coil development especially at ultra-high field. Goal(s): To create individual voxel models of the human head and torso. Approach: A pipeline for head and torso segmentation was developed based on a 3T multi-contrast protocol and tailored post-processing. The resulting voxel models were used for electromagnetic simulation of a self-developed Tx array at 9.4T. Results: Strong agreement was found between measured and simulated B1+ maps using the generated voxel model. Simulated worst-case SAR distributions differed significantly between individual and ‘off-the-shelf’ voxel models. Impact: We present a pipeline for the creation of individual human tissue voxel models covering head and torso, which is based on multi-contrast MR image segmentation. This meets a central need in safety-related simulations of ultra-high field RF coil arrays.
Dynamics of Respiratory Motion in Slow Flow Measurement using Simultaneous Coherent and Incoherent Motion Imaging Method
Authors: Ajala Afis , Jansen Isabelle Heukensfeldt , Lee Seung-Kyun , Abad Nastaren , Foo Thomas , DeMarco J Kevin , Shih Robert , Kohls Gail , Morris H Doug , Pollatou Angeliki , Yalewayker Haymanot , Hood Maureen , Skeete Sonja , Metzger Elizabeth , Ho Vincent , Werner J Kent , Marinelli Luca
Published: November 2024

Motivation: The impact of respiration on fluid flow in brain parenchyma is poorly understood and remains an on-going research topic in MRI velocimetry. Goal(s): To analyze the sensitivity of the simultaneous coherent and incoherent motion imaging (SCIMI) method to respiratory-induced phase in the brain parenchyma and to regress this phase contribution from the underlying slow-flow-induced phase. Approach: Prospectively cardiac-gated SCIMI acquisitions were obtained during three different breathing schemes. Results: Existence of strong correlations (p<0.005) between the breathing profiles and measured phase in various brain regions indicated the presence of respiration-induced phase in the SCIMI acquisition, and regression of such phases showed promising results. Impact: This study showed the sensitivity of simultaneous coherent and incoherent motion imaging method to respiration-induced phase in the brain, and an initial attempt to regress such phase accrual from the desired brain slow flows$$$–$$$an important biomarker of glymphatic function.
Diffusion Modeling with Unrolled Transformers for Self-Supervised MRI Reconstruction
Authors: Korkmaz Yilmaz , Patel Vishal , Cukur Tolga
Published: November 2024

Motivation: Diffusion models can reconstruct high-quality MR images, but their training neglects physical constraints and requires supervision via ground-truth images derived from fully-sampled acquisitions. Goal(s): Our goal was to devise a diffusion-based method that incorporates physical constraints and that can be trained using undersampled acquisitions. Approach: We introduced a novel diffusion model (SSDiffRecon) based on a physics-driven unrolled transformer architecture; and self-supervised training was achieved by predicting held-out subsets of acquired k-space data from remaining subsets. Results: SSDiffRecon achieved superior reconstructions to alternative self-supervised methods, and performed on par with a supervised benchmark trained on fully-sampled acquisitions. Impact: The improvement in image quality and acquisition speed through SSDiffRecon, combined with the ability to train on undersampled acquisitions, may facilitate adoption of AI-based reconstruction for comprehensive MRI exams in many applications, particularly in pediatric and elderly populations.
MOST: MR reconstruction Optimization for multiple downStream Tasks via continual learning
Authors: Jeong Hwihun , Chun Se Young , Lee Jongho
Published: November 2024

Motivation: This research aims to address the problem of performance degradation when a reconstruction network and a downstream network are cascaded. The proposed solution, MOST, optimizes a MR reconstruction network for multiple downstream tasks. Goal(s): Our objective is to sequentially finetune a reconstruction network using losses from multiple downstream tasks while preventing catastrophic forgetting such that the same reconstruction network can be used for the multiple tasks. Approach: We introduce replay-based continual learning into finetuning for multiple downstream tasks. Results: Our method successfully circumvents catastrophic forgetting, exhibiting stable performance across all downstream tasks, enabling a single reconstruction network to be used for multiple tasks. Impact: When k-space reconstruction and downstream tasks are performed using two separate networks (individually optimized), the cascade may introduce suboptimal results. Here, we propose a solution when multiple downsteam tasks exist, addressing challenges in realistic user environment.
Multidimensional MR Spatiospectral Reconstruction Integrating Subspace Modeling and a Plug&Play Denoiser with Recurrent Features
Authors: Zhao Ruiyang , Wang Zepeng , Lam Fan
Published: November 2024

