Low-rank iterative infilling for zero echo-time (ZTE) imaging.

PURPOSE: A new referenceless low-rank reconstruction technique has been introduced to address the issue of missing samples within the Zero Echo Time (ZTE) dead-time gap. METHODS: The proposed method reformulates the in-filling of the missing samples as an inverse problem subject to low-rank constraints. Its performance and robustness are evaluated through a comparative analysis that combines Monte Carlo computational simulations and data obtained from in vivo experiments. RESULTS: The proposed method is tested for dead-time gaps ranging up to 4.5 Nyquist dwells, across signal-to-noise ratio levels of 5, 10, 15, and 20 dB. Consistently superior performance is observed across all cases compared to algebraic and parallel imaging methods. The speed for convergence decreases exponentially as the dead-time gap expands. CONCLUSION: The proposed method enables artifact-free reconstruction up to dead-time gap of 4 Nyquist dwells and thereby supports ZTE imaging up to an imaging bandwidth of ± 41 . 67 $$ \pm 41.67 $$ kHz (assuming transmit and receive switching less than 30 µ $$ \mu $$ s). It demonstrates superior performance compared to algebraic and parallel imaging methods.

Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction.

PURPOSE: To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized 129Xe lung ventilation MRI. METHODS: 129Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VHI) were compared between each set of DL-reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher-order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural-abundance xenon MRI was evaluated in healthy volunteers. RESULTS: 129Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL-reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL-reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that 129Xe ventilation imaging using natural-abundance xenon appears feasible. CONCLUSION: DL-based image reconstruction significantly improves 129Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost-effective 129Xe ventilation imaging with natural-abundance xenon in the future.

Robustness of MR Elastography in the Healthy Brain: Repeatability, Reliability, and Effect of Different Reconstruction Methods.

BACKGROUND: Changes in brain stiffness can be an important biomarker for neurological disease. Magnetic resonance elastography (MRE) quantifies tissue stiffness, but the results vary between acquisition and reconstruction methods. PURPOSE: To measure MRE repeatability and estimate the effect of different reconstruction methods and varying data quality on estimated brain stiffness. STUDY TYPE: Prospective. SUBJECTS: Fifteen healthy subjects. FIELD STRENGTH/SEQUENCE: 3T MRI, gradient-echo elastography sequence with a 50 Hz vibration frequency. ASSESSMENT: Imaging was performed twice in each subject. Images were reconstructed using a curl-based and a finite-element-model (FEM)-based method. Stiffness was measured in the whole brain, in white matter, and in four cortical and four deep gray matter regions. Repeatability coefficients (RC), intraclass correlation coefficients (ICC), and coefficients of variation (CV) were calculated. MRE data quality was quantified by the ratio between shear waves and compressional waves. STATISTICAL TESTS: Median values with range are presented. Reconstruction methods were compared using paired Wilcoxon signed-rank tests, and Spearman's rank correlation was calculated between MRE data quality and stiffness. Holm-Bonferroni corrections were employed to adjust for multiple comparisons. RESULTS: In the whole brain, CV was 4.3% and 3.8% for the curl and the FEM reconstruction, respectively, with 4.0-12.8% for subregions. Whole-brain ICC was 0.60-0.74, ranging from 0.20 to 0.89 in different regions. RC for the whole brain was 0.14 kPa and 0.17 kPa for the curl and FEM methods, respectively. FEM reconstruction resulted in 39% higher stiffness than the curl reconstruction (P < 0.05). MRE data quality, defined as shear-compression wave ratio, was higher in peripheral regions than in central regions of the brain (P < 0.05). No significant correlations were observed between MRE data quality and stiffness estimates. DATA CONCLUSION: MRE of the human brain is a robust technique in terms of repeatability. Caution is warranted when comparing stiffness values obtained with different techniques. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Prospective Clinical Implementation of a Novel Magnetic Resonance Tracking Device for Real-Time Brachytherapy Catheter Positioning.

PURPOSE: We designed and built dedicated active magnetic resonance (MR)-tracked (MRTR) stylets. We explored the role of MRTR in a prospective clinical trial. METHODS AND MATERIALS: Eleven gynecologic cancer patients underwent MRTR to rapidly optimize interstitial catheter placement. MRTR catheter tip location and orientation were computed and overlaid on images displayed on in-room monitors at rates of 6 to 16 frames per second. Three modes of actively tracked navigation were analyzed: coarse navigation to the approximate region around the tumor; fine-tuning, bringing the stylets to the desired location; and pullback, with MRTR stylets rapidly withdrawn from within the catheters, providing catheter trajectories for radiation treatment planning (RTP). Catheters with conventional stylets were inserted, forming baseline locations. MRTR stylets were substituted, and catheter navigation was performed by a clinician working inside the MRI bore, using monitor feedback. RESULTS: Coarse navigation allowed repositioning of the MRTR catheters tips by 16 mm (mean), relative to baseline, in 14 ± 5 s/catheter (mean ± standard deviation [SD]). The fine-tuning mode repositioned the catheter tips by a further 12 mm, in 24 ± 17 s/catheter. Pullback mode provided catheter trajectories with RTP point resolution of ∼1.5 mm, in 1 to 9 s/catheter. CONCLUSIONS: MRTR-based navigation resulted in rapid and optimal placement of interstitial brachytherapy catheters. Catheters were repositioned compared with the initial insertion without tracking. In pullback mode, catheter trajectories matched computed tomographic precision, enabling their use for RTP.