bluuebunny/crossref_metadata_2025 huggingface dataset:
Reproducible Intramuscular Fat Quantification using Vendor-Independent Processing in a Multi-Site, Multi-Vendor Setting
Authors: Eck Brendan , Lartey Richard , Gaj Sibaji , Li Mei , Kim Jeehun , Zaylor William , Xie Dongxing , Winalski Carl , Harkins Kevin , Huston Laura , Robison Ryan , Obuchowski Nancy , Damon Bruce , Altahawi Faysal , Knopp Michael , Jones Morgan , Spindler Kurt , Li Xiaojuan
Published: November 2024

Motivation: Intramuscular fat is associated with muscle degeneration. Chemical shift-encoded MRI quantifies proton density fat fraction (PDFF), but multi-site, multi-vendor reproducibility for intramuscular assessment is scarcely reported. Goal(s): To evaluate the reproducibility of a vendor-independent thigh muscle PDFF quantification approach using multi-site, multi-vendor data and then assess PDFF in patients 10 years post-anterior cruciate ligament reconstruction (ACLR). Approach: Phantoms, traveling controls, and ACLR patients were scanned using five scanners (three sites, two vendors). A correction was developed to address image scaling variations. Results: Average absolute PDFF standard deviation was below 1% after correction. The ACLR patient cohort had elevated PDFF in operated leg hamstrings. Impact: Harmonized acquisition and vendor-independent processing with the proposed image scaling correction can provide reproducible thigh intramuscular proton density fat fraction across sites and vendors. This approach may characterize within-patient muscle changes, such as bilateral differences or potentially longitudinal assessment.
Smart-Uploader: an automatic tool for medical image classification and quality protocol adherence
Authors: Peña-Nogales Óscar , Neylon Evie , Boshkovski Tommy , Ramos Marc , Rodrigues Paulo , Prčkovska Vesna , Trivodaliev Kire
Published: November 2024

Motivation: Imaging biomarkers are becoming a cornerstone to increase throughput and efficiency of large clinical trials. However, the diversity of imaging modalities used to derive them creates complexity for both imaging protocols and image archiving systems. Goal(s): To develop a tool to automatically classify imaging modalities and assess their adherence to the predefined acquisition protocol. Approach: The combination of a few-shot learning classifier trained to classify image modalities according to their contrast characteristics and a deterministic heuristic approach based on the DICOM headers. Results: The proposed approach displays potential for automatic online image classification and identification of protocol deviations, increasing clinical trial operational efficiency. Impact: The proposed joint approach automatically classifies all medical imaging data and assesses its adherence to the predefined acquisition protocol. Consequently, it not only facilitates data management but also identifies protocol deviations increasing the operational efficiency of clinical trials.
SIPAS: A Comprehensive Susceptibility Imaging Process and Analysis Studio
Authors: Qiu Lichu , Bao Lijun
Published: November 2024

Motivation: Quantitative susceptibility mapping (QSM) presents great potential to monitor of neurodegenerative diseases. Goal(s): Our goal is to provide comprehensive pipelines for QSM research including reconstruction and analysis. Approach: This work elaborates on the Susceptibility Imaging Process and Analysis Studio (SIPAS) which offers multi-method options for each step with an abundant parameter tuning user-interface. Subsequent analysis of QSM maps is based on the statistical indicators of region-of-interest (ROI) which are delineated on SIPAS. Results: SIPAS can achieve complete QSM procedures and precise results. Several hospitals have tested SIPAS for QSM research on different organs such as the brain, kidney, and liver. Impact: Quantitative susceptibility mapping is a key means of neurodegenerative diagnosis. SIPAS may serve as a platform for obtaining and evaluating high-quality susceptibility maps, which can be an effective tool for doctors and institutions to conduct QSM studies.
Harmonizing Multi-Modality Biases in Infant Development Analysis with an Integrated MRI Data Processing Pipeline
Authors: Liu Feihong , Huang Jiawei , Guo Lianghu , Tang Haifeng , Cai Xinyi , Zhang Yajuan , Liu Jiameng , Hua Rui , Gu Jinchen , Tao Tianli , Huang Zhongrui , He Yichu , Cao Zehong , Wang Luoyu , Wen Xuyun , Chen Geng , Wang Fan , Lian Chunfeng , Shi Feng , Wang Qian , Feng Jun , Zhang Han , Shen Dinggang
Published: November 2024

Motivation: Understanding infant neurodevelopment is pivotal for unraveling the anatomical underpinnings of psychomotor and cognitive functions, as well as pinpointing the origins of various disorders. Goal(s): Introduce an integrated multi-modality MRI data processing pipeline tailored for infant development studies, with the goal of reliablly discerning relationship across brain anatomy and cognitive functions. Approach: Incorporating precise deep learning tools specifically designed for infant brain, structural, functional, diffusion MRI data can be accurately analyzed, w.r.t. surface attributes for group-level study and network attributes for individual-level study. Results: We introduce an integrated multi-modal infant MRI data processing pipeline toolkit with dedicated processing results. Impact: We introduce the first infant multi-modal atlas and parcellation map
NeuroLibre: Living MRI preprints with built-in support for code review
Authors: Karakuzu Agah , DuPre Elizabeth , Bermudez Patrick , Boudreau Mathieu , Harding Rachel , Poline Jean-Baptiste , Das Samir , Bellec Pierre , Stikov Nikola
Published: November 2024

