Keywords: Diffusion Acquisition, Diffusion Tensor Imaging

Motivation: The reproducibility of diffusion MRI (dMRI) data collected at multiple sites can be affected by differences between MRI scanners, especially scanners from different manufacturers. 
 

Goal(s): To develop vendor-neutral dMRI pulse sequences using our Pulseq development platform and reduce the inter-scanner variability between scanners from different vendors.
 

Approach: Using a diffusion phantom and with three human subjects, we tested inter-scanner variability using Pulseq and vendor-specific product sequences. We report inter-scanner variations using standard error for mean diffusivity and fractional anisotropy.
 

Results: Pulseq sequence yielded dramatically better results (>2x reduction in variability) enhancing the reliability of dMRI measurements across scanners.

Impact: The vendor-neutral Pulseq-diffusion sequence has the potential to harmonize data acquisition and improve the robustness of diffusion MRI, making it an invaluable tool for advancing multi-site studies.

Keywords: Software Tools, Software Tools, QA/QC

Motivation: Reliable neuroimaging pipelines require the implementation of robust QA/QC protocols.

Goal(s): Developing an extension of MRIQC for the QA/QC of diffusion MRI data.

Approach: We build on MRIQC's infrastructure to generate individual visual reports of dMRI images and define new image quality metrics (IQMs).

Results: We developed a minimal processing pipeline for whole-brain dMRI data of human adults. The processing pipeline generates individual visual reports for the QA of unprocessed inputs. The pipeline also extracts IQMs to train automated decision-making, following MRIQC's established pattern.

Impact: MRIQC is a widely-adopted tool for the QA/QC of unprocessed MRI data. However, support for dMRI was previously lacking. This MRIQC extension will improve QA/QC of dMRI by bringing it to  the highest standards and will facilitate the implementation of rigorous protocols in multimodal neuroimaging.

Keywords: Software Tools, Perfusion, Reproducible research

Motivation: Lack of validated and open-source intravoxel incoherent motion (IVIM) post-processing and fitting code is hindering reproducible research, limiting the validation and large-scale roll-out of IVIM imaging.

Goal(s): To create an open-source code repository for IVIM-related code.

Approach: Scientists interested in IVIM are encouraged to upload their code to our open-source code repository built by the ISMRM OSIPI task force 2.4, where automated testing and evaluation based on reference data are used to enable quality control of the code.

Results: As of November 2023, 19 code contributions have been submitted by 6 different institutes, all passing automated testing.

Impact: The work of ISMRM OSIPI task force 2.4 enables an open-source platform for validated code relevant to intravoxel incoherent motion (IVIM) imaging, thus reducing duplicate development, improving reproducibility, and serving as a benchmark for future methods.

Keywords: Software Tools, Software Tools, Standardisation, Quality Control

Motivation: It is critical that MRI data acquired in multi-site, multi-vendor studies conforms to a standardised acquisition protocol.

Goal(s): To develop XNAT tools to highlight scans that do not conform to a specified protocol or are of insufficient quality, enabling rapid correction of errors before future scans.

Approach: Multi-site DICOM data is uploaded to XNAT after acquisition, by integrating software tools with this database, investigators are informed if data does not conform.

Results: DICOM-QC, a tool to automatically compare DICOM metadata to predefined values, and ImageSNR-QC to calculate image SNR, applied here to a multi-site kidney study.

Impact: This work outlines two tools that integrate with XNAT, DICOM-QC and ImageSNR-QC, which can be used by any investigators running large studies to ensure uploaded data conforms to the study protocol, ensuring consistency over sites, vendors, and repeated longitudinal scans.

Keywords: Software Tools, Translational Studies, Standardisation, reproducibility, QA/QC, multi-centre studies

Motivation: Standardisation of imaging protocols in multi-centre studies is challenging, which can hamper clinical translation.

Goal(s): This study aimed to develop and demonstrate a software tool that automatically assesses imaging protocol compliance. 

Approach: The tool was containerised and integrated into an imaging repository. It was applied to a dataset from a whole-body MRI (WB-MRI) myeloma study, which included 174 examinations acquired across 10 sites with scanners from three manufacturers. 

