Quantitative MRI (qMRI) offers significant advantages over weighted images by providing objective parameters related to tissue properties. Deep learning-based methods have demonstrated effectiveness in estimating quantitative maps from series of weighted images. In this study, we present qMRI Diffuser, a novel approach to qMRI utilising deep generative models. Specifically, we implemented denoising diffusion probabilistic models (DDPM) for T1 quantification in the brain, framing the estimation of quantitative maps as a conditional generation task. The proposed method is compared with the residual neural network (ResNet) and the recurrent inference machine (RIM) on both phantom and in vivo data. The results indicate that our method achieves improved accuracy and precision in parameter estimation, along with superior visual performance. Moreover, our method inherently incorporates stochasticity, enabling straightforward quantification of uncertainty. Hence, the proposed method holds significant promise for quantitative MR mapping.@en

Purpose
To harmonize the use of color for MR relaxometry maps and therefore recommend the use of specific color-maps for representing T1, T2, and T2* maps and their inverses.

Methods
Perceptually linearized color-maps were chosen to have similar color settings as those proposed by Griswold et al. in 2018. A Delphi process, polling the opinion of a panel of 81 experts, was used to generate consensus on the suitability of these maps.

Results
Consensus was reached on the suitability of the logarithm-processed Lipari color-map for T1 and the logarithm-processed Navia color-map for T2 and T2*. There was consensus on color bars being mandatory and on the use of a specific value indicating “invalidity.” There was no consensus on whether the ranges should be fixed per anatomy.

Conclusion
The authors recommend the use of the logarithm-processed Lipari color-map for displaying quantitative T1 maps and R1 maps; likewise, the authors recommend the logarithm-processed Navia color-map for displaying T2, T2*, R2, and R2* maps.

This work originated with the Quantitative MR Study Group of the International Society of Magnetic Resonance in Medicine (ISMRM); it has the approval of the Publication Committee and of the Board of the ISMRM.@en

Magnetic resonance thermometry (MRT) can measure in-vivo 3D-temperature changes in real-time and noninvasively. However, for the oropharynx region and the entire head and neck, motion potentially introduces large artifacts. Considering long treatment times of 60–90 min, this study aims to evaluate whether MRT around the oropharynx is clinically feasible for hyperthermia treatments and quantify the effects of breathing and swallowing on MRT performance. A 3D-ME-FGRE sequence was used in a phantom cooling down and around the oropharynx of five volunteers over ∼75 min. The imaging protocol consisted of imaging with acceleration (ARC = 2), number of image averages (NEX = 1,2 and 3). For volunteers, the acquisitions included a breath-hold scan and scans with deliberate swallowing. MRT performance was quantified in neck muscle, spinal cord and masseter muscle, using mean average error (MAE), mean error (ME) and spatial standard deviation (SD). In phantom, an increase in NEX leads to a significant decrease in SD, but MAE and ME were unchanged. No significant difference was found in volunteers between the different scans. There was a significant difference between the regions evaluated: neck muscle had the best MAE (=1.96 °C) and SD (=0.82 °C), followed by spinal cord (MAE = 3.17 °C, SD = 0.92 °C) and masseter muscle (MAE = 4.53 °C, SD = 1.16 °C). Concerning the ME, spinal cord did best, then neck muscle and masseter muscle, with values of −0.64 °C, 1.15 °C and −3.05 °C respectively. Breathing, swallowing, and different ways of imaging (acceleration and NEX) do not significantly influence the MRT performance in the oropharynx region. The ROI selected however, leads to significant differences.

@en

T₂-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We applied a fast, multi-parametric quantitative MRI approach and used a multi-component MR Fingerprinting (MC-MRF) analysis. We assessed the differences in the MRF component representing prolongedrelaxation time between patients with MS and controls and studied the relation between this component's volume and structural white matter damage identified on FLAIR MRI scans in patients with MS. A total of 48 MS patients at two different sites and 12 healthy controls were scanned with FLAIR and MRF-EPI MRI scans. MRF scans were analyzed with a joint-sparsity multi-component analysis to obtain magnetization fraction maps of different components, representing tissues such as myelin water, white matter, gray matter and cerebrospinal fluid. In the MS patients, an additional component was identified with increased transverse relaxation times compared to the white matter, likely representing changes in free water content. Patients with MS had a higher volume of the long- component in the white matter of the brain compared to healthy controls (B (95%-CI) = 0.004 (0.0006–0.008), p = 0.02). Furthermore, this MRF component had a moderate correlation (correlation coefficient R 0.47) with visible structural white matter changes on the FLAIR scans. Also, the component was found to be more extensive compared to structural white matter changes in 73% of MS patients. In conclusion, our MRF acquisition and analysis captured white matter tissue changes in MS patients compared to controls. In patients these tissue changes were more extensive compared to visually detectable white matter changes on FLAIR scans. Our method provides a novel way to quantify the extent of white matter changes in MS patients, which is underestimated using only conventional clinical MRI scans.

@en

Characterization of tumor microvasculature is important in tumor assessment and studying treatment response. This is possible by acquiring vascular biomarkers with magnetic resonance imaging (MRI) based on dynamic susceptibility contrast (DSC). We propose magnetic resonance vascular fingerprinting (MRVF) for hybrid echo planar imaging (HEPI) acquired during the first passage of the contrast agent (CA). The proposed approach was evaluated in patients with gliomas, and we simultaneously estimated vessel radius and relative cerebral blood volume. These parameters were also compared to the respective values estimated using the previously introduced vessel size imaging (VSI) technique. The results of both methods were found to be consistent. MRVF was also found to be robust to noise in the estimation of the parameters. DSC-HEPI-based MRVF provides characterization of microvasculature in gliomas with a short acquisition time and can be further improved in several ways to increase our understanding of tumor physiology.

@en

Purpose: MR thermometry (MRT) enables noninvasive temperature monitoring during hyperthermia treatments. MRT is already clinically applied for hyperthermia treatments in the abdomen and extremities, and devices for the head are under development. In order to optimally exploit MRT in all anatomical regions, the best sequence setup and post-processing must be selected, and the accuracy needs to be demonstrated. Methods: MRT performance of the traditionally used double-echo gradient-echo sequence (DE-GRE, 2 echoes, 2D) was compared to multi-echo sequences: a 2D fast gradient-echo (ME-FGRE, 11 echoes) and a 3D fast gradient-echo sequence (3D-ME-FGRE, 11 echoes). The different methods were assessed on a 1.5 T MR scanner (GE Healthcare) using a phantom cooling down from 59 °C to 34 °C and unheated brains of 10 volunteers. In-plane motion of volunteers was compensated by rigid body image registration. For the ME sequences, the off-resonance frequency was calculated using a multi-peak fitting tool. To correct for B0 drift, the internal body fat was selected automatically using water/fat density maps. Results: The accuracy of the best performing 3D-ME-FGRE sequence was 0.20 °C in phantom (in the clinical temperature range) and 0.75 °C in volunteers, compared to DE-GRE values of 0.37 °C and 1.96 °C, respectively. Conclusion: For hyperthermia applications, where accuracy is more important than resolution or scan-time, the 3D-ME-FGRE sequence is deemed the most promising candidate. Beyond its convincing MRT performance, the ME nature enables automatic selection of internal body fat for B0 drift correction, an important feature for clinical application.

@en