Shading artifact correction in breast CT using an interleaved deep learning segmentation and maximum‐likelihood polynomial fitting approach

Abstract

Purpose The purpose of this work was twofold: (a) To provide a robust and accurate method for image segmentation of dedicated breast CT (bCT) volume data sets, and (b) to improve Hounsfield unit (HU) accuracy in bCT by means of a postprocessing method that uses the segmented images to correct for the low‐frequency shading artifacts in reconstructed images. Methods A sequential and iterative application of image segmentation and low‐order polynomial fitting to bCT volume data sets was used in the interleaved correction (IC) method. Image segmentation was performed through a deep convolutional neural network (CNN) with a modified U‐Net architecture. A total of 45 621 coronal bCT images from 111 patient volume data sets were segmented (using a previously published segmentation algorithm) and used for neural network training, validation, and testing. All patient data sets were selected from scans performed on four different prototype breast CT systems. The adipose voxels for each patient volume data set, segmented using the proposed CNN, were then fit to a three‐dimensional low‐order polynomial. The polynomial fit was subsequently used to correct for the shading artifacts introduced by scatter and beam hardening in a method termed “flat fielding.” An interleaved utilization of image segmentation and flat fielding was repeated until a convergence criterion was satisfied. Mathematical and physical phantom studies were conducted to evaluate the dependence of the proposed algorithm on breast size and the distribution of fibroglandular tissue. In addition, a subset of patient scans (not used in the CNN training, testing or validation) were used to investigate the accuracy of the IC method across different scanner designs and beam qualities. Results The IC method resulted in an accurate classification of different tissue types with an average Dice similarity coefficient > 95%, precision > 97%, recall > 95%, and F1‐score > 96% across all tissue types. The flat fielding correction of bCT images resulted in a significant reduction in either cupping or capping artifacts in both mathematical and physical phantom studies as measured by the integral nonuniformity metric with an average reduction of 71% for cupping and 30% for capping across different phantom sizes, and the Uniformity Index with an average reduction of 53% for cupping and 34% for capping. Conclusion The validation studies demonstrated that the IC method improves Hounsfield Units (HU) accuracy and effectively corrects for shading artifacts caused by scatter contamination and beam hardening. The postprocessing approach described herein is relevant to the broad scope of bCT devices and does not require any modification in hardware or existing scan protocols. The trained CNN parameters and network architecture are available for interested users.

Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy

“…An early reported DL based method for segmentation of breast was specified for diagnostic CT with higher contrast between anatomical structures (20). The advantage of the DL based method in comparison with the regional intensity based or overall histogram-based unsupervised learning methods (21) is obvious.…”

Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging

“…A total of 500 projections were acquired over a 360° scan using a Paxscan 4030CB flat panel detector (Varian Medical Systems, Palo Alto, California, USA) operating at 30 fps (approximately 17 s total scan time) in 2 × 2 binning mode with dynamic gain. All projection images were reconstructed using a variation of the Feldkamp‐filtered backprojection algorithm (with a Shepp‐Logan kernel) with an isotropic voxel size of 0.38 mm, and corrected for shading artifacts using a maximum likelihood polynomial fitting approach in the reconstruction space 28 . The tube current was adjusted for each patient scan based on breast size and mammographic density, resulting in a mean glandular dose of approximately 6.0 mGy.…”

“…All projection images were reconstructed using a variation of the Feldkampfiltered backprojection algorithm (with a Shepp-Logan kernel) with an isotropic voxel size of 0.38 mm, and corrected for shading artifacts using a maximum likelihood polynomial fitting approach in the reconstruction space. 28 The tube current was adjusted for each patient scan based on breast size and mammographic density, resulting in a mean glandular dose of approximately 6.0 mGy.…”

Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT

“…All 12 kV/filter combinations and all three phantom sizes were imaged. The 500 Medical Physics, 47 (7), July 2020 projection images acquired for each acquisition were reconstructed using a variation of the Feldkamp algorithm 28 enabling it to be run on a graphic processing unit (GPU). A Shepp-Logan apodization filter with a cutoff frequency, f C = 2 9 f nyquist , was implemented in the reconstruction of a 1024 9 1024 matrix with an isotropic voxel dimension of 150 lm resulting in 468, 674, and 895 coronal slices in order to reconstruct the entirety of the V1, V3, and V5 phantoms, respectively.…”