Results: The software successfully identified some parameters and sites where persistent deviations occurred, although 88% of examinations were conducted according to the relevant clinical guidelines with good overall compliance to site-specific protocols. 

Impact: Repository-integrated software is presented for automated monitoring of imaging protocol compliance to support standardisation in multi-centre studies and clinical translation. A multi-centre whole-body MRI study demonstrates good compliance that could have been improved further with proactive monitoring using this tool. 

Keywords: Data Processing, Quantitative Imaging, Prostate Cancer

Motivation: The reliable evaluation of T2-weighted MRI (T2w) signal change in prostate cancer following radiotherapy (RT) is challenging due to deformations (physiological and RT-related) and scanner/protocol acquisition variability.

Goal(s): To develop an automated and reproducible methodology for quantification of T2w signal change in longitudinal studies following RT.

Approach: The methodology includes T2w image intensity harmonization and deformable registration of post-RT to pre-RT images for automated detection of prostate, peripheral zone and tumor volume.

Results: The repeatability in T2w intensity estimation improved following the automatic registration relative to manual contours; and the quantitative changes of T2w reached significance when pre- and post-RT series were compared.

Impact: The developed methodology allows to automatically detect ROIs in post-RT MRI exams, reduces data acquisition-related variation and improves imaging features’ repeatability, thereby enables the quantitative characterization of RT-induced changes in T2w.

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Data Harmonization, Bloch equation

Motivation: The MR scanner effect in a multi-site dataset can affect bias in statistical analysis or reduce generality in deep neural networks. 

Goal(s): We aim to suggest a MR physics-informed harmonization framework (PhyCHarm) that generates consistent quantitative maps and harmonized T1w images.

Approach: We introduce a Quantitative Maps Generator and a Harmonization Network to be trained with a constraint loss based on a signal equation. 

Results: PhyCHarm shows the highest evaluation scores in both networks and consistent segmentation accuracy in the downstream task (FSL FAST GM and WM segmentation).

Impact: PhyCHarm works based on the Bloch equation. PhyCHarm enables us to reduce scanner effects efficiently in the dataset before conducting test/retest, longitudinal, or multi-site studies. It can be helpful to ensure deep neural networks' generality.

Keywords: Phantoms, Precision & Accuracy, Quality Assurance, MR Fingerprinting, Quantitative Imaging, Relaxometry, Measurement & Correction

Motivation: Currently, we do not know how frequently quality assurance (QA) should be performed on an MRI scanner to detect changes that impact quantitative measurements.

Goal(s): Our goal is to determine the frequency of QA measurements needed during the course of a quantitative in vivo study to have confidence in the in vivo measurements.

Approach: Phantom quantitative QA measurements were made immediately before or after the in vivo measurements over the duration of a repeatability study.

Results: All quantitative phantom measurements had variation well below 10 % over the course of the 99 day study.

Impact: We now know that for measurements using magnetic resonance fingerprinting on this system, QA using phantom measurements is only necessary at the start and end of an in vivo study when the study duration is less than approximately 3 months.

Keywords: Quantitative Imaging, Validation, Liver, Phantoms

Motivation: The Quantitative Imaging Biomarkers Alliance (QIBA) PDFF Profile describes the expected performance of an imaging technique when measuring PDFF. However, the expected performance in phantoms was determined in one phantom on 27 scanners and may not be applicable widely.

Goal(s): We tested the hypothesis that the QIBA PDFF bias criteria (mean:within ±5.0%, maximum:within ±7.0% (percentage points)) cannot be attained at scale with multiple phantoms on >400 scanners.

Approach: We calculated the QIBA PDFF criteria using phantom data from 416 scanners across three vendors at 1.5T and 3T.

Results: All six combinations of scanner vendor and field strength passed the QIBA PDFF criteria.

Impact: The hypothesis that the QIBA PDFF bias criteria cannot be maintained with multiple phantoms on >400 scanners was disproven in a novel dataset with 416 scanners, strongly suggesting that it is possible to achieve this level of performance at scale.