Background: Magnetic resonance imaging (MRI) is used extensively to quantify myelin content, however computational bottlenecks remain challenging for advanced imaging techniques in clinical settings. We present a fast, open-source toolkit for processing quantitative magnetization transfer derived from selective inversion recovery (SIR) acquisitions that allows parameter map estimation, including the myelin-sensitive macromolecular pool size ratio (PSR). Significant progress has been made in reducing SIR acquisition times to improve clinically feasibility. However, parameter map estimation from the resulting data remains computationally expensive. To overcome this computational limitation, we developed a computationally efficient, open-source toolkit implemented in the Julia language. Methods: To test the accuracy of this toolkit, we simulated SIR images with varying PSR and spin-lattice relaxation time of the free water pool (R1f) over a physiologically meaningful scale from 5 to 20% and 0.5 to 1.5 s-1, respectively. Rician noise was then added, and the parameter maps were estimated using our Julia toolkit. Probability density histogram plots and Lin's concordance correlation coefficients (LCCC) were used to assess accuracy and precision of the fits to our known simulation data. To further mimic biological tissue, we generated five cross-linked bovine serum albumin (BSA) phantoms with concentrations that ranged from 1.25 to 20%. The phantoms were imaged at 3T using SIR, and data were fit to estimate PSR and R1f. Similarly, a healthy volunteer was imaged at 3T, and SIR parameter maps were estimated to demonstrate the reduced computational time for a real-world clinical example. Results: Estimated SIR parameter maps from our Julia toolkit agreed with simulated values (LCCC> 0.98). This toolkit was further validated using BSA phantoms and a whole brain scan at 3T. In both cases, SIR parameter estimates were consistent with published values using MATLAB. However, compared to earlier work using MATLAB, our Julia toolkit provided an approximate 20-fold reduction in computational time. Conclusions: Presented here, we developed a fast, open-source, toolkit for rapid and accurate SIR MRI using Julia. The reduction in computational cost should allow SIR parameters to be accessible in clinical settings.
Background Magnetic resonance imaging (MRI) is used extensively to quantify myelin content, however computational bottlenecks remain challenging for advanced imaging techniques in clinical settings. We present a fast, open-source toolkit for processing quantitative magnetization transfer derived from selective inversion recovery (SIR) acquisitions that allows parameter map estimation, including the myelin-sensitive macromolecular pool size ratio (PSR). Significant progress has been made in reducing SIR acquisition times to improve clinically feasibility. However, parameter map estimation from the resulting data remains computationally expensive. To overcome this computational limitation, we developed a computationally efficient, open-source toolkit implemented in the Julia language. Methods To test the accuracy of this toolkit, we simulated SIR images with varying PSR and spin-lattice relaxation time of the free water pool (R1f) over a physiologically meaningful scale from 5% to 20% and 0.5 to 1.5 s−1, respectively. Rician noise was then added, and the parameter maps were estimated using our Julia toolkit. Probability density histogram plots and Lin’s concordance correlation coefficients (LCCC) were used to assess accuracy and precision of the fits to our known simulation data. To further mimic biological tissue, we generated five cross-linked bovine serum albumin (BSA) phantoms with concentrations that ranged from 1.25% to 20%. The phantoms were imaged at 3T using SIR, and data were fit to estimate PSR and R1f. Similarly, a healthy volunteer was imaged at 3T, and SIR parameter maps were estimated to demonstrate the reduced computational time for a real-world clinical example. Results Estimated SIR parameter maps from our Julia toolkit agreed with simulated values (LCCC > 0.98). This toolkit was further validated using BSA phantoms and a whole brain scan at 3T. In both cases, SIR parameter estimates were consistent with published values using MATLAB. However, compared to earlier work using MATLAB, our Julia toolkit provided an approximate 20-fold reduction in computational time. Conclusions Presented here, we developed a fast, open-source, toolkit for rapid and accurate SIR MRI using Julia. The reduction in computational cost should allow SIR parameters to be accessible in clinical settings.
Purpose Quantitative magnetization transfer (QMT) using selective inversion recovery (SIR) can quantify the macromolecular‐to‐free proton pool size ratio (PSR), which has been shown to relate closely with myelin content. Currently clinical applications of SIR have been hampered by long scan times. In this work, the acceleration of SIR‐QMT using CS‐SENSE (compressed sensing SENSE) was systematically studied. Theory and Methods Phantoms of varied concentrations of bovine serum albumin and human scans were first conducted to evaluate the SNR, precision of SIR‐QMT parameters, and scan time. Based on these results, an optimized CS‐SENSE factor of 8 was determined and the test–retest repeatability was further investigated. Results A whole‐brain SIR imaging of 6 min can be achieved. Bland–Altman analyses indicated excellent agreement between the test and retest sessions with a difference in mean PSR of 0.06% (and a difference in mean R1f of −0.001 s−1). In addition, the assessment of the intraclass correlation coefficient (ICC) revealed high reliability in nearly all the white matter and gray matter regions. In white matter regions, the ICC was 0.93 (95% confidence interval [CI]: 0.88–0.96, p < 0.001) for PSR, and 0.90 (95% CI: 0.83–0.94, p < 0.001) for R1f. In gray matter, ICC was 0.84 (95% CI: 0.66–0.93, p < 0.001) in PSR, and 0.98 (95% CI: 0.95–0.99, p < 0.001) for R1f. The method also showed excellent capability to detect focal lesions in multiple sclerosis. Conclusion Rapid, reliable, and sensitive whole‐brain SIR imaging can be achieved using CS‐SENSE, which is expected to significantly promote widespread clinical translation.
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