Motivation: The ISMRM community is swiftly adopting data sharing and code review. While the advantages are clear, challenges persist in ensuring the quality and functionality of these shared resources. Goal(s): To establish a platform for simplifying technical (or code) reviews and generating open-source living preprints with interactive data apps (e.g., dashboards). Approach: We created NeuroLibre.org, offering dedicated cloud resources for hosting living preprints that combine narrative and executable content. Results: NeuroLibre has published 8 living preprints, covering a variety of MRI applications. Each preprint is registered as citable and online-executable content with DOI links to archived reproducibility objects (code, runtime, data). Impact: Our living preprints showcase how NeuroLibre helps reviewers interactively assess the quality and functionality of reproducibility objects effortlessly, bolstering the reproducibility of MRI publications. The ISMRM 2020 reproducibility challenge is our flagship example: https://doi.org/10.55458/neurolibre.00014
Evaluating MRE-Tract Integrity in HIV-CSVD Cohort: A Comprehensive Analysis with Functionally Defined Atlases and Neurocognitive Assessment
Authors: Faiyaz Abrar , Weber Miriam , Kabir Irteza Enan , Doyley Marvin , Sack Ingolf , Uddin Md Nasir , Schifitto Giovanni
Published: November 2024

Motivation: MR-Elastography and diffusion-MRI represents complementary modalities with mechanical-and-structural information of the brain. Structural-connectomes alone lack tract-integrity information. Tissue viscoelastic-measures can provide valuable insights for tract-integrity when combined with connectomes. Goal(s): To aid the studies with neurodegeneration(HIV/CSVD), an analytical approach to combine viscoelastic-measures with diffusion-tractography is proposed which shows promise in targeted-analysis of functionally-defined-networks. Approach: For 14 functionally-defined brain-networks, viscoelastic measures from MRE are mean-sampled with the dMRI derived tracts. Then, the significantly-affected viscoelastic alterations are studied in-accord with cognitive-changes. Results: In a cohort of HIV-CSVD, by MRE-Tract-Integrity analysis, we reported significantly affected network connectivity that follows the cognitive decline(p<0.05) in processing-speed and motor-skills. Impact: The MRE-Tract-Integrity analysis enables us to study the missing mechanical properties of the structural connections from diffusion-tractography. This will help researchers performing targeted cognitive performance analysis with brain connectivity aided with mechanical basis which is a prominent marker for neural-change.
Multilayer Metabolic Networks of Mesial Temporal Lobe Epilepsy: Insights from Simultaneous PET/MRSI
Authors: Huang Hui , Zhang Miao , Zhao Yibo , Jin Wen , Li Yudu , Cai Bingyang , Li Jiwei , Liang Zhi-Pei , Li Biao , Luo Jie
Published: November 2024

Motivation: How chronic epilepsy impacts the interplay between neuronal metabolites and inter-regional metabolic connectivity remains unclear. Goal(s): To identify neurometabolic imaging biomarkers for epilepsy progression using PET/MRSI. Approach: Forty-eight patients with drug-resistant mesial temporal lobe epilepsy and fifteen patients with extratemporal epilepsy underwent simultaneous high-resolution MRSI and FDG PET. Moderation effects of disease duration were evaluated for multiple brain regions; multilayer metabolic networks were constructed to investigate metabolic changes of NAA, FDG and their interplay. Results: We found disease duration moderated changes in the interplay between NAA and FDG. Metabolic networks form distinct modules in short duration and long duration groups. Impact: This is the first simultaneous PET/MRSI study to investigate multilayer metabolic network associated with disease duration of mTLE, which could offer a comprehensive view of neurometabolic profile, facilitating the exploration of imaging markers in epileptic lesion detection and disease progression.
Imaging brain metabolic alterations of multiple sclerosis using fast high-resolution 3D-CRT-MRSI at 7T
Authors: Niess Eva , Dal-Bianco Assunta , Hingerl Lukas , Strasser Bernhard , Lipka Alexandra , Niess Fabian , Motyka Stanislav , Petrova Anna , Hangel Gilbert , Rommer Paulus , Trattnig Siegfried , Bogner Wolfgang
Published: November 2024

Motivation: MR spectroscopy offers biomarkers like myo-inositol and N-acetylasparate that can better predict disability progression and clinical status in multiple sclerosis. However, traditional MR spectroscopic imaging faces challenges like limited resolution and lengthy acquisition times, hampering its clinical utility. Goal(s): This study aimed to assess a novel 7T 3D-concentric-ring-trajectory-readout MRSI, addressing these limitations, for reliable metabolic marker imaging in MS. Approach: Metabolic images were obtained from 26 MS patients and compared with 13 healthy controls. Results: Altered brain metabolism in MS was effectively visualized across a large brain volume, revealing significant differences in metabolite levels within normal-appearing white matter. Impact: We showcase extensive, high-resolution brain metabolic mapping of multiple sclerosis within a clinically viable timeframe. This can enhance disease monitoring and improve the assessment of treatment effectiveness.
Longitudinal quantitative MRI detects heterogeneity in response to radiation therapy within prostate cancer
Authors: Wang Yu-Feng , Tadimalla Sirisha , Holloway Lois , Thiruthaneeswaran Niluja , Turner Sandra , Sidhom Mark , Hayden Amy , Martin Jarad , Haworth Annette
Published: November 2024

