Accelerated Reduced Field of View T2-Weighted Imaging of Pancreaticobiliary Disorders Using Deep Learning-Based...

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.

198 patients who underwent pancreaticobiliary rFOV T2WI were included. The protocol included rFOV T2WI with 2 NEX (rFOV T2WI_N2) and 1 NEX (rFOV T2WI_N1). DLR was applied to generate corresponding datasets: rFOV T2WI_N2-DLR and rFOV T2WI_N1-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.

The noise, RMA, OIQ, and DC scores of rFOV T2WI_N1-DLR were notably higher than those of rFOV T2WI_N1. The noise, OIQ, and DC scores of rFOV T2WI_N2 were notably higher than those of rFOV T2WI_N1 (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 T2WI_N1-DLR sequence yielded a higher lesion detection rate (92.5%; 491/531) compared to rFOV T2WI_N1 (76.5%; 406/531).

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.

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