Keywords: Software Tools, Tissue Characterization, Cardiac Diffusion Tensor Imaging, cDTI, Heart, Data Processing

Motivation: cDTI provides several new and useful MRI biomarkers, but robust and reliable data processing pipelines are still needed to adequately handle cDTI data.

Goal(s): Goal: To demonstrate the benefits of an open-source Python cDTI data processing toolbox and its impact on measurement accuracy and precision.

Approach: A direct averaging and tensor-fitting data processing technique was compared to our open-source data processing pipeline. Data from healthy subjects was used to demonstrate improvements in the accuracy and uncertainty of cDTI metrics.

Results: Our open-source cDTI data processing toolbox provides smoother parametric maps that are more accurate with less uncertainty compared to direct averaging and tensor-fitting.

Impact: Development of Cardiac Diffusion in Python (CarDpy), an open-source python toolbox for cardiac diffusion tensor imaging (cDTI) data processing to facilitate reproducible cDTI research for new and established researchers. A strong foundation, plus software modularity encourages contributions from the community.

Keywords: Muscle, Muscle, Reproducibility, fat, Dixon, post-processing, image processing, multi-site, multi-vendor, osteoarthritis, orthopaedics, ACL reconstruction

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.

Keywords: Software Tools, Software Tools

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.

Keywords: Software Tools, Software Tools, Vessel

Motivation: Aiming to unravel the complexities of intracranial arterial structures, we recognized the need for advanced 3D annotation capabilities to improve upon conventional 2D methodologies.

Goal(s): Our goal was to develop VesselVoyager, a tool that facilitates detailed 3D mapping and analysis of cerebral vasculature, filling a critical gap in neurovascular diagnostic technology.

Approach: We utilized real-time 3D rendering and a 3D virtual camera system in VesselVoyager, enabling precise, interactive annotation and analysis within a user-friendly, gamified environment.

Results: VesselVoyager not only improved accuracy but also created new approaches for analyzing understudied diseases, enhancing our understanding of complex neurovascular conditions.

Impact: VesselVoyager enhances centerline tracing precision and triples processing efficiency, thus lightening the workload for clinicians and researchers. This advancement broadens its applicability to intricate vascular pathologies, including moyamoya disease.

Keywords: Data Processing, Cancer, Clinical Translation

Motivation: Translation of quantitative biomarkers requires a reproducible and efficient analysis environment to underpin assessments of clinical utility. 

Goal(s): This study aimed to provide a 24-hour turnaround of clinically relevant MR-derived hypoxia maps for use in biopsy planning. 

Approach: The analysis framework was integrated into an XNAT imaging repository and applied to oxygen-enhanced and dynamic-contrast-enhanced images to quantify and map the extent of hypoxia within low-grade glioma.

Results: A group of geo-dispersed researchers consistently delivered MR-derived hypoxia maps to the clinical study team for use alongside ¹⁸F-FDOPA PET in biopsy site definition prior to surgery.

Impact: A framework of repository-integrated analysis enabled rapid turnaround of quantitative MR imaging biomarkers for clinical decision-making. Specifically, biopsy-planning using MR-derived hypoxia mapping of low-grade glioma was delivered with standardised, reproducible, auditable results in under 24 hours.

Keywords: Software Tools, Software Tools, Quantitative Suceptibility Mapping

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.

Keywords: Data Processing, Brain, Neuroimage computing，pipeline

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

Keywords: Software Tools, Software Tools

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

Keywords: Data Processing, Pediatric, Neuroimage computing, Auditory pathway, Normal development

Motivation: Charting the development of infant auditory system is vital for understanding language acquisition and hearing disorders. 

Goal(s): Extracting auditory fiber bundles from diffusion MRI data and overcoming the processing difficulties due to tiny and complex structures, as well as very low tissue contrast in the structural MRI data. 

Approach: We propose an NAT framework with three core processes: 1) constructing a high-resolution atlas, 2) segmenting tissues and regions of interest (ROIs), and 3) applying a hierarchical registration framework.

Results: A high-resolution auditory fiber bundle template is constructed, and 12 auditory fiber bundles are successfully extracted. 

Impact: Our approach is the first toolbox to identify individual auditory fiber bundles in infants, thus effectively mitigating the processing challenges caused by spatial-temporal asynchrony during the development of the first two postnatal years.

Keywords: Data Processing, Brain, data release

Motivation: Advances in MRI have increased our understanding of the human brain but are frequently limited by single modality study designs. Combining data from multiple modalities/MR contrasts can enhance our understanding of the complex multi-scale neural relationships that underpin human behaviour. 