Motivation: Tumour heterogeneity can result in a spatial variation in response to radiation. While quantitative MRI (qMRI) shows promise in monitoring treatment response in prostate cancer (PCa), there remains an untapped potential for spatial response mapping to detect radioresistant tumour sub-regions suitable for early salvage therapy. Goal(s): To assess the feasibility of treatment response mapping in a post-radiation therapy longitudinal qMRI PCa dataset. Approach: Longitudinal qMRI parameter maps of 16 PCa patients were analysed using voxel-wise and region of interest (ROI) approaches. Results: Voxel-wise analysis identified heterogeneity in treatment-related changes within PCa that would otherwise be concealed in ROI-based assessments. Impact: Voxel-wise analysis of longitudinal qMRI parameter maps can detect radiation treatment response in sub-volumes of prostate cancer. Reliable detection and localisation of early treatment response provides an opportunity for adaptive or salvage therapies of treatment-resistant tumours.
Fast water/fat T2 and PDFF mapping via multiple overlapping‑echo acquisition and deep learning reconstruction
Authors: Lin Qing , Chen Weikun , Kang Taishan , Chen Xinran , Lin Liangjie , Chen Zhong , Cai Shuhui , Cai Congbo
Published: November 2024

Motivation: Skeletal muscle inflammation/necrosis and fat infiltration are strong indicators of disease activity and progression in many neuromuscular disorders. They can be assessed by muscle T2 relaxometry and water-fat separation techniques, respectively. Goal(s): Develop a method for simultaneous water-fat separation and T2 quantification. Approach: The chemical-shift encoding multiple overlapping-echo detachment (CSE-MOLED) sequence was designed for MRI data acquisition, and synthetic data and deep learning were used for image reconstruction. Results: The experiments showed that accurate T2 maps of water and fat and proton density fat fraction maps (PDFF) can be fast and simultaneously acquired by CSE-MOLED. Impact: A new MRI method is proposed for fast and simultaneous T2 and PDFF mapping, which may help improve the clinical diagnosis of neuromuscular diseases.
Motion-Corrected Subspace Navigator Reconstruction for High Resolution Spiral First-pass Myocardial Perfusion Imaging at 3 Tesla
Authors: Chen Quan , Wang Junyu , Wang Xitong , Zhao Shen , Liu Sizhuo , Salerno Michael
Published: November 2024

Motivation: As perfusion images are typically acquired over 60 heart beats, respiratory motion is unavoidable. Motion compromises spatio-temporal reconstructions. Goal(s): Residual undersampling artifacts and contrast variation make deformation field estimation challenging. This work aims to get an accurate deformation field from the auxiliary reconstruction and incorporate it into the forward reconstruction model to improve perfusion images. Approach: To obtain high-quality images for motion estimation, the fixed-angle spiral navigator is used to extract temporal basis. The rigid and non-rigid motion corrections are jointly incorporated into the subspace reconstruction. Results: Motion-corrected whole-heart first-pass spiral myocardial perfusion imaging with a high resolution of 1.3 mm2 is achieved. Impact: The proposed navigator-guided subspace motion correction reconstruction pipeline substantially improves the image quality, sharpness, and alignment of the 1.3mm² high-resolution spiral myocardial perfusion imaging, benefiting voxel-wise perfusion quantification crucial for assessing ischemic heart disease.
Cardiac and respiratory motion extraction at 0.55T with high-amplitude Pilot Tone
Authors: Tasdelen Bilal , Yagiz Ecrin , Tian Ye , Nayak Krishna
Published: November 2024

Motivation: With Pilot Tone, it is challenging to extract weak modulations, specifically those related to cardiac motion, at lower B0 field strengths (<1.5T). Goal(s): To enable the use of cardiac Pilot Tone at low-fields (0.55T). Approach: We utilize high-amplitude pilot tone transmission in conjunction with interference mitigation to eliminate ensuing image artifacts. Results: We demonstrate robust extraction of cardiac pilot-tone signals at 0.55T. We demonstrate ability to track motion with real-time MRI, and demonstrate the ability to separate cardiac and respiratory phases with retrospective binning. Compared to ECG, the measured timing standard deviation was 36ms for Cartesian and 47ms for spiral acquisitions. Impact: This work makes it possible to extract cardiac motion from Pilot Tone at 0.55T, which was not possible before. Pilot Tone could potentially replace ECG gating, simplify the clinical workflow, and serve for scanners that do not employ ECG.
Cardiovascular MRI study of the relationship between right ventricular function and biventricular shape in repaired tetralogy of Fallot
Authors: Gusseva Maria , Hussain Tarique , Pickardt Thomas , Beerbaum Philipp , Sarikouch Samir , Latus Heiner , Greil Gerald , Chabiniok Radomir , Chapelle Dominque , Lamata Pablo
Published: November 2024

Motivation: In patients with repaired tetralogy of Fallot prolonged exposure to the right ventricular (RV) pressure and/or volume overload can affect the function of both RV and left ventricle (LV). Goal(s): We aimed to investigate the link between RV function and RV and LV shape variation. Approach: We combined biomechanical modeling (i.e., provides RV contractility) with statistical shape modeling to quantify function and shape interaction. Results: Higher values of RV contractility were associated with a compressed LV cavity with associated septal flattening. No meaningful relationship was identified between RV function and RV endocardial shape. Impact: The link between anatomy and function can be studied with unprecedented detail in rTOF by combining biomechanical and statistical modeling frameworks from MRI. Such an approach facilitates the discovery of new disease mechanisms.
Interventional Cardiac MR (iCMR)-based Myocardial Mechanics and Circulatory Flow Dynamics in Patients with Fontan Circulation
Authors: Dahshi Hamza , Reddy Surendranath , Divekar Abhay , Hussain Mohammad , Gusseva Maria
Published: November 2024

