Accelerated Reduced Field of View T2-Weighted Imaging of Pancreaticobiliary Disorders Using Deep Learning-Based Reconstruction: Reduction of Acquisition Time and Improvement of Image Quality.

BACKGROUND

Reduced field-of-view (rFOV) T2WI improves in-plane spatial resolution. Deep learning-based reconstruction (DLR) has emerged as enhancing image quality. We compared examination time, image quality, and lesion detection rates between pancreaticobiliary rFOV T2WI with and without DLR.

METHODS

198 patients who underwent pancreaticobiliary rFOV T2WI were included. The protocol included rFOV T2WI with 2 NEX (rFOV T2WIN2) and 1 NEX (rFOV T2WIN1). DLR was applied to generate corresponding datasets: rFOV T2WIN2-DLR and rFOV T2WIN1-DLR datasets. Three observers evaluated the noise, respiratory motion artifacts (RMA), overall image quality (OIQ), and diagnostic confidence (DC). Two observers measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality metrics were compared using either ANOVA or Friedman test. Lesion detection rates were tested using the Cochran's Q test and McNemar test.

RESULTS

The noise, RMA, OIQ, and DC scores of rFOV T2WIN1-DLR were notably higher than those of rFOV T2WIN1. The noise, OIQ, and DC scores of rFOV T2WIN2 were notably higher than those of rFOV T2WIN1 (P < .001). The SNR and CNR of rFOV T2WI with DLR were notably higher than those of rFOV T2WI without DLR (P < .05). The rFOV T2WIN1-DLR sequence yielded a higher lesion detection rate (92.5%; 491/531) compared to rFOV T2WIN1 (76.5%; 406/531).

CONCLUSIONS

Pancreaticobiliary rFOV T2WI with DLR is feasible and yields superior image quality compared to rFOV T2WI without DLR. A 37.1% to 74.4% reduction in acquisition time is achievable without increasing image noise or compromising overall image quality and lesion detection rate.

RELEVANCE STATEMENT: The perfect balance of image quality, scanning time, and lesion detection rate can be achieved by using DLR in pancreaticobiliary rFOV T2WI.