ABSTRACT
This study's aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3-6, mean 4.2) artificial lung nodules (diameter: 3, 5, 8 mm; density: -800, -630, +100 HU) were inserted at different locations in the Kyoto Kagaku Lungman phantom. In total, 16 configurations (10 abnormal, 6 normal) were scanned at 7.6, 3, 1.6 and 0.38 mGy CTDIvol (respectively 0, 60, 80 and 95% dose reduction). Images were reconstructed using 50% ASIR-V and a deep learning-based algorithm with low (DL-L), medium (DL-M) and high (DL-H) strength. Four chest radiologists evaluated 256 series by locating and scoring nodules on a five-point scale. No statistically significant difference was found among the reconstruction algorithms (p = 0.987, average across readers AUC: 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H).
Subject(s)
Deep Learning , Radiographic Image Interpretation, Computer-Assisted , Algorithms , Phantoms, Imaging , Radiation Dosage , Tomography, X-Ray ComputedABSTRACT
A 62-years-old female presented to the Emergency Department with abdominal pain and absence of defaecation for two days. CT revealed an ileus with a change in the diameter of the small intestine and a segment without contrast in the intestinal wall. Laparotomy confirmed that a segment of the ileum was ischaemic.