Establishing intra- and inter-vendor reproducibility of T1 relaxation time measurements with 3T MRI

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.

Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review

Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings. Keywords: MRI, Genital/Reproductive, Prostate Segmentation, Deep Learning Systematic review registration link: osf.io/nxaev © RSNA, 2024.

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.

Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study

PURPOSE

To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping.

METHODS

This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers.

RESULTS

Quantitative maps obtained with 3D-QALAS showed high linearity (R2  = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis.

CONCLUSION

The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

A preparation pulse for fast steady state approach in Actual Flip angle Imaging

BACKGROUND

Actual Flip angle Imaging (AFI) is a sequence used for B1 mapping, also embedded in the Variable flip angle with AFI for simultaneous estimation of T1 , B1 and equilibrium magnetization.

PURPOSE

To investigate the design of a preparation module for AFI to allow a fast approach to steady state (SS) without requiring the use of dummy acquisitions.

METHODS

The features of a preparation module with a B1 insensitive adiabatic pulse, spoiler gradients, and a recovery timeT r e c $T_{rec}$ were studied with simulations and validated via experiments and acquired with different k-space traveling strategies. The robustness of the flip angle of the preparation pulse on the acquired signal is studied.

RESULTS

When a 90° adiabatic pulse is used, the forthcomingT r e c $T_{rec}$ can be expressed as a function of repetition times and AFI flip angle only asTR 1 ( n + cos α ) / ( 1 - cos 2 α ) $\mathrm{TR_1}(n+\cos \alpha )/(1-\cos ^2\alpha )$ , where n represents the ratio between the two repetition times of AFI. The robustness of the method is demonstrated by showing that using the values further away from 90° still allows for a faster approach to SS than the use of dummy pulses.

CONCLUSIONS

The preparation module is particularly advantageous for low flip angles, as well as for AFI sequences that sample the center of the k-space early in the sequence, such as centric ordering acquisitions, and for ultrafast EPI-based AFI methods, thus allowing to reduce scanner overhead time.

A Temporal Instability Measure for fMRI Quality Assurance

BACKGROUND

There exist several fMRI quality assurance measures to assess scanner stability. Because they have practical and/or theoretical limitations, a different and more practical measure for instability would be desirable.

PURPOSE

To develop and test a sensitive, reliable and widely applicable temporal instability measure (TIM) for fMRI quality assurance.

STUDY TYPE

Technical development.

PHANTOM

Spherical gel phantom.

POPULATION

A total of 120 datasets from a local Philips scanner with two different receive-only head coils (32ch and 8ch, 60 datasets per coil) were collected as well as 29 additional datasets with three different receive-only head coils (20ch, 32ch, and 64ch) from two additional sites with GE (seven runs with 32ch) and Siemens scanners (seven runs with 32ch and Multiband imaging, five runs with 20ch, 32ch, and 64ch) were borrowed.

FIELD STRENGTH/SEQUENCE

2D Echo-planar-imaging (EPI).

ASSESSMENT

A new TIM was proposed that is based on the eigenratio of the correlation coefficient matrix, where each entry of the matrix is a correlation coefficient between two time-points of the time-series.

STATISTICAL TESTS

Nonparametric bootstrap resampling was used twice to estimate confidence intervals (CI) of the TIM values and to assess the improved sensitivity of this measure. Differences in coil performance were assessed via a nonparametric bootstrap two-sample t-test. P-values <0.05 were considered significant.

RESULTS

The TIM values ranged between 60 parts-per-million and 10,780 parts-per-million across all 149 experiments. The mean CI was 2.96% and 2.16% for the 120 and 29 fMRI datasets, respectively (the repeated bootstrap analysis gave 2.9% and 2.19%, respectively). The 32ch coils of the local Philips data provided more stable measurements than the 8ch coil (observed two-sample t-values = 26.36, -0.2 and -6.2 for TIM, tSNR, and RDC, respectively. PtSNR  = 0.58).

DATA CONCLUSION

The proposed TIM is particularly useful for multichannel coils with spatially nonuniform receive sensitivity and overcomes several limitations of other measures. As such, it provides a reliable test for ascertaining scanner stability for fMRI experiments.

EVIDENCE LEVEL

5.

TECHNICAL EFFICACY

Stage 1.

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/.

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.

