Magnetic Resonance Materials in Physics, Biology and Medicine最新文献

Objective: Dynamic MRI synchronised with neuromuscular electrical stimulation (NMES) offers a reproducible method for assessing muscle activity but requires MRI-compatible force sensors to correlate quantitative muscle dynamics parameters with muscle force output. Most available sensors are expensive, rely on non-free software or are MR-incompatible This work presents an open-source, low-cost, MR-compatible grip force sensor as a viable alternative to commercial devices.

Materials and methods: Phantom measurements were performed with and without the sensor at a 3T MRI to assess the MRI compatibility and its impact on image quality, field homogeneity and signal-to-noise ratio (SNR). Furthermore, the force sensor was integrated into a dynamic MRI setup with NMES and applied in vivo to four subjects.

Results: The force sensor demonstrated good compatibility with a 3 T MRI scanner, exhibiting minimal SNR reduction and minimal increase in B₀ inhomogeneities in phantom measurements. During dynamic MRI with NMES, a 2D in-plane phase-contrast MRI sequence successfully retrieved the muscle's velocity field, proving effective for dynamic MRI applications, while preserving image quality.

Discussion: The design of the force sensor, building instructions and software are publicly released as open source. This allows the proposed sensor to be adapted in multiple applications where grip force needs to be recorded in an MR scanner.

Purpose: To enable accelerated Bloch simulations by enhancing the open-source multi-purpose MRI simulation tool JEMRIS with graphic processing units (GPU) parallelization.

Methods: A GPU-compatible version of JEMRIS was built by shifting the computationally expensive parallelizable processes to the GPU to benefit from heterogeneous computing and by adding asynchronous communication and mixed precision support. With key classes reimplemented in CUDA C++, the developed GPU-JEMRIS framework was tested on simulations of common MRI artifacts in numerical phantoms. The accuracy and performance of the GPU-parallelized JEMRIS simulator were benchmarked against the CPU-parallelized JEMRIS and GPU-enabled KomaMRI.jl simulators. Additionally, an example of liver fat quantification errors due to respiratory motion artifacts was simulated in a multi-echo gradient echo (MEGRE) acquisition.

Results: The GPU-accelerated JEMRIS achieved speed-up factors 3-12 and 7-65 using double and single precision numerical integrators, respectively, when compared to the parallelized CPU implementation in the investigated numerical phantom scenarios. While double precision GPU simulations negligibly differ (<0.1% NRMSE) from double precision CPU simulations, the single precision simulations still present small errors of up to 1% k-space signal NRMSE. The developed a GPU extension enabled computationally demanding motion simulations with a multi-species abdominal phantom and a MEGRE sequence, showing significant and spatially varying fat fraction bias in the presence of motion.

Conclusion: By solving the Bloch equations in parallel on device, accelerated Bloch simulations can be performed on any GPU-equipped device with CUDA support using the developed GPU-JEMRIS. This would enable further insights into more realistic large spin pool MR simulations such as experiments with large multi-dimensional phantoms, multiple chemical species and dynamic effects.

Purpose: Cardiac magnetic resonance fingerprinting (cMRF) is a powerful quantitative imaging technique that provides multi-parametric diagnostic information. Here, we introduce an open-source framework for cardiac MRF including open-source pulse sequences, image reconstruction, and parameter estimation tools that are needed for the processing of the data.

Methods: A 2D cMRF sequence with a variable-density spiral readout is implemented using the open-source and vendor-agnostic sequence format Pulseq. Cardiac triggering is used to synchronize acquisition with the rest period of the heart. $T_1$ inversion and $T_2$ preparation pulses are added to ensure accurate parameter estimation. Data acquisition is carried out over 15 heartbeats. The images showing the signal changes over time are reconstructed and matched to a pre-calculated signal dictionary. In addition to the cMRF sequence, spin-echo reference sequences for quality control in phantoms are provided. The method is evaluated in phantom experiments using a T1MES phantom on four different scanners. In vivo experiments were performed to compare the open-source cMRF sequence with a vendor-specific cMRF sequence and clinical sequences used for $T_1$ and $T_2$ mapping of the heart. Three volunteers were imaged on two different scanners.

Results: The error of $T_1$ and $T_2$ over all tissue types present in the T1MES phantom was comparable between all four scanners and on average 4.50 ± 2.48%. $T_1$ and $T_2$ maps obtained in vivo were comparable between the open-source and vendor-specific implementation of cMRF.

Conclusion: The proposed open-source cMRF implementation enables accurate parameter estimation across multiple different scanners. Sequence files, image reconstruction, and parameter estimation scripts are available for reproducible quantitative MRI.

Objective: To validate the automated analysis of magnetic resonance imaging (MRI) diffusion phantoms with an updated version of the magnetic resonance biomarker assessment software (MR-BIAS), an open-source tool initially developed for the analysis of MRI relaxometry phantoms.

