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Purpose: Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in‐plane resolution but typically has long TEs. Half‐sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half‐sinc acquisitions while maintaining SNR and TSC. Methods: Ex vivo and in vivo TSC were compared between 2D spiral sequences with full‐sinc (TE = 0.73 ms, scan time ≈ 5 min) and half‐sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half‐sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. Results: TSC was significantly higher from half‐sinc than from full‐sinc acquisitions, ex vivo and in vivo. Ex vivo, half‐sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half‐sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. Conclusion: 2D sodium MRI with half‐sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins. [ABSTRACT FROM AUTHOR]

Tumors have a greater reliance on anaerobic glycolysis for energy production than normal tissues. We developed a noninvasive method for imaging glucose uptake in vivo that is based on magnetic [...]

Intra-arterial Digital Subtraction Angiography (DSA) is the gold standard technique for radiosurgery target delineation in brain Arterio-Venous Malformations (AVMs). This study aims to evaluate whether a combination of three Magnetic Resonance Angiography sequences (triple-MRA) could be used for delineation of brain AVMs for Gamma Knife Radiosurgery (GKR). Fifteen patients undergoing DSA for GKR targeting of brain AVMs also underwent triple-MRA: 4D Arterial Spin Labelling based angiography (ASL-MRA), Contrast-Enhanced Time-Resolved MRA (CE-MRA) and High Definition post-contrast Time-Of-Flight angiography (HD-TOF). The arterial phase of the AVM nidus was delineated on triple-MRA by an interventional neuroradiologist and a consultant neurosurgeon (triple-MRA volume). Triple-MRA volumes were compared to AVM targets delineated by the clinical team for delivery of GKR using the current planning paradigm, i.e., stereotactic DSA and volumetric MRI (DSA volume). Difference in size, degree of inclusion (DI) and concordance index (CcI) between DSA and triple-MRA volumes are reported. AVM target volumes delineated on triple-MRA were on average 9.8% smaller than DSA volumes (95%CI:5.6–13.9%; SD:7.14%; p =.003). DI of DSA volume in triple-MRA volume was on average 73.5% (95%CI:71.2–76; range: 65–80%). The mean percentage of triple-MRA volume not included on DSA volume was 18% (95%CI:14.7–21.3; range: 7–30%). The technical feasibility of using triple-MRA for visualisation and delineation of brain AVMs for GKR planning has been demonstrated. Tighter and more precise delineation of AVM target volumes could be achieved by using triple-MRA for radiosurgery targeting. However, further research is required to ascertain the impact this may have in obliteration rates and side effects. [ABSTRACT FROM AUTHOR]

Purpose: 23 Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the inherently low 23 Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as compressed sensing (CS) have been proposed to mitigate low signal-to-noise ratio (SNR); although, these can result in unnatural images, suboptimal denoising and long processing times. Recently, machine learning has been increasingly used to denoise 1 H MRI acquisitions; however, this approach typically requires large volumes of high-quality training data, which is not readily available for 23 Na MRI. Here, we propose using 1 H data to train a denoising convolutional neural network (CNN), which we subsequently demonstrate on prospective 23 Na images of the calf., Methods: 1893 1 H fat-saturated transverse slices of the knee from the open-source fastMRI dataset were used to train denoising CNNs for different levels of noise. Synthetic low SNR images were generated by adding gaussian noise to the high-quality 1 H k-space data before reconstruction to create paired training data. For prospective testing, 23 Na images of the calf were acquired in 10 healthy volunteers with a total of 150 averages over ten minutes, which were used as a reference throughout the study. From this data, images with fewer averages were retrospectively reconstructed using a non-uniform fast Fourier transform (NUFFT) as well as CS, with the NUFFT images subsequently denoised using the trained CNN., Results: CNNs were successfully applied to 23 Na images reconstructed with 50, 40 and 30 averages. Muscle and skin apparent TSC quantification from CNN-denoised images were equivalent to those from CS images, with <0.9 mM bias compared to reference values. Estimated SNR was significantly higher in CNN-denoised images compared to NUFFT, CS and reference images. Quantitative edge sharpness was equivalent for all images. For subjective image quality ranking, CNN-denoised images ranked equally best with reference images and significantly better than NUFFT and CS images., Conclusion: Denoising CNNs trained on 1 H data can be successfully applied to 23 Na images of the calf; thus, allowing scan time to be reduced from ten minutes to two minutes with little impact on image quality or apparent TSC quantification accuracy., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)