Short-term repeatability and long-term reproducibility of quantitative MR imaging biomarkers in a single centre longitudinal study

Quantitative imaging biomarkers (QIBs) can be defined as objective measures that are sensitive and specific to changes in tissue physiology. Provided the acquired QIBs are not affected by scanner changes, they could play an important role in disease diagnosis, prognosis, management, and treatment monitoring. The precision of selected QIBs was assessed from data collected on a 3-T scanner in four healthy participants over a 5-year period. Inevitable scanner changes and acquisition protocol revisions occurred during this time. Standard and custom processing pipelines were used to calculate regional brain volume, cortical thickness, T2, T2*, quantitative susceptibility, cerebral blood flow, axial, radial and mean diffusivity, peak width of skeletonized mean diffusivity, and fractional anisotropy from the acquired images. Coefficient of variation (CoV) and intra-class correlation (ICC) indices were determined in the short-term (i.e., repeatable over three acquisitions within 4 weeks) and in the long-term (i.e., reproducible over four acquisition sessions in 5 years). Precision indices varied based on acquisition technique, processing pipeline, and anatomical region. Good repeatability (average CoV=2.40% and ICC=0.78) and reproducibility (average CoV=8.86 % and ICC=0.72) were found over all QIBs. The best performance indices were obtained for diffusion derived biomarkers (CoV∼0.96% and ICCs=0.87); conversely, the poorest indices were found for the cerebral blood flow biomarker (CoV>10% and ICC<0.5). These results demonstrate that changes in protocol, along with hardware and software upgrades, did not affect the estimates of the selected biomarkers and their precision. Further characterization of the QIB is necessary to understand meaningful changes in the biomarkers in longitudinal studies of normal brain aging and translation to clinical research.

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

PURPOSE

We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequences decrease inter-vendor variability of T1, magnetization transfer ratio (MTR), and magnetization transfer saturation-index (MTsat) measurements.

METHODS

We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR, and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis.

RESULTS

In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8% to 19.4% to 0.2% to 5% with an overall accuracy improvement, reducing ground truth T1 deviations from 7% to 11% to 0.2% to 4%. In vivo, we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR, and MTsat) using the vendor-neutral sequence.

CONCLUSION

We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.

Reliability of quantitative multiparameter maps is high for magnetization transfer and proton density but attenuated for R1 and R2 * in healthy young adults

We investigate the reliability of individual differences of four quantities measured by magnetic resonance imaging‐based multiparameter mapping (MPM): magnetization transfer saturation (MT), proton density (PD), longitudinal relaxation rate (R₁), and effective transverse relaxation rate (R₂*). Four MPM datasets, two on each of two consecutive days, were acquired in healthy young adults. On Day 1, no repositioning occurred and on Day 2, participants were repositioned between MPM datasets. Using intraclass correlation effect decomposition (ICED), we assessed the contributions of session‐specific, day‐specific, and residual sources of measurement error. For whole‐brain gray and white matter, all four MPM parameters showed high reproducibility and high reliability, as indexed by the coefficient of variation (CoV) and the intraclass correlation (ICC). However, MT, PD, R₁, and R₂* differed markedly in the extent to which reliability varied across brain regions. MT and PD showed high reliability in almost all regions. In contrast, R₁ and R₂* showed low reliability in some regions outside the basal ganglia, such that the sum of the measurement error estimates in our structural equation model was higher than estimates of between‐person differences. In addition, in this sample of healthy young adults, the four MPM parameters showed very little variability over four measurements but differed in how well they could assess between‐person differences. We conclude that R₁ and R₂* might carry only limited person‐specific information in some regions of the brain in healthy young adults, and, by implication, might be of restricted utility for studying associations to between‐person differences in behavior in those regions.

Challenges in ensuring the generalizability of image quantitation methods for MRI

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.

Determining the longitudinal accuracy and reproducibility of T1 and T2 in a 3T MRI scanner

Purpose

To determine baseline accuracy and reproducibility of T₁ and T₂ relaxation times over 12 months on a dedicated radiotherapy MRI scanner.

Methods

An International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) System Phantom was scanned monthly on a 3T MRI scanner for 1 year. T₁ was measured using inversion recovery (T₁‐IR) and variable flip angle (T₁‐VFA) sequences and T₂ was measured using a multi‐echo spin echo (T₂‐SE) sequence. For each vial in the phantom, accuracy errors (%bias) were determined by the relative differences in measured T₁ and T₂ times compared to reference values. Reproducibility was measured by the coefficient of variation (CV) of T₁ and T₂ measurements across monthly scans. Accuracy and reproducibility were mainly assessed on vials with relaxation times expected to be in physiological ranges at 3T.

Results

A strong linear correlation between measured and reference relaxation times was found for all sequences tested (R² > 0.997). Baseline bias (and CV[%]) for T₁‐IR, T₁‐VFA and T₂‐SE sequences were +2.0% (2.1), +6.5% (4.2), and +8.5% (1.9), respectively.

