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Purpose: To improve image quality, mitigate quantification biases and variations for free‐breathing liver proton density fat fraction (PDFF) and R2*$$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k‐space undersampling. Methods: A free‐breathing multi‐echo stack‐of‐radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath‐hold Cartesian acquisitions. Images, PDFF, and R2*$$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k‐space sampling factors and reconstruction settings. Results were compared with reference‐standard results using Bland–Altman analysis. Using linear mixed‐effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF and R2*$$ {\mathrm{R}}_2^{\ast } $$, respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. Results: Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [−0.5; −33.6, 32.7] s−1 for R2*$$ {\mathrm{R}}_2^{\ast } $$ and [−1.0%; −5.8%, 3.8%] for PDFF, close to those of a previous self‐gating method using fully sampled radial views: [−0.1; −27.1, 27.0] s−1 for R2*$$ {\mathrm{R}}_2^{\ast } $$ and [−0.4%; −4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. Conclusion: This proposed method may allow accelerated free‐breathing liver PDFF and R2*$$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate a novel MR elastography (MRE) technique, termed here wavelet MRE. With this technique, broadband motion sensitivity is achievable. Moreover, the true tissue displacement can be reconstructed with a simple inverse transform. Methods: A wavelet MRE sequence was developed with motion‐encoding gradients based on Haar wavelets. From the phase images' displacement was estimated using an inverse transform. Simulations were performed using a frequency sweep and a transient as ground‐truth motions. A PVC phantom was scanned using wavelet MRE and standard MRE with both transient (one and 10 cycles of 90‐Hz motion) and steady‐state dual‐frequency motion (30 and 60 Hz) for comparison. The technique was tested in a human brain, and motion trajectories were estimated for each voxel. Results: In simulation, the displacement information estimated from wavelet MRE closely matched the true motion. In the phantom test, the MRE phase data generated from the displacement information derived from wavelet MRE agreed well with standard MRE data. Testing of wavelet MRE to assess transient motion waveforms in the brain was successful, and the tissue motion observed was consistent with a previous study. Conclusion: The uniform and broadband frequency response of wavelet MRE makes it a promising method for imaging transient, multifrequency motion, or motion with unknown frequency content. One potential application is measuring the response of brain tissue undergoing low‐amplitude, transient vibrations as a model for the study of traumatic brain injury. [ABSTRACT FROM AUTHOR]

Background: The T2w sequence is a standard component of a prostate MRI examination; however, it is time‐consuming, requiring multiple signal averages to achieve acceptable image quality. Purpose/Hypothesis: To determine whether a denoised, single‐average T2 sequence (T2‐R) is noninferior to the standard multiaverage T2 sequence (T2‐S) in terms of lesion detection and PI‐RADS score assessment. Study Type: Retrospective. Population: A total of 45 males (age range 60–75 years) who underwent clinically indicated prostate MRI examinations, 21 of whom had pathologically proven prostate cancer. Field Strength/Sequence: A 3 T; T2w FSE, DWI with ADC maps, and dynamic contrast‐enhanced images with color‐coded perfusion maps. T2‐R images were created from the raw data utilizing a single "average" with iterative denoising. Assessment: Nine readers randomly assessed complete exams including T2‐R and T2‐S images in separate sessions. PI‐RADS version 2.1 was used. All readers then compared the T2‐R and T2‐S images side by side to evaluate subjective preference. An additional detailed image quality assessment was performed by three senior level readers. Statistical Tests: Generalized linear mixed effects models for differences in lesion detection, image quality features, and overall preference between T2‐R and T2‐S sequences. Intraclass correlation coefficients (ICC) were used to assess reader agreement for all comparisons. A significance threshold of P = 0.05 was used for all statistical tests. Results: There was no significant difference between sequences regarding identification of lesions with PI‐RADS ≥3 (P = 0.10) or PI‐RADS score (P = 0.77). Reader agreement was excellent for lesion identification (ICC = 0.84). There was no significant overall preference between the two sequences regarding image quality (P = 0.07, 95% CI: [−0.23, 0.01]). Reader agreement was good regarding sequence preference (ICC = 0.62). Data Conclusion: Use of single‐average, denoised T2‐weighted images was noninferior in prostate lesion detection or PI‐RADS scoring when compared to standard multiaverage T2‐weighted images. Evidence Level: 3. Technical Efficacy: Stage 3. [ABSTRACT FROM AUTHOR]

To investigate the impact of a prototypical deep learning–based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBE SR) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBE SD). Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBE SD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBE SR and VIBE SD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test. A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBE SR , breath-hold time was significantly reduced by approximately 13.6% (VIBE SR 11.9 ± 1.2 seconds vs. VIBE SD : 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBE SR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBE SR and VIBE SD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBE SR than in VIBE SD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBE SD and VIBE SR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBE SD and VIBE SR. The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique. [ABSTRACT FROM AUTHOR]