Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI

Repeatability of Open-MOLLI: An open-source inversion recovery myocardial T1 mapping sequence for fast prototyping

PURPOSE

To develop an open-source prototype of myocardial T1 mapping (Open-MOLLI) to improve accessibility to cardiac T1 mapping and evaluate its repeatability. With Open-MOLLI, we aim to enable faster implementation and testing of sequence modifications and to facilitate inter-scanner and cross-vendor reproducibility studies.

METHODS

Open-MOLLI is an inversion-recovery sequence using a balanced SSFP (bSSFP) readout, with inversion and triggering schemes based on the 5(3)3 MOLLI sequence, developed in Pulseq. Open-MOLLI and MOLLI sequences were acquired in the ISMRM/NIST phantom and 21 healthy volunteers. In 18 of those subjects, Open-MOLLI and MOLLI were repeated in the same session (test-retest).

RESULTS

Phantom T1 values were comparable between methods, specifically for the vial with reference T1 value most similar to healthy myocardium T1 (T1vial3 = 1027 ms): T1MOLLI = 1011 ± 24 ms versus T1Open-MOLLI = 1009 ± 20 ms. In vivo T1 estimates were similar between Open-MOLLI and MOLLI (T1MOLLI = 1004 ± 33 ms vs. T1Open-MOLLI = 998 ± 52 ms), with a mean difference of -17 ms (p = 0.20), despite noisier Open-MOLLI weighted images and maps. Repeatability measures were slightly higher for Open-MOLLI (RCMOLLI = 3.0% vs. RCOpen-MOLLI = 4.4%).

CONCLUSION

The open-source sequence Open-MOLLI can be used for T1 mapping in vivo with similar mean T1 values to the MOLLI method. Open-MOLLI increases the accessibility to cardiac T1 mapping, providing also a base sequence to which further improvements can easily be added and tested.

Open-source magnetic resonance imaging: Improving access, science, and education through global collaboration

Open-source practices and resources in magnetic resonance imaging (MRI) have increased substantially in recent years. This trend started with software and data being published open-source and, more recently, open-source hardware designs have become increasingly available. These developments towards a culture of sharing and establishing nonexclusive global collaborations have already improved the reproducibility and reusability of code and designs, while providing a more inclusive approach, especially for low-income settings. Community-driven standardization and documentation efforts are further strengthening and expanding these milestones. The future of open-source MRI is bright and we have just started to discover its full collaborative potential. In this review we will give an overview of open-source software and open-source hardware projects in human MRI research.

Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI

PURPOSE

To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform.

METHODS

We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD).

RESULTS

Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences.

CONCLUSION

The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge

PURPOSE

T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains.

METHODS

The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed.

RESULTS

Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org.

CONCLUSION

The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

The relaxometry hype cycle

Relaxometry is a field with a glorious and controversial history, and no review will ever do it justice. It is full of egos and inventions, patents and lawsuits, high expectations and deep disillusionments. Rather than a paragraph dedicated to each of these, we want to give it an impressionistic overview, painted over with a coat of personal opinions and ruminations about the future of the field. For those unfamiliar with the Gartner hype cycle, here's a brief recap. The cycle starts with a technology trigger and goes through a phase of unrealistically inflated expectations. Eventually the hype dies down as implementations fail to deliver on their promise, and disillusionment sets in. Technologies that manage to live through the trough reach the slope of enlightenment, when there is a flurry of second and third generation products that make the initial promise feel feasible again. Finally, we reach the slope of productivity, where mainstream adoption takes off, and more incremental progress is made, eventually reaching steady state in terms of the technology's visibility. The entire interactive timeline can be viewed at https://qmrlab.org/relaxometry/.

Quantitative analysis of MR T2 relaxation times in neck muscles

T2 relaxation times (T2 times) are different between resting and exercised muscles and between muscles of healthy subjects and subjects with muscle pathology. However, studies specifically focusing on neck muscles are lacking. Furthermore, normative neck muscle T2 times are not well defined and methodology used to analyse T2 times in neck muscles is not robust. We analysed T2 times in key neck muscles and explored factors affecting variability between muscles. 20 healthy subjects were recruited. Two circular regions of interest (ROIs) were drawn in two mutually exclusive regions within neck muscles on T2 weighted images and values averaged. ROI measurements were performed by a co-investigator, supervised by a neuro-radiologist. For the first ten subjects, measurements were done from C1-T1. For the remaining subjects, ROIs were drawn at two pre-determined levels. Two MRIs were repeated at 31 degrees acquisition to evaluate the effect of muscle fibre orientation. ROI values were translated into T2 times. Results showed semispinalis capitis had the longest T2 times (range 46.88-51.42 ms), followed by splenius capitis (range 47.37-48.33 ms), trapezius (range 45.27-47.46 ms), levator scapulae (range 43.17-45.63 ms) and sternocleidomastoid (range 38.45-42.91 ms). T2 times did not vary along length of muscles and were unaffected by muscle fibre orientation (P > 0.05). T2 times of splenius capitis correlated significantly with age at C2/C3 and C5/C6 levels and trapezius at C7/T1 level. Gender did not influence relaxation times (P > 0.05). In conclusion, results of normative neck muscle T2 time values and factors influencing the T2 times could serve as a reference for future MR analysis of neck muscles. The methodology used may also be useful for related studies of neck muscles.

Utility of MRI in Quantifying Tissue Injury in Cervical Spondylotic Myelopathy

Cervical spondylotic myelopathy (CSM) is a progressive disease that worsens over time if untreated. However, the rate of progression can vary among individuals and may be influenced by various factors, such as the age of the patients, underlying conditions, and the severity and location of the spinal cord compression. Early diagnosis and prompt treatment can help slow the progression of CSM and improve symptoms. There has been an increased use of magnetic resonance imaging (MRI) methods in diagnosing and managing CSM. MRI methods provide detailed images and quantitative structural and functional data of the cervical spinal cord and brain, allowing for an accurate evaluation of the extent and location of tissue injury. This review aims to provide an understanding of the use of MRI methods in interrogating functional and structural changes in the central nervous system in CSM. Further, we identified several challenges hindering the clinical utility of these neuroimaging methods.

Quantitative magnetic resonance imaging (qMRI) in axial spondyloarthritis

Imaging, and particularly MRI, plays a crucial role in the assessment of inflammation in rheumatic disease, and forms a core component of the diagnostic pathway in axial spondyloarthritis. However, conventional imaging techniques are limited by image contrast being non-specific to inflammation and a reliance on subjective, qualitative reader interpretation. Quantitative MRI methods offer scope to address these limitations and improve our ability to accurately and precisely detect and characterise inflammation, potentially facilitating a more personalised approach to management. Here, we review quantitative MRI methods and emerging quantitative imaging biomarkers for imaging inflammation in axial spondyloarthritis. We discuss the potential benefits as well as the practical considerations that must be addressed in the movement toward clinical translation of quantitative imaging biomarkers.

Open and reproducible neuroimaging: From study inception to publication

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

qMRI-BIDS: An extension to the brain imaging data structure for quantitative magnetic resonance imaging data

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.