Conclusions

The accuracy and reproducibility of T₁ and T₂ on the scanner were considered sufficient for the sequences tested. No longitudinal trends of variation were deduced, suggesting less frequent measurements are required following the establishment of baselines.

MRI of healthy brain aging: A review

We present a review of the characterization of healthy brain aging using MRI with an emphasis on morphology, lesions, and quantitative MR parameters. A scope review found 6612 articles encompassing the keywords "Brain Aging" and "Magnetic Resonance"; papers involving functional MRI or not involving imaging of healthy human brain aging were discarded, leaving 2246 articles. We first consider some of the biogerontological mechanisms of aging, and the consequences of aging in terms of cognition and onset of disease. Morphological changes with aging are reviewed for the whole brain, cerebral cortex, white matter, subcortical gray matter, and other individual structures. In general, volume and cortical thickness decline with age, beginning in mid-life. Prevalent silent lesions such as white matter hyperintensities, microbleeds, and lacunar infarcts are also observed with increasing frequency. The literature regarding quantitative MR parameter changes includes T₁ , T₂ , T₂ *, magnetic susceptibility, spectroscopy, magnetization transfer, diffusion, and blood flow. We summarize the findings on how each of these parameters varies with aging. Finally, we examine how the aforementioned techniques have been used for age prediction. While relatively large in scope, we present a comprehensive review that should provide the reader with sound understanding of what MRI has been able to tell us about how the healthy brain ages.

Extracellular volume quantification using synthetic haematocrit assessed from native and post-contrast longitudinal relaxation T1 times of a blood pool

Background

In terms of cardiovascular magnetic resonance are haematocrit values required for calculation of extracellular volume fraction (ECV). Previously published studies have hypothesized that haematocrit could be calculated from T1 blood pool relaxation time, however only native T1 relaxation time values have been used and the resulting formulae had been both in reciprocal and linear proportion. The aim of the study was to generate a synthetic haematocrit formula from only native relaxation time values first, calculate whether linear or reciprocal model is more precise in haematocrit estimation and then determine whether adding post-contrast values further improve its precision.

Methods

One hundred thirty-nine subjects underwent CMR examination. Haematocrit was measured using standard laboratory methods. Afterwards T1 relaxation times before and after the application of a contrast agent were measured and a statistical relationship between these values was calculated.

Results

Different linear and reciprocal models were created to estimate the value of synthetic haematocrit and ECV. The highest coefficient of determination was observed in the combined reciprocal model “− 0.047 + (779/ blood native) − (11.36/ blood post-contrast)”.

Conclusions

This study provides more evidence that assessing synthetic haematocrit and synthetic ECV is feasible and statistically most accurate model to use is reciprocal. Adding post-contrast values to the calculation was proved to improve the precision of the formula statistically significantly.

Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T₁. The purpose of this study was to determine the bias and reproducibility of T₁ measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T₁ measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T₁ measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T₁ measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T₁ measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T₁ values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T₁ values than the inversion recovery measurements. At 3 T, the measured variable flip angle T₁ for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T₁. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.

Traveling Volunteers: A Multi-Vendor, Multi-Center Study on Reproducibility and Comparability of 4D Flow Derived Aortic Hemodynamics in Cardiovascular Magnetic Resonance

BACKGROUND

Implementation of four-dimensional flow magnetic resonance (4D Flow MR) in clinical routine requires awareness of confounders.

PURPOSE

To investigate inter-vendor comparability of 4D Flow MR derived aortic hemodynamic parameters, assess scan-rescan repeatability, and intra- and interobserver reproducibility.

STUDY TYPE

Prospective multicenter study.

POPULATION

Fifteen healthy volunteers (age 24.5 ± 5.3 years, 8 females).

FIELD STRENGTH/SEQUENCE

3 T, vendor-provided and clinically used 4D Flow MR sequences of each site.

ASSESSMENT

Forward flow volume, peak velocity, average, and maximum wall shear stress (WSS) were assessed via nine planes (P1-P9) throughout the thoracic aorta by a single observer (AD, 2 years of experience). Inter-vendor comparability as well as scan-rescan, intra- and interobserver reproducibility were examined.

STATISTICAL TESTS

Equivalence was tested setting the 95% confidence interval of intraobserver and scan-rescan difference as the limit of clinical acceptable disagreement. Intraclass correlation coefficient (ICC) and Bland-Altman plots were used for scan-rescan reproducibility and intra- and interobserver agreement. A P-value <0.05 was considered statistically significant. ICCs ≥ 0.75 indicated strong correlation (>0.9: excellent, 0.75-0.9: good).