Goal(s): Our goal was to create an open-access multi-scale, multi-modal imaging database of the healthy human brain.

Approach: The Welsh Advanced Neuroimaging Database (WAND) includes micro and macro-structural, functional and spectroscopic MRI, MEG and cognitive data from over 150 healthy volunteers.

Results: WAND is free, open-source, organised using the Brain Imaging Data Structure (BIDS), and now available for download. 

Impact: The Welsh Advanced Neuroimaging Database takes steps toward democratising magnetic resonance research by making multi-modal, multi-scale neuroimaging data freely and easily available, enhancing opportunities for collaboration and development of novel analysis techniques, further progressing the field of neuroimaging. 

Keywords: Software Tools, Data Processing, Data Analysis

Motivation: Robust evaluation of novel quantitative imaging biomarkers in multi-centre imaging trials requires harmonised workflow for storage, quality control and analysis of imaging data to facilitate clinical translation.

Goal(s): To establish a standardised, repository-integrated framework for quantitative MR data analysis to aid reproducibility.

Approach: A software container for quantitative MR data analysis was created using Docker, integrated with the XNAT imaging repository and demonstrated using example data.

Results: Repository-integrated software was used to generate quantitative maps of T₁, ADC and hypoxia, as well as DCE and IVIM modelling parameters in primary and nodal tumours from a patient with head-and-neck cancer.

Impact: Quantitative MR biomarker translation can be accelerated by standardisation of analysis protocols across multi-centre trials. Integration of containerised, user-configurable data analysis software within imaging repositories will improve repeatability and lower the barrier for entry to clinical trial involvement.

Keywords: Software Tools, Software Tools, brain asymmetry, rs-fMRI, DTI, connectome

Motivation:  Hemispheric asymmetries have shown a great potential for early detection of a variety of neurological disorders such as traumatic brain injury. However, the lack of consistency in study outcomes is a common issue, largely stemming from the restricted sample sizes and methodological variations.

Goal(s): To develop a unified framework for the processing of multimodal imaging data for a comprehensive evaluation of brain asymmetry.

Approach: Combination of state-of-the-art algorithms for multimodal imaging data to estimate different metrics of brain asymmetry.

Results: By providing comprehensive measurements of brain asymmetry, this framework has the potential for early detection and monitoring of neurological disease.

Impact: The study aims to address inconsistencies in hemispheric asymmetry research by developing a unified framework for the estimation of brain asymmetry and offers a promising avenue for early detection and monitoring of neurological diseases.

Keywords: Data Processing, Elastography

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.

Keywords: Data Processing, DSC & DCE Perfusion, Deep Learning

Motivation: There is a need for reproducibility, repeatability, and accuracy. Previously, OSIPI organized a challenge for benchmarking DCE software. As the use of artificial intelligence grows, we now set out to repeat the challenge, focusing on deep learning techniques.

Goal(s): To encourage researchers put their quantitative methods to test and stimulate collaboration and to charter the heterogeneity of DCE analysis software.

Approach: To use deep learning techniques to estimate perfusion parameters in DCE-MRI of the uterus. We share repeated in-vivo data to assess the algorithm’s precision, and simulated DCE-data to test the accuracy.

Results: Top three winners may present their method at ISMRM 2025.

Impact: As quantitative perfusion MRI receives more importance and attention, the need for reproducibility, repeatability, and accuracy is inevitable. A public challenge within the MRI community is a great way to highlight the quantification of DCE-MRI.

Keywords: Simulation/Validation, Simulations, Monte Carlo Simulation, Massively Parallel Simulation

Motivation: The software encompassed by this abstract, which we call simDRIFT, fulfills a presently unmet need by allowing for mesh-free Monte Carlo simulations of DWI that unify researchers' needs for computational performance and biophysical realism with easy-to-use and configurable open-source software. 

Goal(s): simDRIFT  aims to provides for rapid and flexible Monte-Carlo simulations of Pulsed Gradient Spin Echo (PGSE) Diffusion-Weighted Magnetic Resonance Imaging (DWI) experiments.

Approach: We compared the performance of simDRIFT against other  open-source DWI simulators on identical voxel geometries. 

Results: Our results show that simDRIFT achieves orders of magnitude improved performance relative to other simulators, especially for large resident spin ensemble sizes.

Impact: The performance gains achieved by simDRIFT support its aim to provide a customizable tool for the rapid prototyping of diffusion models, ground-truth model validation, and in silico phantom production.

