Different Lung Nodule Detection Tasks at Different Dose Levels by Different Computed Tomography Image Reconstruction Strategies

Detecting clinically-significant lung nodules, a potential precursor of lung cancer, at as low as achievable radiation dose is demanded to minimize the stochastic radiation effects. This study aims to fulfill these goals by exploring optimal image reconstruction strategies and evaluating these strategies at multiple dose levels with prospective patient studies. Total 133 patients with a suspicious pulmonary nodule scheduled for biopsy, were recruited and data were acquired at 120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images in a random fashion, blinded to the algorithms used for the reconstructions. Each nodule identified in the image volume was marked, (including the 133 biopsy target nodules and 28 other non-target nodules). A 10-point likert scale was used for all scoring to characterize the target nodule images. The score for FBP drops from 100 to 20mAs faster than MRF-H, who drops faster than MRF-T, as expected. All the plots drop faster from 100 to 40 than from 40 to 20mAs. For detection of both the target nodules and the non-target nodules some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively, while MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs.