Quantifying lumen diameter in coronary artery stents with high-resolution photon counting detector CT and convolutional neural network denoising

Abstract

Background: Small coronary arteries containing stents pose a challenge in CT imaging due to metal-induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency.

Purpose: The purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon-counting-detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy-integrating-detector (EID) CT system.

Methods: Seven coronary stents of different materials and inner diameters between 3.43 and 4.72 mm were placed in plastic tubes of diameters 3.96-4.87 mm containing 20 mg/mL of iodine solution, mimicking stented contrast-enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z-axis in an anthropomorphic phantom emulating an average-sized patient and scanned with a clinical EID-CT and PCD-CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol (120 kV, 180 quality reference mAs). PCD scans were performed using the ultra-high-resolution (UHR) mode (120 × 0.2 mm collimation) at 120 kV with tube current adjusted so that CTDI_vol was matched to that of EID scans. EID images were reconstructed per our routine clinical protocol (Br40, 0.6 mm thickness), and with the sharpest available kernel (Br69). PCD images were reconstructed at a thickness of 0.6 mm and a dedicated sharp kernel (Br89) which is only possible with the PCD UHR mode. To address increased image noise introduced by the Br89 kernel, an image-based CNN denoising algorithm was applied to the PCD images of stents scanned parallel to the scanner's z-axis. Stents were segmented based on full-width half maximum thresholding and morphological operations, from which effective lumen diameter was calculated and compared to reference sizes measured with a caliper.

Results: Substantial blooming artifacts were observed on EID Br40 images, resulting in larger stent struts and reduced lumen diameter (effective diameter underestimated by 41% and 47% for parallel and perpendicular orientations, respectively). Blooming artifacts were observed on EID Br69 images with 19% and 31% underestimation of lumen diameter compared to the caliper for parallel and perpendicular scans, respectively. Overall image quality was substantially improved on PCD, with higher spatial resolution and reduced blooming artifacts, resulting in the clearer delineation of stent struts. Effective lumen diameters were underestimated by 9% and 19% relative to the reference for parallel and perpendicular scans, respectively. CNN reduced image noise by about 50% on PCD images without impacting lumen quantification (<0.3% difference).

Conclusion: The PCD UHR mode improved in-stent lumen quantification for all seven stents as compared to EID images due to decreased blooming artifacts. Implementation of CNN denoising algorithms to PCD data substantially improved image quality.

Keywords: computed tomography; convolutional neural network; coronary artery stents; denoising; photon counting detector.

Figure 1.

Example of the process of coronary artery balloon angioplasty (A-D) used in inflation and deployment of stents in plastic tubes. Figure created using Biorender^™.

Figure 2.

Close up image of stent on holder in axial position in the water cylinder (red arrow, A). Image of experimental setup with water cylinder in an anthropomorphic phantom and submerged stents (B). Stents scanned parallel (C) and perpendicular (D) to the scanner’s z-axis. Figure created using Biorender^™.

Figure 3.

A schematic of CNN denoising training framework. Training data included filtered-back projections (FBP) and quantum iterative reconstruction (QIR) images.

Figure 4.

Process pipeline for automated segmentation algorithm. Axial images with stents were extracted and stent location was identified. Binary images were created from stent images and connected using morphological operations. Areas inside the stents were filled for calculation of inner lumen effective diameter.

Figure 5.

Evaluation of EID Br40 images (left), EID Br69 (left middle), PCD (right middle) and PCD+CNN Denoising (right) for in-plane with scanner’s z-axis (top), multi-planar reformat (MPR, middle), and volume rendering technique (VRT, bottom). PCD images shown were reconstructed with QIR. WW/WL = 1000/10000 HU.

Figure 6.

Evaluation of EID Br40 images (left), EID Br69 (left middle), PCD (right middle) and PCD+CNN Denoising (right) for through-plane with scanner’s z-axis (top) and MPR (bottom). PCD images shown were reconstructed with QIR. WW/WL = 1000/10000 HU.

Figure 7.

Results of segmented inner stent lumen effective diameter (mm) for EID Br40, EID Br69, PCD, and PCD + CNN Denoising for all seven stents parallel to the scanner’s z-axis (A) and results for EID Br40, EID Br69, and PCD perpendicular to the scanner’s z-axis (B). Error bars represent standard deviation of values used in effective diameter calculation for each stent.