Pitfalls on PET/MRI.

A decade of PET/MRI clinical imaging has passed and many of the pitfalls are similar to those on earlier studies. However, techniques to overcome them have emerged and continue to develop. Although clinically significant lung nodules are demonstrable, smaller nodules may be detected using ultrashort/zero echo-time (TE) lung MRI. Fast reconstruction ultrashort TE sequences have also been used to achieve high-resolution lung MRI even with free-breathing. The introduction and improvement of time-of-flight scanners and increasing the axial length of the PET detector arrays have more than doubled the sensitivity of the PET part of the system. MRI for attenuation correction has provided many potential pitfalls, including misclassification of tissue classes based on MRI information for attenuation correction. Although the use of short echo times have helped to address these pitfalls, one of the most exciting developments has been the use of deep learning algorithms and computational neural networks to rapidly provide soft tissue, fat, bone and air information for the attenuation correction as a supplement to the attenuation correction information from fat-water imaging. Challenges with motion correction, particularly respiratory and cardiac remain but are being addressed with respiratory monitors and using PET data. In order to address truncation artefacts, the system manufacturers have developed methods to extend the MR field-of-view for the purpose of the attenuation and scatter corrections. General pitfalls like stitching of body sections for individual studies, optimum delivery of images for viewing and reporting, and resource implications for the sheer volume of data generated remain Methods to overcome these pitfalls serve as a strong foundation for the future of PET/MRI. Advances in the underlying technology with significant evolution in hard-ware and software and the exiting developments in use of deep learning algorithms and computational neural networks will drive the next decade of PET/MRI imaging.

Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan.

BACKGROUND: MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T. PURPOSE: To evaluate the efficacy of a novel algorithm, Uniform Combined Reconstruction (UNICORN), to correct receive coil-induced nonuniformity in musculoskeletal 7T MRI without the use of a reference scan. STUDY TYPE: Retrospective image analysis study. SUBJECTS: MRI data of 20 subjects was retrospectively processed offline. Field Strength/Sequence: Knees of 20 subjects were imaged at 7T with a single-channel transmit, 28-channel phased-array receive knee coil. A turbo-spin-echo sequence was used to acquire 33 series of images. ASSESSMENT: Three fellowship-trained musculoskeletal radiologists with cumulative experience of 42 years reviewed the images. The uniformity, contrast, signal-to-noise ratio (SNR), and overall image quality were evaluated for images with no postprocessing, images processed with N4 bias field correction algorithm, and the UNICORN algorithm. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) was used for measuring the interrater reliability. ICC and 95% confidence intervals (CIs) were calculated using the R statistical package employing a two-way mixed-effects model based on a mean rating (k = 3) for absolute agreement. The Wilcoxon signed-rank test with continuity correction was used for analyzing the overall image quality scores. RESULTS: UNICORN was preferred among the three methods evaluated for uniformity in 97.9% of the pooled ratings, with excellent interrater agreement (ICC of 0.98, CI 0.97-0.99). UNICORN was also rated better than N4 for contrast and equivalent to N4 in SNR with ICCs of 0.80 (CI 0.72-0.86) and 0.67 (CI 0.54-0.77), respectively. The overall image quality scores for UNICORN were significantly higher than N4 (P < 6 × 10-13 ), with good to excellent interrater agreement (ICC 0.90, CI 0.86-0.93). DATA CONCLUSION: Without the use of a reference scan, UNICORN provides better image uniformity, contrast, and overall image quality at 7T compared with the N4 bias field-correction algorithm. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.