Advances in IPMN imaging: deep learning-enhanced HASTE improves lesion assessment
摘要
The prevalence of asymptomatic pancreatic cysts is increasing due to advances in imaging techniques. Among these, intraductal papillary mucinous neoplasms (IPMNs) are most common, with potential for malignant transformation, often necessitating close follow-up. This study evaluates novel MRI techniques for the assessment of IPMN.
Materials and methodsFrom May to December 2023, 59 patients undergoing abdominal MRI were retrospectively enrolled. Examinations were conducted on 3-Tesla scanners using a Deep-Learning Accelerated Half-Fourier Single-Shot Turbo Spin-Echo (HASTEDL) and standard HASTE (HASTES) sequence. Two readers assessed minimum detectable lesion size and lesion-to-parenchyma contrast quantitatively, and qualitative assessments focused on image quality. Statistical analyses included the Wilcoxon signed-rank and chi-squared tests.
ResultsHASTEDL demonstrated superior overall image quality (p < 0.001), with higher sharpness and contrast ratings (p < 0.001, p = 0.112). HASTEDL showed enhanced conspicuity of IPMN (p < 0.001) and lymph nodes (p < 0.001), with more frequent visualization of IPMN communication with the pancreatic duct (p < 0.001). Visualization of complex features (dilated pancreatic duct, septa, and mural nodules) was superior in HASTEDL (p < 0.001). The minimum detectable cyst size was significantly smaller for HASTEDL (4.17 mm ± 3.00 vs. 5.51 mm ± 4.75; p < 0.001). Inter-reader agreement was for (к 0.936) for HASTEDL, slightly lower (к 0.885) for HASTES.
ConclusionHASTEDL in IPMN imaging provides superior image quality and significantly reduced scan times. Given the increasing prevalence of IPMN and the ensuing clinical need for fast and precise imaging, HASTEDL improves the availability and quality of patient care.
Key Points