Motivation: The complexity of Fontan palliation in single-ventricle physiology prompts the need to understand the associated cardiovascular adaptations and their impact on ventricular function. Goal(s): The research aims to quantify the relationship between single-ventricle contractile function and flow dynamic efficiency within the total cavopulmonary connection. Approach: Utilizing Interventional Cardiac MR and a biomechanical heart model, this study analysed hemodynamic data from 11 post-Fontan procedure patients, correlating myocardial contractility with energy loss (EL) and clinical metrics. Results: The analysis showed that EL significantly predicts myocardial contractility and the max(dP/dt), offering insights into the mechanical implications of Fontan circulation, with potential applications in patient-specific intervention strategies. Impact: This study exemplifies the integration of patient-specific biomechanical modelling with clinical cardiac MRI and hemodynamic data to understand Fontan circulation, potentially providing a quantitative framework for personalized treatment strategies and improving the cardiovascular health of patients with congenital heart disease.
Trajectories of abdominal subcutaneous and visceral adipose tissue accumulation and cardiometabolic health in children: The GUSTO study
Authors: Sadananthan Suresh Anand , Gupta Varsha , Kway Yeshe , Thirumurugan Kashthuri , Tint Mya Thway , Michael Navin , Yap Fabian , Tan Kok Hian , Godfrey Keith , Gluckman Peter , Chong Yap Seng , Wang Dennis , Lee Yung Seng , Fortier Marielle , Eriksson Johan , Velan S. Sendhil
Published: November 2024

Motivation: Unravel the links between childhood abdominal fat distribution patterns and subsequent cardiometabolic risks aiming to provide effective strategies for preventing childhood obesity. Goal(s): To investigate the presence of distinct abdominal fat accumulation patterns during infancy and childhood in a multi-ethnic cohort, and their associations with cardiometabolic risk. Approach: Latent class growth mixture modeling was used to identify three trajectories (stable, slow acceleration, rapid acceleration) for deep subcutaneous (DSAT), superficial subcutaneous (SSAT), and visceral adipose tissues (VAT). Results: Compared to Chinese children, Indian children had higher odds of being in the rapid acceleration trajectory. All accelerated trajectories were associated with inflammatory marker, hsCRP. Impact: This research has the potential to impact public health by providing evidence-based insights into the relationships between abdominal fat distribution patterns and cardiometabolic health. Our findings may aid development of targeted interventions and strategies to mitigate long-term adverse cardiometabolic consequences.
Automatic segmentation of T2-weighted hyperintense lesions in spinal cord injury
Authors: Valosek Jan , Enamundram Naga , Bouthillier Maxime , Schading-Sassenhausen Simon , Farner Lynn , Pfyffer Dario , Smith Andrew , Weber Kenneth , Freund Patrick , Cohen-Adad Julien
Published: November 2024

Motivation: Morphometric analysis of the intramedullary lesion following spinal cord injury will assist in understanding the extent of the injury and choosing the best therapeutic strategy for rehabilitation. Goal(s): Our objective was to develop a deep learning-based tool for the segmentation of T2-weighted hyperintense spinal cord injury lesions. Approach: A nnUNet model was trained to segment both the spinal cord and lesions from two different datasets. Results: Compared to existing methods, our model achieved the best segmentation performance for both cord and lesions. The code/model is available on GitHub and will soon be part of the Spinal Cord Toolbox. Impact: Automatic segmentation of spinal cord injury lesions replaces the tedious process of manual annotation and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model generalizes across lesion etiologies (traumatic/ischemic), scanner manufacturers and heterogeneous image resolutions.
Serial neuromelanin MRI detects progression of depigmentation over 6 months in early Parkinson’s
Authors: Xing Yue , Pszczolkowski Stefan , Hayat Tayyib , Morgan Paul , Evans Jonathan , Tench Chris , Auer Dorothee
Published: November 2024

Motivation: The potential of neuromelanin-MRI in tracking disease progression is underexplored. Goal(s): We investigated the detectability of changes of contrast in substructures of the substantia nigra over 6 months. Approach: We compared changes of contrast between healthy controls and subjects with early PD. Results: Depigmentation progresses over 6 months in early PD, but not in healthy controls, advancing the use of neuromelanin-MRI as a potential marker of PD. Impact: Our study addresses the knowledge gap of neuromelanin-MRI as a potential progression marker of early PD, which will be particularly helpful in the context of future disease-modifying trials.
Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson’s disease
Authors: Shang Song'an , Dou Weiqiang , Ye Jing
Published: November 2024

Motivation: Parkinson’s disease (PD) pathologically involves regional impairments and network disturbances. However, its microstructural abnormalities remain to be further elucidated via an appropriate neuroimaging approach. Goal(s): We thus aimed to investigate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI). Approach: The intergroup difference and classification performance of global microstructural complexity were analyzed, respectively. The network disruptions were explored in terms of structural connectivity, network covariance and modular connectivity. Results: Our findings indicated that PD encountered globally impaired microstructural complexity, disturbed structural connectivity between basal ganglia and cortices, aberrant network covariance within the striatum and thalamus and altered modular connectivity. Impact: These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer insights into the neurodegenerative process.
Exploring the cerebellar cortical stripes in humans with 7T, motion-corrected, RF-shimmed MRI
Authors: Priovoulos Nikos , Caan Matthan , Brouwer Emma , Mejias Jorje , Bazin Pierre , Alkemade Anneke , van der Zwaag Wietske
Published: November 2024