Keywords: Software Tools, Brain, MRS, fitting, editing, GABA, 2HG

Motivation: Reproducible and accurate fitting of the MR spectrum is critical in estimating metabolite concentrations. We have previously developed a fitting software called ProFit-1D which was shown to fit 9.4T sLASER data from the human brain with high accuracy and precision.

Goal(s): Here, ProFit-1D was optimized for fitting 3T J-difference edited data. 

Approach: ProFit-1D was evaluated for accuracy and precision in simulated and in vivo 2HG-edited and GABA-edited data and compared to that of Gannet and LCModel

Results: ProFit-1D was found to be more accurate than LCModel in fitting the GABA-edited and 2HG-edited data and more precise than Gannet in fitting the GABA-edited spectra.

Impact: Here, we show that ProFit-1D produces more accurate measurements than LCModel and more precise measurements than Gannet in simulated and in vivo spectra from tumors and healthy participants. ProFit-1D is a promising fitting software with high clinical applicability.

Keywords: Software Tools, Low-Field MRI, Simulation, denoising, super-resolution

Motivation: Low-field MRI is increasingly being applied in lower- and middle-income countries, but due to limited resources, training is scare.

Goal(s): Provide an open-source plateform for simulation, teaching, and a denoising and super-resolution pieline of low-field MRI images.

Approach: Using 3D maps of relaxation times, proton density, B0 and low-field system-specific parameters, such as limited gradient linearity, simulation of low-field images are created. Application of bm4d denoising and AI super-resolution on low-field images is additionally proposed.

Results: We provide an open-source graphical interface that can simulate and generate multiple sequences of low-field MRI, and a denoised and super-resolution pipeline increasing low-field image enhancement.

Impact: This study provides a simple open-source python platform to simulate point-of-care low-field MRI images, reflecting specific system-specific parameters for teaching purposes, and a fast advanced denoising and AI-based super-resolution pipeline for low-field images.

Keywords: Radiomics, Radiomics

Motivation: Radiomic feature robustness as an input to a downstream model is an important consideration for model reliability. Generating a subset of robust, reproducible, and repeatable features is an important step in determining predictive or indicative features.

Goal(s): To provide a framework for radiomics robustness, repeatability, and reproducibility.

Approach: A Python implementation using the pyradiomics, scikit-learn, numpy, and other open-source library is provided as tool to quickly summarize the radiomic features surviving differing sampling, time point, or field strength acquisitions.

Results: The QRadAR Toolbox provides researchers and clinicians with a collection of reliable features for downstream model input.

Impact: The QRadAR Toolbox provides researchers and clinicians with a collection of reliable features for downstream model input by providing a providing Python a framework for radiomics robustness, repeatability, and reproducibility.

Keywords: Software Tools, MR Value

Motivation: A deeper understanding of how information in log files can contribute to service improvement, for example reducing patient backlog and improving patient safety. 

Goal(s): To construct an automated data pipeline that analyses and processes log files, returning quantitative results and graphical visualisations that can be interpreted in a clinically useful way. 

Approach: We obtained log files from our scanner fleet and constructed a Python codebase to read, analyse and interpret data pertaining to time utilisation and patient exposure.

Results: We have demonstrated the code’s capability to provide detailed exposure monitoring as well as insights into how productively we use time on our scanners.

Impact: Our work will help us to optimise the patient pathway, improve patient safety and investigate how more obscure parameters can affect our service delivery.  Our code will be open source, enabling others to benefit and contribute, improving services more widely.

Keywords: Data Processing, Data Processing, Optimization, Torch, Auto-differentiation.

Motivation: Addressing the time constraints in Magnetic Resonance Spectroscopy (MRS) metabolite quantification, TensorFit aims to overcome processing delays hindering clinical use.

Goal(s): TensorFit seeks to accelerate MRS analysis with a strong focus in time efficiency, using GPU acceleration, and modeling capabilities within the Torch framework.

Approach: Implemented in Python, employs Torch for efficient forward- and back-propagation, allowing rapid quantification of large datasets. It supports GPU  usage, and integration with SpectrIm-QMRS for clinical practices.

Results: TensorFit achieves speed-ups surpassing existing methods by up to 200x on GPU and 17x on CPU, making it a powerful tool for metabolite quantification in EPSI data.

Impact: TensorFit speed-up MRS metabolite quantification in clinical practices, enabling ultra-fast analysis. This tool could lead to enhance the use of high-resolution MRS adquisition for both research and clinical practices.