Materials and methods: The updated MR-BIAS was validated against two published diffusion weighted MRI datasets: (i) a single-site study (n = 48) was used for validation of apparent diffusion coefficients (ADC) and to identify optimal region of interest (ROI) selection, and (ii) a multi-centre multi-vendor study including diffusion imaging from a shared benchmark protocol (n = 49) and site-specific protocols (n = 43). ADC analysis compared both datasets with ROIs manually matched to the original studies, and with automatically detected optimal ROIs.

Results: MR-BIAS ADC values were statistically equivalent (p < 0.05) to original studies within tolerances (manual ROI, automatic ROI) for the single-site study (± 0.01, ± 6 μm²/s) and for the multi-vendor study for benchmark (± 4, ± 7 μm²/s) and site-specific (± 3, ± 6 μm²/s) protocols. The optimal ROI was a central cylinder (height = 10mm, diameter = 10mm). MR-BIAS ADC summary metrics were comparable to those of the original studies.

Discussion: MR-BIAS can automatically and accurately perform ADC analysis of diffusion phantoms, making the software suitable for the quality assurance of multi-centre studies of multi-parametric MRI.

Objective: To assess multi-site and multi-vendor accuracy, and intra- and inter-scanner variability of T1 and T2 measurements using the ISMRM/NIST System MRI phantom at room temperature.

Materials and methods: T1 and T2 measurements were acquired using standardized NIST protocols on 13 scanners (1.5 T and 3 T) from 3 vendors at 7 sites and compared with reference values at room temperature. Pearson's correlation (r) and accuracy error were used for comparison with reference values, while inter-scanner agreement was assessed using the coefficient of variation (CV%). Short-term reproducibility was evaluated using Bland-Altman plots and precision error. Generalized linear mixed models and post hoc tests (α = 0.05) were adopted to compare accuracy and precision across field strengths, vendors, and scanners. T2 measurements were corrected with StimFit toolbox for stimulated echo compensation.

Results: T1 and T2 measurements had excellent correlation with reference values at both field strengths. Stimfit significantly improved T2 accuracy in the renal range for 9 of 13 scanners. Short-term reproducibility (limits of agreement < 10%) and inter-scanner agreement were good (median CV < 7%) for both T1 and T2 values. Inter-scanner CV was < 5% in the renal range for both parameters.

Discussion: These findings support the need of scanner evaluation processes to ensure reliable T1-T2 measurements in multi-center MRI studies.

Objectives: By rapidly changing magnetic field strength between 0.2 and 200 mT during the pulse sequence Field-Cycling Imaging (FCI) makes it possible to identify and evaluate new quantitative markers of pathology derived from dispersion of spin-lattice relaxation rate (R₁) in vivo. The aim of this work was to determine the most effective approach to reliably estimate multi-field R₁ dispersion measurements in brain tissue using FCI.

Materials and methods: This repeatability study consisted of twenty participants with moderate or severe small vessel disease. Each participant underwent 3 T MRI and FCI scans, repeated 30 days apart. After R₁ maps were generated at 0.2, 2, 20, and 200 mT, co-registered tissue labels generated from 3 T MRI were used to extract tissue averaged values of R₁ dispersion from regions of white matter (WM) and WM hyperintensities (WMHs).

Results: The fitted model which yielded best overall image contrast between WM and WMH regions and R₁ dispersion model adherence was determined. Tissue averaged values of R₁ (0.2 mT) and R₁ dispersion slope exhibited Cohen's d effect sizes of 3.07 and 1.48, respectively, between regions of WM and WMH. The cohort study results were repeatable between study visits.

Discussion: Differences in R₁ measurements could repeatably be discerned between normal and abnormal appearing brain tissues.

Objective: This work presents an automated quality control (QC) system within quantitative MRI (qMRI) workflows. By leveraging the ISMRM/NIST quantitative MRI system phantom, we establish an open-source pipeline for rapid, repeatable, and accurate validation and stability tracking of sequence quantification performance across diverse clinical settings.

Materials and methods: A microservice-based QC system for automated vial segmentation from quantitative maps was developed and tested across various MRF acquisition and protocol designs, with reports generated and returned to the scanner in real time.

Results: The system demonstrated consistent and repeatable value segmentation and reporting, successfully extracted all 252 T1 and T2 vial samples tested. Values extracted from the same sequence were found to be repeatable with 0.09% ± 1.23% and - 0.26% ± 2.68% intersession error, respectively.

Discussion: By providing real-time quantification performance assessment, this easily deployable automated QC approach streamlines sequence validation and long-term performance monitoring, vital for the broader acceptance of qMRI as a standard component of clinical protocols.