Motivation: The cerebellar cortex is organized in stripe-like clusters, similar to the neocortical layers/columns. The cerebellar anatomical complexity and lack of non-invasive methods makes their detection in humans challenging. Goal(s): To determine if the human cerebellum shows stripe-like patterns as observed in animals. Approach: We employed high-resolution, motion-corrected, RF-shimmed, 7T MRI to construct detailed cerebellocortical surfaces. We examined the presence of stripes across fMRI paradigms, their relationship to macrovasculature and variability. We additionally used immunohistochemistry for validation. Results: We observed consistent stripe-like patterns in the human cerebellum. These patterns were not associated with macrovasculature and conformed with immunohistochemistry, indicating a neuronal origin. Impact: Cerebellar stripes are a widely-known functional-organization feature but unreported in humans. Here, we combine motion-corrected, 7T-(f)MRI and immunohistochemistry to demonstrate stripe-like patterns in humans. This may provide a new paradigm for cerebellar function, akin to the discoveries in neocortical layers.
Molecular-informed Functional Imaging of Working Memory Processes
Authors: Ferrari Asia , Moretto Manuela , Saviola Francesca , Tambalo Stefano , Dipasquale Ottavia , Jovicich Jorge
Published: November 2024

Motivation: Task-based fMRI studies highlighted the dorsolateral prefrontal cortex (dlPFC) involvement during working memory (WM) processes. However, BOLD fMRI indirectly estimates neural activity and lacks neuroreceptor specificity. Goal(s): We investigated inhibitory and excitatory receptor density influence on functional connectivity (FC) during varying WM loads. Approach: Using N-back fMRI tasks and Receptor-Enriched Analysis of Functional Connectivity by Targets (REACT), we assessed GABA-A and mGluR5 connectivity effects. Results: We found decreased GABA-A- and increased mGluR5-enriched FC with increasing WM load in networks involving the dlPFC, in line with fMRI and single-voxel MRS studies. Therefore, REACT is a promising tool bridging whole-brain molecular organization and FC. Impact: Our molecular-enriched fMRI analysis revealed how varying working memory load modulates functional connectivity related to the underlying neurotransmitters. This provides crucial information for a better understanding of the neural mechanisms underlying brain disorders like Alzheimer’s disease and Schizophrenia.
Real-time MR elastography of the brain in search of the fast viscoelastic response to functional activity
Authors: Schattenfroh Jakob , Herthum Helge , Anders Matthias , Warmuth Carsten , Pfeuffer Josef , Braun Jürgen , Sack Ingolf , Hetzer Stefan
Published: November 2024

Motivation: The need for advanced imaging techniques to precisely localize neuronal activity, overcoming the limitations of fMRI in terms of temporal resolution and direct measurement of neural activation. Goal(s): To determine how neural activity influences tissue stiffness by detecting viscoelastic changes associated with neuronal firing and hemodynamic responses. Approach: Using real-time MR elastography techniques to simultaneously measure both BOLD activation and viscoelastic changes in the brain during visual stimulation at two different time scales. Results: Distinct viscoelastic activation patterns strongly link neurovascular coupling and tissue stiffness. However, no rapid viscoelastic response related directly to the underlying neural activity was detected. Impact: Functional real-time MR elastography is sensitive to biomechanical property changes associated with the hemodynamic response to brain stimulation providing a valuable tool to study possible effects that occur on a subsecond timescale.
Interleaved TMS-fMRI Explains Variability in TMS Response
Authors: Vasileiadi Maria , Grosshagauer Sarah , Woletz Michael , Schuler Anna-lisa , Linhardt David , Williams Nolan , Windischberger Christian , Tik Martin
Published: November 2024

Motivation: TMS has become an invaluable asset in both research and clinical environments. However, variability in individual responses to TMS is a persistent issue, which limits its broader adoption. Goal(s): The integration of TMS with fMRI through interleaved paradigms is a promising strategy for gaining insights into the factors that underlie this response variability. Approach: Adopting an interleaved TMS-fMRI approach we explored the different factors of stimulation dose, sex differences, and cognitive state. Results: Interleaved TMS-fMRI revealed individual dose-response patterns. Inherent sex differences were found between men and women. Precise timing of TMS relative to cognitive state demonstrated differential effects on relevant brain regions. Impact: The findings represent a critical step toward addressing the challenge of response variability. TMS-fMRI promises to be a valuable tool for not only understanding factors that influence TMS response but also for potentially enhancing response rates in TMS applications.
Unsupervised q-Space Interpolation Using Physics-Constrained Coordinate-Based Implicit Network
Authors: Topcu Atakan , Alkilani Abdallah Zaid , Çukur Tolga , Saritas Emine Ulku
Published: November 2024

Motivation: Most diffusion MRI techniques require extensive sampling of q-space to effectively resolve fiber structures at a fine detail. The scan times become impractically long, especially for clinical settings. Goal(s): Our goal is to arbitrarily interpolate the q-space data to enable downsampling of q-space, while maintaining high fidelity diffusion metrics. Approach: We propose QUCCI, a subject-specific unsupervised implicit network model that utilizes both implicit and physics-driven explicit regularization to encode diffusion MRI signals with angular continuity. Results: QUCCI achieves superior q-space interpolation, outperforming traditional and deep learning methods. Impact: QUCCI provides high-fidelity diffusion MRI metrics via improving the angular interpolation of diffusion MRI signals under highly undersampled q-space cases, which may especially be beneficial in the clinical settings where excessively long scan times are impractical.
A Novel End-to-end Joint Reconstruction and Segmentation Interaction Network for MRI
Authors: Li Xiaodi , Hu Yue
Published: November 2024