RESULTS

Ten volunteers finished the complete study successfully. 4D flow derived hemodynamic parameters between scanners of three different vendors are not equivalent exceeding the equivalence range. P3-P9 differed significantly between all three scanners for forward flow (59.1 ± 13.1 mL vs. 68.1 ± 12.0 mL vs. 55.4 ± 13.1 mL), maximum WSS (1842.0 ± 190.5 mPa vs. 1969.5 ± 398.7 mPa vs. 1500.6 ± 247.2 mPa), average WSS (1400.0 ± 149.3 mPa vs. 1322.6 ± 211.8 mPa vs. 1142.0 ± 198.5 mPa), and peak velocity between scanners I vs. III (114.7 ± 12.6 cm/s vs. 101.3 ± 15.6 cm/s). Overall, the plane location at the sinotubular junction (P1) presented most inter-vendor stability (forward: 78.5 ± 15.1 mL vs. 80.3 ± 15.4 mL vs. 79.5 ± 19.9 mL [P = 0.368]; peak: 126.4 ± 16.7 cm/s vs. 119.7 ± 13.6 cm/s vs. 111.2 ± 22.6 cm/s [P = 0.097]). Scan-rescan reproducibility and intra- and interobserver variability were good to excellent (ICC ≥ 0.8) with best agreement for forward flow (ICC ≥ 0.98).

DATA CONCLUSION

The clinical protocol used at three different sites led to differences in hemodynamic parameters assessed by 4D flow.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 2.

Sources of systematic error in DCE-MRI estimation of low-level blood-brain barrier leakage

PURPOSE

Dynamic contrast-enhanced (DCE) -MRI with Patlak model analysis is increasingly used to quantify low-level blood-brain barrier (BBB) leakage in studies of pathophysiology. We aimed to investigate systematic errors due to physiological, experimental, and modeling factors influencing quantification of the permeability-surface area product PS and blood plasma volume v_p , and to propose modifications to reduce the errors so that subtle differences in BBB permeability can be accurately measured.

METHODS

Simulations were performed to predict the effects of potential sources of systematic error on conventional PS and v_p quantification: restricted BBB water exchange, reduced cerebral blood flow, arterial input function (AIF) delay and B 1 + error. The impact of targeted modifications to the acquisition and processing were evaluated, including: assumption of fast versus no BBB water exchange, bolus versus slow injection of contrast agent, exclusion of early data from model fitting and B 1 + correction. The optimal protocol was applied in a cohort of recent mild ischaemic stroke patients.

RESULTS

Simulation results demonstrated substantial systematic errors due to the factors investigated (absolute PS error ≤ 4.48 × 10^-4 min^-1 ). However, these were reduced (≤0.56 × 10^-4 min^-1 ) by applying modifications to the acquisition and processing pipeline. Processing modifications also had substantial effects on in-vivo normal-appearing white matter PS estimation (absolute change ≤ 0.45 × 10^-4 min^-1 ).

CONCLUSION

Measuring subtle BBB leakage with DCE-MRI presents unique challenges and is affected by several confounds that should be considered when acquiring or interpreting such data. The evaluated modifications should improve accuracy in studies of neurodegenerative diseases involving subtle BBB breakdown.

System-specific evaluation of the dual flip angle MRI technique for quantitative T1 measurement

PURPOSE

To investigate if the accuracy of the dual flip angle (DFA) technique for T₁ measurement is affected by the system-specific RF excitation performance.

METHODS

A T₁ phantom, made of 12 vials of unique T₁ value ranging approximately from 200 ms to 2000 ms, was built and tested on seven different clinical scanners. For each experiment, the reference T₁ of each vial was obtained by the inversion recovery-based technique, and the DFA technique was applied repeatedly with several flip angle (FA) pairs conventionally proposed as optimal. The accuracy of the DFA technique for each FA pair was then evaluated by comparing the measured T₁ values for the vials to the references. Any variation of the accuracy was then evaluated across different FA pairs, and across different MRI systems. To improve accuracy with a selected FA pair, the signal ratio (SR) curve, obtained from the phantom, was utilized in a calibration strategy of the DFA technique.

RESULTS

When combined for all the vials, the average ratio of the measured T₁ to the reference generally increased as the FA pair window gradually slid from the smaller to the larger FA values. Furthermore, among several optimal FA pairs, the pair of the best accuracy varied slightly by the MRI system. The accuracy for any FA pair could be improved when the calibration strategy was utilized.

