Sorting lung tumor volumes from 4D‐MRI data using an automatic tumor‐based signal reduces stitching artifacts

Abstract

AbstractPurposeTo investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D‐magnetic resonance (4D‐MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes.Methods(4D‐MRI) scans were collected for 10 lung cancer patients using a 2D T2‐weighted single‐shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor‐motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison.For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test.ResultsThe TumorPC1 signal was most strongly correlated with superior‐inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior‐inferior tumor motion (p < 0.05).Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02–0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one.ConclusionTumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.