Motivation: For magnetic resonance imaging (MRI) applications, rapid imaging and automatic segmentation of target tissues are critical. However, most existing methods barely consider MR image segmentation in fast imaging scenarios. Goal(s): Our goal is to simultaneously achieve high scanning acceleration and accurate multi-class tissue segmentation results under a unified framework. Approach: We propose a novel multi-task method with a novel interaction module to reconstruct undersampled MR images based on modified ISTA-Net and simultaneously segment tissues based on lightweight U-Net. Results: Experiments on cardiac and knee datasets demonstrate that our method outperforms existing state-of-the-art multi-task approaches for joint MR image reconstruction and segmentation. Impact: The proposed multi-task interaction method can effectively achieve high scanning acceleration and accurate segmentation results simultaneously, which can further expand the application of MR in clinical disease diagnosis.
Accelerate 3D Coronary Magnetic Resonance Angiography by De-Aliasing Regularization based Compressed Sensing (DARCS)
Authors: Xue Zhihao , Yang Fan , Gao Juan , Chen Zhuo , Peng Hao , Zou Chao , Hu Chenxi
Published: November 2024

Motivation: While classical non-learning reconstruction methods for 3D coronary magnetic resonance angiography (CMRA) lack a task-adaptive image prior, 3D deep unrolling suffers from a low memory efficiency, causing a reduced number of iterations and a compromised image quality. Goal(s): We aim to combine compressed sensing and deep learning regularization by using a trained de-aliasing network as the sparsifying transform. Approach: We compared the method with PROST, Plug-and-Play, DAGAN, and MoDL for accelerating CMRA in 20 healthy subjects. Results: Visual inspections and quantitative comparisons both found a substantially improved reconstruction quality from DARCS relative to the other methods. Impact: The proposed method overcomes an important limitation of 3D unrolling while maintaining its core advantage of task-adaptive regularization. The method not only can accelerate 3D CMRA, but also has the potential for general 3D image reconstructions.
A precise location comparison of SOX2 positive glioma invasion and conventional MP-MRI signatures at autopsy
Authors: Bobholz Samuel , Winiarz Aleksandra , Lowman Allison , Flatley Michael , Duenweg Savannah , Nath Biprojit , Kyereme Fitzgerald , Connelly Jennifer , Coss Dylan , Krucoff Max , Banerjee Anjishnu , LaViolette Peter
Published: November 2024

Motivation: SOX2 is a marker of pluripotency that highlights tumor invasion beyond gross histological signatures, but the imaging characteristics of pluripotent tumor areas are unknown. Goal(s): Do conventional imaging signatures deliniate SOX2 positive staining tumor cell regions?Approach: This study compared imaging data from 22 glioma patients to aligned SOX2 autopsy tissue samples. Results: SOX2-positive regions were often present beyond the imaging-defined tumor mass, with a positive but not collinear association between SOX2 and cell density across most. However, MR intensity distributions did not effectively distinguish SOX2 positivity. Impact: These results highlight a novel signature of tumor presence that exists well-beyond the imaging-defined margin and is not readily detectable via conventional imaging. These areas are spared treatment and require new technological developments to detect non-invasively.
Relaxation-compensated CEST imaging of the APT can predict response to radiotherapy and progression-free survival in patients with glioma at 3T
Authors: von Knebel Doeberitz Nikolaus , Kroh Florian , Graß Svenja , König Laila , Bauspieß Cora , Boyd Philip , Debus Jürgen , Bachert Peter , Ladd Mark , Schlemmer Heinz-Peter , Korzowski Andreas , Paech Daniel
Published: November 2024

Motivation: There is a scarcity of studies comparing the clinical value of asymmetry- and different Lorentzian-fit-based CEST contrasts of the amide proton transfer (APT) and semi-solid magnetization transfer (ssMT) in patients with glioma. Goal(s): To assess and compare the potential of asymmetry-based (APTwasym) and Lorentzian-fit-based CEST imaging with (MTRRexAPT and MTRRexMT) and without (MTconst) relaxation compensation for the prediction of therapy response and survival in patients with glioma. Approach: 78 study participants prospectively underwent CEST MRI at baseline before radiotherapy. Results: Imaging of the MTRRexAPT and MTRRexMT predicted response to radiotherapy, whilst the MTRRexAPT was also associated with progression-free survival. Impact: Here we demonstrate for the first time that Lorentzian-fit-based CEST imaging of the APT and ssMT with relaxation compensation can predict therapy response and progression-free survival of patients with glioma at baseline before radiotherapy, at 3T.
Explaining Deep fMRI Classifiers with Diffusion-Driven Counterfactual Generation
Authors: Bedel Hasan , Çukur Tolga
Published: November 2024

Motivation: Deep-learning classifiers for functional MRI (fMRI) offer state-of-the-art performance in detection of cognitive states from BOLD responses, but their black-box nature hinders interpretation of results. Goal(s): Our goal was to devise a reliable method to infer the important BOLD-response attributes that drive the decisions of deep fMRI classifiers. Approach: We introduced a novel counterfactual explanation method (DreaMR) based on a new fractional, distilled diffusion prior for efficient generation of high-fidelity counterfactual samples. Results: DreaMR generated more specific and plausible explanations of deep fMRI classifiers trained for resting-state and task-based fMRI analysis than previous state-of-the-art explanation methods. Impact: The improvement in sensitivity, plausability and efficiency in explanation of deep classifiers through DreaMR may facilitate adoption of AI-based analyses in fMRI studies, thereby benefiting assessment of cognitive processes in both normal and neurological disease states.
AI-based motion estimation in k-space using guidance lines enables scoutless prospective motion correction.
Authors: Hossbach Julian , Splitthoff Daniel , Clifford Bryan , Polak Daniel , Lo Wei-Ching , Cauley Stephen , Kober Tobias , Lang Min , Tabari Azadeh , Ford Jeremy , Manzoor Komal , Wald Lawrence , Rapalino Otto , Schaefer Pamela , Conklin John , Huang Susie , Meyer Heiko , Maier Andreas
Published: November 2024