CONCLUSIONS

The RF excitation performance may vary by the specific FA pair and by the specific MRI system, influencing the accuracy of the DFA technique. The system-specific evaluation, and, if needed, its calibration, would help improve the accuracy of the DFA technique.

Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method

Quantitative tissue characteristics, which provide valuable diagnostic information, can be represented by magnetic resonance (MR) parameter maps using magnetic resonance imaging (MRI); however, a long scan time is necessary to acquire them, which prevents the application of quantitative MR parameter mapping to real clinical protocols. For fast MR parameter mapping, we propose a deep model-based MR parameter mapping network called DOPAMINE that combines a deep learning network with a model-based method to reconstruct MR parameter maps from undersampled multi-channel k-space data. DOPAMINE consists of two networks: 1) an MR parameter mapping network that uses a deep convolutional neural network (CNN) that estimates initial parameter maps from undersampled k-space data (CNN-based mapping), and 2) a reconstruction network that removes aliasing artifacts in the parameter maps with a deep CNN (CNN-based reconstruction) and an interleaved data consistency layer by an embedded MR model-based optimization procedure. We demonstrated the performance of DOPAMINE in brain T₁ map reconstruction with a variable flip angle (VFA) model. To evaluate the performance of DOPAMINE, we compared it with conventional parallel imaging, low-rank based reconstruction, model-based reconstruction, and state-of-the-art deep-learning-based mapping methods for three different reduction factors (R = 3, 5, and 7) and two different sampling patterns (1D Cartesian and 2D Poisson-disk). Quantitative metrics indicated that DOPAMINE outperformed other methods in reconstructing T₁ maps for all sampling patterns and reduction factors. DOPAMINE exhibited quantitatively and qualitatively superior performance to that of conventional methods in reconstructing MR parameter maps from undersampled multi-channel k-space data. The proposed method can thus reduce the scan time of quantitative MR parameter mapping that uses a VFA model.

Three dimensional MRF obtains highly repeatable and reproducible multi-parametric estimations in the healthy human brain at 1.5T and 3T

Magnetic resonance fingerprinting (MRF) is highly promising as a quantitative MRI technique due to its accuracy, robustness, and efficiency. Previous studies have found high repeatability and reproducibility of 2D MRF acquisitions in the brain. Here, we have extended our investigations to 3D MRF acquisitions covering the whole brain using spiral projection k-space trajectories. Our travelling head study acquired test/retest data from the brains of 12 healthy volunteers and 8 MRI systems (3 systems at 3 T and 5 at 1.5 T, all from a single vendor), using a study design not requiring all subjects to be scanned at all sites. The pulse sequence and reconstruction algorithm were the same for all acquisitions. After registration of the MRF-derived PD T1 and T2 maps to an anatomical atlas, coefficients of variation (CVs) were computed to assess test/retest repeatability and inter-site reproducibility in each voxel, while a General Linear Model (GLM) was used to determine the voxel-wise variability between all confounders, which included test/retest, subject, field strength and site. Our analysis demonstrated a high repeatability (CVs 0.7-1.3% for T1, 2.0-7.8% for T2, 1.4-2.5% for normalized PD) and reproducibility (CVs of 2.0-5.8% for T1, 7.4-10.2% for T2, 5.2-9.2% for normalized PD) in gray and white matter. Both repeatability and reproducibility improved when compared to similar experiments using 2D acquisitions. Three-dimensional MRF obtains highly repeatable and reproducible estimations of T1 and T2, supporting the translation of MRF-based fast quantitative imaging into clinical applications.

Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases

Imaging biomarkers require technical, biological, and clinical validation to be translated into robust tools in research or clinical settings. This study contributes to the technical validation of radiomic features from magnetic resonance imaging (MRI) by evaluating the repeatability of features from four MR sequences: pre-contrast T1- and T2-weighted images, pre-contrast quantitative T1 maps (qT1), and contrast-enhanced T1-weighted images. Fifty-one patients with colorectal cancer liver metastases were scanned twice, up to 7 days apart. Repeatability was quantified using the intraclass correlation coefficient (ICC) and repeatability coefficient (RC), and the impact of non-Gaussian feature distributions and image normalisation was evaluated. Most radiomic features had non-Gaussian distributions, but Box-Cox transformations enabled ICCs and RCs to be calculated appropriately for an average of 97% of features across sequences. ICCs ranged from 0.30 to 0.99, with volume and other shape features tending to be most repeatable; volume ICC > 0.98 for all sequences. 19% of features from non-normalised images exhibited significantly different ICCs in pair-wise sequence comparisons. Normalisation tended to increase ICCs for pre-contrast T1- and T2-weighted images, and decrease ICCs for qT1 maps. RCs tended to vary more between sequences than ICCs, showing that evaluations of feature performance depend on the chosen metric. This work suggests that feature-specific repeatability, from specific combinations of MR sequence and pre-processing steps, should be evaluated to select robust radiomic features as biomarkers in specific studies. In addition, as different repeatability metrics can provide different insights into a specific feature, consideration of the appropriate metric should be taken in a study-specific context.

Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence

Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.

Multiparameter mapping of relaxation (R1, R2*), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study

Multicenter clinical and quantitative magnetic resonance imaging (qMRI) studies require a high degree of reproducibility across different sites and scanner manufacturers, as well as time points. We therefore implemented a multiparameter mapping (MPM) protocol based on vendor's product sequences and demonstrate its repeatability and reproducibility for whole‐brain coverage. Within ~20 min, four MPM metrics (magnetization transfer saturation [MT], proton density [PD], longitudinal [R1], and effective transverse [R2*] relaxation rates) were measured using an optimized 1 mm isotropic resolution protocol on six 3 T MRI scanners from two different vendors. The same five healthy participants underwent two scanning sessions, on the same scanner, at each site. MPM metrics were calculated using the hMRI‐toolbox. To account for different MT pulses used by each vendor, we linearly scaled the MT values to harmonize them across vendors. To determine longitudinal repeatability and inter‐site comparability, the intra‐site (i.e., scan‐rescan experiment) coefficient of variation (CoV), inter‐site CoV, and bias across sites were estimated. For MT, R1, and PD, the intra‐ and inter‐site CoV was between 4 and 10% across sites and scan time points for intracranial gray and white matter. A higher intra‐site CoV (16%) was observed in R2* maps. The inter‐site bias was below 5% for all parameters. In conclusion, the MPM protocol yielded reliable quantitative maps at high resolution with a short acquisition time. The high reproducibility of MPM metrics across sites and scan time points combined with its tissue microstructure sensitivity facilitates longitudinal multicenter imaging studies targeting microstructural changes, for example, as a quantitative MRI biomarker for interventional clinical trials.

Consensus-based technical recommendations for clinical translation of renal T1 and T2 mapping MRI

To develop technical recommendations on the acquisition and post-processing of renal longitudinal (T1) and transverse (T2) relaxation time mapping. A multidisciplinary panel consisting of 18 experts in the field of renal T1 and T2 mapping participated in a consensus project, which was initiated by the European Cooperation in Science and Technology Action PARENCHIMA CA16103. Consensus recommendations were formulated using a two-step modified Delphi method. The first survey consisted of 56 items on T1 mapping, of which 4 reached the pre-defined consensus threshold of 75% or higher. The second survey was expanded to include both T1 and T2 mapping, and consisted of 54 items of which 32 reached consensus. Recommendations based were formulated on hardware, patient preparation, acquisition, analysis and reporting. Consensus-based technical recommendations for renal T1 and T2 mapping were formulated. However, there was considerable lack of consensus for renal T1 and particularly renal T2 mapping, to some extent surprising considering the long history of relaxometry in MRI, highlighting key knowledge gaps that require further work. This paper should be regarded as a first step in a long-term evidence-based iterative process towards ever increasing harmonization of scan protocols across sites, to ultimately facilitate clinical implementation.

Controlled saturation magnetization transfer for reproducible multivendor variable flip angle T1 and T2 mapping

Purpose

The widespread clinical application of quantitative MRI has been hindered by a lack of reproducibility across sites and vendors. Previous work has attributed this to incorrect B₁ mapping or insufficient spoiling conditions. We recently proposed the controlled saturation magnetization transfer (CSMT) framework and hypothesized that the lack of reproducibility can also be attributed to magnetization transfer effects. This work seeks to validate this hypothesis and demonstrate that reproducible multivendor single‐pool relaxometry can be achieved with the CSMT approach.

Methods

Three healthy volunteers were scanned on scanners from 3 vendors (GE Healthcare, Philips, Siemens). An extensive set of images necessary for joint T₁ and T₂ estimation were acquired with (1) each vendor default RF pulses and spoiling conditions; (2) harmonized RF spoiling; and (3) harmonized RF spoiling and CSMT pulses. Different subsets of images were used to generate 6 different T₁ and T₂ maps for each subject’s data from each vendor. Cross‐protocol, cross‐vendor, and test/retest variability were estimated.

