Purpose

To compare performance of sequential and Hadamard-encoded pseudocontinuous arterial spin labeling (PCASL).

Materials and methods

Monte Carlo simulations and in vivo experiments were performed in 10 healthy subjects. Field strength and sequence: 5-delay sequential (5-del. Seq.), 7-delay Hadamard-encoded (7-del. Had.), and a single-delay (1-del.) PCASL, without and with vascular crushing at 3.0T. The errors and variations of cerebral blood flow (CBF) and arterial transit time (ATT) from simulations and the CBF and ATT estimates and variations in gray matter (GM) with different ATT ranges were compared. Pairwise t-tests with Bonferroni correction were used.

Results

The simulations and in vivo experiments showed that 1-del. PCASL underestimated GM CBF due to insufficient postlabeling delay (PLD) (37.2 ± 8.1 vs. 47.3 ± 8.5 and 47.3 ± 9.0 ml/100g/min, P ≤ 6.5 × 10^-6 ), while 5-del. Seq. and 7-del. Had. yielded comparable GM CBF (P ≥ 0.49). 5-del. Seq. was more reproducible for CBF (P = 4.7 × 10^-4 ), while 7-del. Had. was more reproducible for ATT (P = 0.033). 5-del. Seq. was more prone to intravascular artifacts and yielded lower GM ATTs compared to 7-del. Had. without crushing (1.13 ± 0.18 vs. 1.23 ± 0.13 seconds, P = 2.3 × 10^-3 ), but they gave comparable ATTs with crushing (P = 0.12). ATTs measured with crushing were longer than those without crushing (P ≤ 6.7 × 10^-4 ), but CBF was not affected (P ≥ 0.16).

Conclusion

The theoretical signal-to-noise ratio (SNR) gain through Hadamard encoding was confirmed experimentally. For 1-del., a PLD of 1.8 seconds is recommended for healthy subjects. With current parameters, 5-del. Seq. was more reproducible for CBF, and 7-del. Had. for ATT. Vascular crushing may help reduce variations in multidelay experiments without compromising tissue CBF or ATT measurements.

Level of evidence

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1119-1132.

Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 postcontrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater κ was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. Keywords: MR Imaging, CNS, Brain/Brain Stem, Reconstruction Algorithms © RSNA, 2022.

Lower back pain is a common problem frequently encountered without specific biomarkers that correlate well with an individual patient's pain generators. MRI quantification of diffusion and T2 relaxation properties may provide novel insight into the mechanical and inflammatory changes that occur in the lumbosacral nerve roots in patients with lower back pain. Accurate imaging of the spinal nerve roots is difficult because of their small caliber and oblique course in all three planes. Two-dimensional in-plane imaging of the lumbosacral nerve roots requires oblique coronal imaging with large field of view (FOV) in both dimensions, resulting in severe geometric distortions using single-shot echo planar imaging (EPI) techniques. The present work describes initial success using a reduced-FOV single-shot spin-echo EPI acquisition to obtain in-plane diffusion tensor imaging (DTI) and T2 mapping of the bilateral lumbar nerve roots at the L4 level of healthy subjects, minimizing partial volume effects, breathing artifacts and geometric distortions. A significant variation in DTI and T2 mapping metrics is also reported along the course of the normal nerve root. The fractional anisotropy is statistically significantly lower in the dorsal root ganglia (0.287 ± 0.068) than in more distal regions in the spinal nerve (0.402 ± 0.040) (p < 10(-5) ). The T2 relaxation value is statistically significantly higher in the dorsal root ganglia (78.0 ± 11.9 ms) than in more distal regions in the spinal nerve (59.5 ± 7.4 ms) (p < 10(-5) ). The quantification of nerve root DTI and T2 properties using the proposed methodology may identify the specific site of any degenerative and inflammatory changes along the nerve roots of patients with lower back pain.

Rationale and objectives

The aim of this work was to compare a high-resolution diffusion-weighted imaging (HR-DWI) acquisition (voxel size = 4.8 mm(3)) to a standard diffusion-weighted imaging (STD-DWI) acquisition (voxel size = 29.3 mm(3)) for monitoring neoadjuvant therapy-induced changes in breast tumors.

Materials and methods

Nine women with locally advanced breast cancer were imaged with both HR-DWI and STD-DWI before and after 3 weeks (early treatment) of neoadjuvant taxane-based treatment. Tumor apparent diffusion coefficient (ADC) metrics (mean and histogram percentiles) from both DWI methods were calculated, and their relationship to tumor volume change after 12 weeks of treatment (posttreatment) measured by dynamic contrast enhanced magnetic resonance imaging was evaluated with a Spearman's rank correlation.

Results

The HR-DWI pretreatment 15th percentile tumor ADC (P = .03) and early treatment 15th, 25th, and 50th percentile tumor ADCs (P = .008, .010, .04, respectively) were significantly lower than the corresponding STD-DWI percentile ADCs. The mean tumor HR-ADC was significantly lower than STD-ADC at the early treatment time point (P = .02), but not at the pretreatment time point (P = .07). A significant early treatment increase in tumor ADC was found with both methods (P < .05). Correlations between HR-DWI tumor ADC and posttreatment tumor volume change were higher than the STD-DWI correlations at both time points and the lower percentile ADCs had the strongest correlations.

Conclusion

These initial results suggest that the HR-DWI technique has potential for improving characterization of low tumor ADC values over STD-DWI and that HR-DWI may be of value in evaluating tumor change with treatment.