Motivation: Novel research reduced the acquisition of motion navigators to a few guidance lines. A prospective correction is not yet possible due to reconstruction and optimization times. Goal(s): We determine the feasibility of a fast AI-based motion estimation for prospective correction in a 3D MPRAGE research sequence. Approach: A DL network to prospectively estimate the head pose from seamlessly integrated guidance lines in a 3D MPRAGE research sequence was trained on simulated data and used for a rapid adaption of the FOV to achieve a prospective correction. Results: In-vivo experiments showed greatly reduced motion artifacts. The motion estimation is accurate and stable. Impact: Prospectively adapting the FOV using the proposed AI-based method greatly improves the image quality of 3D MPRAGE acquisitions. This unique application of ML enables promising research of prospectively mitigating motion artifacts with minimal changes to the sequence.
High temporal resolution motion correction in MRF using quantitative-scout-based navigation (QUEEN) and motion-dictionary matching.
Authors: Nurdinova Aizada , Cao Xiaozhi , Oscanoa Julio , Abraham Daniel , Wang Nan , Setsompop Kawin
Published: November 2024

Motivation: Motion correction in MRF using navigators in sequence deadtime improves imaging robustness, however, temporal resolution of the approaches is limited to 6-10 s. Goal(s): We aim to achieve accurate motion tracking at 0.5 s temporal resolution, by integrating the QUantitatively-Enhanced parameter Estimation from Navigators (QUEEN) approach into MRF. Approach: Compact navigators were inserted throughout the MRF acquisition at a minimal encoding efficiency reduction of ~5%. The acquisition of the quantitative scout was integrated into the MRF’s dummy scan, resulting in no added scantime. Results: The estimated in vivo motion parameters have MAE of 0.4 mm and 0.2 deg compared to image registration estimates. Impact: The proposed method can achieve high temporal resolution motion estimates, and therefore, is a promising approach for high-precision motion correction in MRF.
Deep Learning Algorithm for Prediction of Molecular Subtypes and Grades in Adult-type Diffuse Gliomas: According to the 2021 WHO Updates
Authors: Byeon Yunsu , Park Yae Won , Lee Soohyun , Shin HyungSeob , Park Doohyun , Ahn Sung Soo , Lee Seung-Koo , Hwang Dosik
Published: November 2024

Motivation: Noninvasive prediction of molecular subtype and grade in adult-type diffuse gliomas based on 2021 WHO classification can aid in clinical practice. Goal(s): To establish a robust and interpretable deep learning model for molecular subtyping and grading in adult-type diffuse gliomas. Approach: Institutional multiparametric MRI data (n=1,053) were used to train deep learning models, including 2D CNN and Vision Transformer. Our models were externally validated on the TCGA dataset (n=200). Explainable AI methods were used to interpret the predictions of our models. Results: ViT outperformed CNN with AUCs of 0.87, 0.73, and 0.81 for prediction of IDH mutation, 1p/19q codeletion, and grading, respectively. Impact: Our study demonstrates that Vision Transformer provides reliable and interpretable prediction of molecular subtype and grades in adult-type diffuse gliomas based on the 2021 WHO classification using multiparametric MRI data.
Integration of Whole Brain Spectroscopic Imaging in Planning Workflow for Personalized Delivery of TTFields in Glioblastomas
Authors: de Godoy Laiz Laura , Rajan Arthich , Berger Brian , Sheriff Sulaiman , Sarkar Atom , Shah Rinku , Aregawi Dawit , Morrison Tara , Hosur Srilatha , Shah Sunjay , Shukla Gaurav , Lanciano Rachelle , Jain Varsha , Herbert Scott , Amankulor Nduka , Weiss Stephanie , Giri Anshu , Padmanabhan Aruna , Bagley Stephen , Lim Kheng Choon , de Paula Demetrius Ribeiro , Lee Demetrius , Vineis Kristina , Desiderio Lisa , Nasrallah MacLean , Mohan Suyash , Chawla Sanjeev
Published: November 2024

Motivation: Optimal treatment for GBM requires precise targeting of all viable tumor cells, many of which are not visible on conventional neuroimaging. Goal(s): We aimed to utilize WBSI to identify infiltrating tumor cells in GBM patients for selecting a precise target volume for personalized mapping of transducer arrays for enhanced delivery of TTFields. Approach: A mean value of choline/NAA was computed from normal mask, and all voxels that exceeded two-fold threshold value were included in a 3D-composite mask from the tumor region. Results: WBSI provided higher yield of voxels with good spectral quality, resulting in improved brain tumor coverage compared to anatomical MRI sequences. Impact: Alternative array configuration created from WBSI will allow precise delineation of tumor margins for enhanced delivery of TTFields dose to all proliferating regions of a GBM, decreasing the rate of local recurrence and ultimately improving overall survival.
Monitoring intranasal treatment to brain tumor using multiple CEST contrasts
Authors: Law Lok Hin , Su Haoyun , Liu Yang , Chan Kannie WY
Published: November 2024