Results

Harmonized RF spoiling conditions are insufficient to ensure good cross‐vendor reproducibility. Controlled saturation magnetization transfer allows cross‐protocol variability to be reduced from 18.3% to 4.0%. Whole‐brain variability using the same protocol was reduced from a maximum of 19% to 4.5% across sites. Both CSMT and native vendor RF conditions have a reported variability of less than 5% for repeat measures on the same vendor.

Conclusion

Magnetization transfer effects are a major contributor to intersite/intrasite variability of T₁ and T₂ estimation. Controlled saturation magnetization transfer stabilizes these effects, paving the way for the use of single‐pool T₁ and T₂ as a reliable source for clinical diagnosis across sites.

Consensus-based technical recommendations for clinical translation of renal T1 and T2 mapping MRI

To develop technical recommendations on the acquisition and post-processing of renal longitudinal (T1) and transverse (T2) relaxation time mapping. A multidisciplinary panel consisting of 18 experts in the field of renal T1 and T2 mapping participated in a consensus project, which was initiated by the European Cooperation in Science and Technology Action PARENCHIMA CA16103. Consensus recommendations were formulated using a two-step modified Delphi method. The first survey consisted of 56 items on T1 mapping, of which 4 reached the pre-defined consensus threshold of 75% or higher. The second survey was expanded to include both T1 and T2 mapping, and consisted of 54 items of which 32 reached consensus. Recommendations based were formulated on hardware, patient preparation, acquisition, analysis and reporting. Consensus-based technical recommendations for renal T1 and T2 mapping were formulated. However, there was considerable lack of consensus for renal T1 and particularly renal T2 mapping, to some extent surprising considering the long history of relaxometry in MRI, highlighting key knowledge gaps that require further work. This paper should be regarded as a first step in a long-term evidence-based iterative process towards ever increasing harmonization of scan protocols across sites, to ultimately facilitate clinical implementation.

Robust 3D Bloch-Siegert based B 1 + mapping using multi-echo general linear modeling

PURPOSE

Quantitative MRI applications, such as mapping the T₁ time of tissue, puts high demands on the accuracy and precision of transmit field ( B 1 + ) estimation. A candidate approach to satisfy these requirements exploits the difference in phase induced by the Bloch-Siegert frequency shift (BSS) of 2 acquisitions with opposite off-resonance frequency radiofrequency pulses. Interleaving these radiofrequency pulses ensures robustness to motion and scanner drifts; however, here we demonstrate that doing so also introduces a bias in the B 1 + estimates.

THEORY AND METHODS

It is shown here by means of simulation and experiments that the amplitude of the error depends on MR pulse sequence parameters, such as repetition time and radiofrequency spoiling increment, but more problematically, on the intrinsic properties, T₁ and T₂ , of the investigated tissue. To solve these problems, a new approach to BSS-based B 1 + estimation that uses a multi-echo acquisition and a general linear model to estimate the correct BSS-induced phase is presented.

RESULTS

In line with simulations, phantom and in vivo experiments confirmed that the general linear model-based method removed the dependency on tissue properties and pulse sequence settings.

CONCLUSION

The general linear model-based method is recommended as a more accurate approach to BSS-based B 1 + mapping.

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

Development, integration and use of an ultra-high-strength gradient system on a human-size 3 T magnet for small animal MRI

This study aims to integrate an ultra-high-strength gradient coil system on a clinical 3 T magnet and demonstrate its preclinical imaging capabilities. Dedicated phantoms were used to qualitatively and quantitatively assess the performance of the gradient system. Advanced MR imaging sequences, including diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM), were implemented and executed on an ex vivo specimen as well as in vivo rats. The DTI and QSM results on the phantom agreed well with those in the literature. Furthermore, studies on ex vivo specimens have demonstrated the applicability of DTI and QSM on our system to probe microstructural changes in a mild traumatic brain injury rat model. The feasibility of in vivo rat DTI was also demonstrated. We showed that the inserted ultra-high-strength gradient coil was successfully integrated on a clinically used magnet. After careful tuning and calibration, we verified the accuracy and quantitative preclinical imaging capability of the integrated system in phantom and in vivo rat brain experiments. This study can be essential to establish dedicated animal MRI platform on clinical MRI scanners and facilitate translational studies at clinical settings.