Motivation: Multidimensional MR spatiospectral imaging (MD-MRSI) has many applications but is challenging due to high dimensionality and limited SNR. Subspace and learning-based methods have both demonstrated success. Goal(s): To develop a new MD-MRSI reconstruction method synergizing subspace modeling and a spatiospectral denoiser that can be ‘pre-learned’ without noisy/clean image pairs. Approach: A self-supervised training strategy was proposed to learn a network-based denoiser combining convolutional, fully-connected, and recurrent features and effectively exploiting multidimensional “correlations”. A plug-and-play ADMM-based algorithm was used to integrate the denoising prior and subspace reconstruction. Results: Impressive SNR-enhancing reconstruction was demonstrated using simulations and in vivo data from different MD-MRSI acquisitions. Impact: A new approach is proposed for multidimensional MR spatiospectral image reconstruction integrating low-dimensional modeling and a prelearned denoiser trained via multidimensional interpolation using only noisy data. Potential impacts on quantitative molecular imaging are demonstrated using different MRSI acquisitions.
Compressibility-Based Unsupervised Loss for Physics-Driven MRI Reconstruction Networks
Authors: Alcalar Yasar , Gulle Merve , Akçakaya Mehmet
Published: November 2024

Motivation: Alternative unsupervised training methods are needed for training physics-driven deep learning reconstruction without fully-sampled data. Goal(s): We propose a novel loss formulation, inspired by compressibility, to evaluate reconstruction quality in supervised, unsupervised and zero-shot settings. Approach: We leverage reweighted $$$\ell_1$$$-norm, which corresponds to $$$\ell_0$$$-norm of a sparse signal, to evaluate reconstruction quality. In supervised setting, reference weights are used for reweighting, while in unsupervised case, they are updated after each reweighting. Results: Our findings demonstrate that the networks trained with this loss outperform conventional compressed sensing, while performing similarly to deep learning methods trained using established supervised and unsupervised techniques. Impact: This work proposes an alternative compressibility-inspired loss formulation that is applicable to supervised, unsupervised and zero-shot learning problems for the training of physics-driven reconstruction neural networks. This approach utilizes compressibility and convexity for learning.
Retrospective Motion Correction for Fetal 4D Flow MRI
Authors: Tompkins Reagan , Fujiwara Takashi , Schrauben Eric , Browne Lorna , van Schuppen Joost , Clur Sally-Ann , Friesen Richard , Englund Erin , van Ooij Pim , Barker Alex
Published: November 2024

Motivation: Maternal breathing and fetal bulk motion frequently limit the utility of fetal 4D flow MRI. Goal(s): To demonstrate the effects of maternal respiratory and fetal bulk motion correction on 4D flow MRI Approach: Prospective undersampled fetal 4D flow data were acquired in two subjects, followed by compressed sensing reconstruction that included maternal respiratory gating and bulk motion correction. Standard SENSE-accelerated 4D flow acquisitions without motion correction (N=22) provided reference for the ability to quantify flow. Results: Comparisons of the motion corrected data to normative performance illustrate the technique’s potential for mitigating motion in fetal 4D flow, with equivalence to standard SENSE accelerated scans. Impact: The proposed sequence and flexible reconstruction workflow provide motion robustness for fetal 4D flow MRI. Further exploration of motion correction techniques has potential to enhance spatial and temporal resolution and to mitigate motion-related errors over extended scanning durations.
Development of a cross-modality tissue-mimicking and anatomic mimicking Fetal Phantom to improve image based fetus assessment
Authors: Hattat Remi , Zambrano Mariela , He Zhongzheng , Bozec Erwan , Ngremmadji Mbaimou Auxence , Beaumont Marine , Morel Olivier , Grandjean Gaëlle Ambroise , Chen Bailiang
Published: November 2024

Motivation: Fetal MRI necessitates extra care due to inherent safety concerns, complicating the optimization of MRI protocols and calibration with ultrasound assessments. Goal(s): To mitigate the aforementioned safety problems, we aim to create a cross-modality, tissue-mimicking, anatomically correct fetal phantom. Approach: We designed a fetus phantom encapsulated within a natural rubber balloon filled with mimicking amniotic fluid, creating an amniotic sac analogue. This design utilized varying compositions of agarose, gadolinium, and gelatin to replicate different fetal organs with distinct T1 and T2 values. Results: The developed phantom comprises various fetal organs and relaxation times, enabling precise fetal biometric comparisons between MRI and ultrasound modalities. Impact: With the help of the developped cross-modality, tissue-mimicking and anatomical mimicking fetus phantom, the aforementioned constraints on fetal MR protocol optimization and calibration between different MR and ultrasound should be relaxed.
Quantitative $$$T_{2}^{*}$$$ and $$$B_{0}$$$ Mapping of Fetal Brain Using Stack-of-Star Multi-Echo FLASH and Model-Based Reconstruction
Authors: Wang Xiaoqing , Wang Jian , Afacan Onur , Vasylechko Serge , Warfield Simon , Gholipour Ali
Published: November 2024