Motivation: Theranostic application of intranasal drug delivery to glioblastoma using multiple CEST contrast.Goal(s): Our goal is to monitor the drug delivery to brain tumor and evaluate the treatment effect simultaneously. Approach: We investigated the imaging of liposome-based drug delivery to the brain tumor via intranasal administration,in which the amount of liposome and the tumor response can be detected by CEST MRI at 3T. Results: CEST contrast at 3.5ppm of tumor region and the tumor size comparison between treatment and control group could indicate the therapeutic effect.CEST contrast at 4.3and-3.5ppm from pre-injection to post-injection in-vivo,could indicate the liposome drug delivery efficacy and drug distribution. Impact: CEST MRI guided intranasal drug delivery could provide valuable information for assessing efficacy of drug delivery and treatment outcome. This can potentially translate to clinics as a non-invasive theranostic approach for glioblastoma treatment.
Initial evaluation of breast MRI protocols for cancer treatment monitoring at low field 0.55 T
Authors: Zimmermann Judith , Su Pan , Wilmes Lisa , Leon Pedro Itriago , Nickel Marcel , Li Wen , Joe Bonnie , Hylton Nola
Published: November 2024

Motivation: With increasing availability and technical advances of low field 0.55T MRI systems, it is important to understand their value for breast applications. Goal(s): To present preliminary data of breast MRI at 0.55T with a newly available dedicated 7-channel prone breast coil. Approach: Breast MRI at 0.55T and 3T with NIST-calibrated breast phantom and two healthy female volunteers using protocols that are clinically relevant for breast cancer treatment monitoring. Results: Preliminary 0.55T breast MRI data has been successfully generated with acceptable image quality and will initiate future studies with breast cancer patients to advance breast MRI with low field systems. Impact: This first acquisition of breast phantom and healthy volunteer data using sequences relevant in breast cancer treatment monitoring (T1-weighted, T2-weighted, diffusion-weighted) will initiate further, more detailed studies to explore the value of low field MRI for examining the breast.
Identification of pretreatment habitat signatures for the prediction of patient outcome in triple negative breast cancer
Authors: Kazerouni Anum , Kennedy Laura , Vinayak Shaveta , Dintzis Suzanne , Rahbar Habib , Partridge Savannah
Published: November 2024

Motivation: Triple negative breast cancer (TNBC) patients exhibit diverse response to therapy, with ~30% achieving pathological complete response (pCR). Goal(s): In this study, we seek to spatially-resolve heterogeneity of the tumor microenvironment using multiparametric MRI (mpMRI) in TNBC patients undergoing neoadjuvant chemotherapy to predict treatment outcomes. Approach: We employ habitat imaging, clustering mpMRI data to identify physiologically distinct tumor subregions, or habitats. Patients are then defined by tumor habitat composition and clustered to identify common habitat signatures. Associations between habitat signatures and patient outcomes are evaluated. Results: Clustering of patients yielded three habitat signatures with significantly different rates of pCR and recurrence-free survival. Impact: We demonstrate that tumor habitat signatures can differentiate triple-negative breast cancer patients prior to neoadjuvant chemotherapy, identifying those with improved treatment response and long-term outcomes. Clinical translation of this approach could enable patient stratification for therapy escalation/de-escalation and treatment optimization.
MRI advancement and research in Africa: Report on the Inaugural ISMRM African Chapter conference
Authors: Obungoloch Johnes , Adepoju Adesola , Samuels Petronella , Amadi Mary-Jane , Ayilara Segun , Kamuzora Mary , Dzefi-Tettey Klenam , Ikpeme Anthonia , Robertson Frances , Anazodo Udunna , Truwit Chip , Dako Farouk , Webb Andrew , Mensah Yaw , Asllani Iris , Meintjes Ernesta , Ogbole Godwin
Published: November 2024

Motivation: Due to rising non-communicable diseases, limited MRI accessibility, and Africa's underrepresentation in ISMRM, the African Chapter (AC) was founded in 2023. An inaugural conference in Ghana focused on emerging MRI technology for improved accessibility. Goal(s): To provide the inaugural conference report of AC-ISMRM, with the identification of challenges and barriers to MRI access and propose solutions toward democratization of MRI across Africa. Approach: A white paper approach was adoptedResults: Over 100 scientists from 12 African countries met to identify challenges and propose solutions for advancing MRI access and value in Africa. Low-field MRI was identified as a breakthrough innovation toward this goal. Impact: The AC-ISMRM conference marks a pioneer event, convening African scientists and clinicians, aimed at establishing a network dedicated to rectify Africa's underrepresentation in MRI research, seeking solutions to challenges on the continent and promoting collaboration and MRI advancements
Portable Ultra-Low Field MRI is Sensitive to Distinct Profiles in Brain Development of Malnutrition and Nutritional Intervention
Authors: Bruchhage Muriel , Zhou Hang , Zhou Yidong , Scheiene Daniel , Bourke Niall , O’Muircheartaigh Jonathan , Cole James , Bouchard Kristofer , Martin-Herz Susanne , Laleau Victoria , Flaherman Valerie , Deoni Sean , Müller Hans-Georg , Murungi Joan , Nankabirwa Victoria
Published: November 2024

Motivation: The first years are essential for a child’s development and adverse factors, including malnutrition, can affect neurodevelopment and survival rates. Access to high-field MRI scanners in Sub-Saharan Africa is highly limited. Goal(s): To detect distinct profiles in brain development of malnutrition and nutritional intervention using ultra-low field MRI. Approach: We used ultra-low field MRI in a pediatric cohort in Uganda of 71 infants (<1.5 years) with and without history of malnourishment, imaged before and after receiving an intervention. Results: Using PACE brain-for-age growth percentiles, we demonstrate that ultra-low field MRI is sensitive to distinct profiles in brain development of malnutrition and nutritional intervention. Impact: The distinct profiles of early malnourishment on neurodevelopment and their changes after nutritional intervention derived by ultra-low field MRI could allow for more appropriate neurodevelopmental burden estimates in LMIC pediatric populations and support early intervention evaluation.