Sub-millimeter T1 mapping of rapidly relaxing compartments with gradient delay corrected spiral TAPIR and compressed sensing at 3T

PURPOSE

The TAPIR sequence is an accurate and efficient method for T₁ mapping. It combines a slice-interleaving Look-Locker read-out with an acquisition of multiple k-space lines in 1 shot. Whereas the acquisition of multiple lines per excitation increases imaging speed, the corresponding increase in TR and TE is detrimental to the T₁ fitting performance. This is especially problematic for substances exhibiting rapid T₂ ^* relaxation (e.g., myelin water).

METHODS

The T₁ fitting performance of TAPIR is enhanced by using an interleaved spiral read-out with shorter TE and TR. Furthermore, an improvement to a method for fast gradient delay estimation is presented. Whereas previous methods assume the gradient delay to be stationary, the presented approach corrects the spiral k-space trajectory by using a polynomial fit of the measured gradient delays.

RESULTS

Gradient delay artifacts are largely eliminated, requiring very little additional scanning time. The sampling efficiency of the spiral read-out allows for a significant reduction of the acquisition time in comparison to Cartesian TAPIR. Spiral TAPIR enables the sampling of more slices and an accurate measurement of rapidly relaxing compartments. Over a wide T₁ range (448-3115 ms), spiral TAPIR reduces the mean fitting error from -2.5% to -0.1%. Combining 50% undersampling with the shorter TR of spiral TAPIR, an increase in imaging speed by a factor of up to 3.3 was achieved.

CONCLUSION

Using a spiral read-out trajectory, the established TAPIR sequence enables measurement of rapidly relaxing T₁ compartments, while improving T₁ mapping performance and imaging speed.

Assessing effects of scanner upgrades for clinical studies

BACKGROUND

Scanner upgrades due to software and hardware changes are an inevitable part of MR research and, without quality assurance protocols, can jeopardize studies.

PURPOSE

To evaluate changes in T₁ relaxation time by inversion recovery (IR) and variable flip angle (VFA) measurements on a 3T system that underwent an "everything but the magnet" upgrade.

STUDY TYPE

Longitudinal.

PHANTOM

An International Society of Magnetic Resonance in Medicine / National Institute of Standards and Technology (ISMRM/NIST) system phantom with repeated measurements across multiple (n = 3) days.

FIELD STRENGTH/SEQUENCE

T₁ IR, VFA at 3T.

ASSESSMENT

The T₁ measurements by IR and VFA were compared with the nuclear magnetic resonance (NMR) measurements, which constitute the known values for the ISMRM/NIST system phantom, to determine the measurement error.

STATISTICAL TESTS

Descriptive.

RESULTS

The T₁ VFA measurement errors were distributed around zero (-15% to +10%) on the original system and then the errors were distributed entirely above zero post-upgrade (+5% to 30%). The T₁ IR results had a dramatic increase in error distribution (±5% original, ±20% post-upgrade) prior to the identification of signal saturation as an issue. Once the signal saturation was accounted for, the T₁ IR errors decreased to ±10% post-upgrade.

DATA CONCLUSION

The T₁ VFA measurement differences between the original and post-upgrade systems can be entirely attributed to contributions from B₁ . The T₁ IR measurements demonstrate the need for quantitative quality assurance (QA) processes. The site under study passed the QA protocols in place, which did not identify the increased T₁ error, nor the signal saturation issue. To improve on this study, we would make systematic, quantitative measurements at intervals less than a year and following any hardware or software upgrade.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1948-1954.

Repetitive MRI of organs at risk in head and neck cancer patients undergoing radiotherapy

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First review on MRI changes in head and neck organs at risk during radiotherapy.

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Focus on dynamics in salivary gland, muscle and bone in the head and neck region.

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Pointing out the limitations in implementing MRI in guiding radiation therapy.

With emerging technical advances like real-time MR imaging during radiotherapy (RT) with an integrated MR linear accelerator, it will soon be possible to analyze changes in the organs at risk (OARs) during radiotherapy without additional effort for the patients. Until then, patients have to undergo additional MR imaging and often without the same immobilization devices as used for radiotherapy. Consequently, studies with repetitive MRI during the course of radiotherapy are rare, with low patient numbers and with the challenge of registration between the different MR sequences and the varying imaging time points.

This review focuses on studies with at least two MRIs, one before and another either during or post-RT, in order to report on RT-induced changes in normal tissues and their correlation with toxicity. We therefore included clinical studies published in English until March 2019, with repetitive MRI of OARs in head and neck cancer patients receiving external beam radiotherapy. OARs analyzed were salivary glands, musculoskeletal structures and bones. MR sequences used included T1, T2, dynamic contrast enhanced (DCE) imaging, diffusion-weighted imaging (DWI), DIXON and MR sialography.