Motivation: Quantitative $$$T_{2}^{*}$$$ and susceptibility mapping is of great interest for fetal MRI. While conventional EPI-based approaches are efficient, they usually suffer from motion and field distortion artifacts. Goal(s): To develop a distortion-free and motion-robust quantitative $$$T_{2}^{*}$$$ and susceptibility mapping approach for fetal brain. Approach: A stack-of-star multi-echo FLASH sequence and model-based reconstruction were developed for quantitative mapping of $$$T_{2}^{*}$$$ and $$$B_{0}$$$ of fetal brain. Motion estimation and correction is incorporated into the reconstruction to reduce motion artifacts. Results: Initial findings indicate accurate $$$T_{2}^{*}$$$ measurements. Motion-corrected image reconstruction effectively minimized motion artifacts. Fetal $$$T_{2}^{*}$$$ and $$$B_{0}$$$ maps are obtained with reasonable quantitative $$$T_{2}^{*}$$$ values. Impact: Our technique enables distortion-free and motion-robust quantitative $$$T_{2}^{*}$$$ and $$$B_{0}$$$ mapping for the fetal brain, utilizing a stack-of-star multi-echo acquisition and model-based reconstruction. It has the potential to address motion and field distortion artifacts typically encountered in EPI-based methods.
Normal subcortical nucleiand and cortex Growth ，and Lateral Asymmetries at Fetal Brain MRI
Authors: Songhong Yue , Jie Li , Xiao Ling , Weihao Zheng , Jing Zhang
Published: November 2024

Motivation: The subcortical nuclei have important brain connectivity, which are rarely studied in fetal brain development. Goal(s): The aim of this study is to analyze the developmental characteristics of fetal subcortical nuclei and their relationship with gestational age and cortical development. Approach: The subcortical nuclei and cortical volumes were manually segmented from the three-dimensional (3D) volume parameters of tomosynthesis to volume reconstruction (SVR) imagesResults: We found a good linear relationship between subcortical nuclei and gestational age, with agreement on the left and right sides. In addition, we found good coupling between the subcortical nuclei and right cerebral cortex development. Impact: 3D Volumetric MR to assess the developmental characteristics of fetal subcortical nuclei and their relationship with gestational age. The normative values of fetal intracranial structures across a range of gestations could e used as a reference tool in prenatal counseling.
Improved Quantitative Diffusion in Fetal Lungs with Multiecho EPI Distortion and Motion Correction
Authors: Timms Liam , Utkur Mustafa , Gholipour Ali , Didier Ryne , Shamshirsaz Alireza , Kurugol Sila , Afacan Onur
Published: November 2024

Motivation: Accurately assessing fetal lung maturity and development with quantitative diffusion parameters could guide critical care for at-risk fetuses, but has been severely limited by artifacts. Goal(s): Develop an MRI technique to enable reliable diffusion imaging of fetal lungs. Approach: Investigated a multi-echo spin echo sequence to provide motion/distortion correction. Results: The technique improved lung analysis, in particular, resulting in more consistent ADC fitting. The method also increased the geometric fidelity of the diffusion image with structural images. Impact: This work demonstrates a novel MRI technique to enable reliable diffusion imaging of fetal lungs, overcoming current barriers of motion and artifacts. Improving lung maturity assessment during pregnancy has the potential to transform care for at-risk fetuses through earlier interventions.
Functional connectivity of motor resting-state networks in infants who are HIV-exposed uninfected in a South African birth cohort study
Authors: Williams Simone , Madzime Joanah , Zieff Michal , Bouke Niall , Davel Lauren , Bradford Layla , Samuels Reese , Jacobs Chloë , Williams Sadeeka , Mlandu Nwabisa , Pan Tracy , Madi Zamazimba , Mazubane Thandeka , Mhlakwaphalwa Tembeka , Nkubungu Khanyisa , Methola Bokang , Miles Marlie , Ringshaw Jessica , Alexander Daniel , Jones Derek , Williams Steven , Donald Kirsten
Published: November 2024

Motivation: Children who are HIV-exposed uninfected present with an increased risk of adverse motor developmental outcomes. Little is known about the pathophysiological mechanisms governing these outcomes. Goal(s): This study aimed to investigate functional connectivity within the motor resting-state network in infants who are HIV-exposed uninfected using resting-state functional MRI. Approach: We used Group Independent Component Analysis to identify the motor resting-state network and multivariate linear regression was used to compare its functional connectivity between groups. Results: Infants who are HIV-exposed uninfected showed significant connectivity alterations in 26 connections within the motor resting-state network when compared to infants who are unexposed. Impact: Functional connectivity alterations observed in the motor resting-state network could be linked to adverse motor developmental outcomes in children who are HIV-exposed uninfected. Future research will look at associations between functional connectivity of motor resting state network and motor development.