Coronary plaque risk classification in images acquired with photon-counting-detector (PCD) CT was performed using a radiomics-based machine learning (ML) model. With IRB approval, 17 coronary CTA patients were scanned on a PCD-CT (NAEOTOM Alpha, Siemens Healthineers) with median CTDIvol of 4.56 mGy. Four types of images: 120-kV PCD-CT image, virtual monoenergetic images (VMIs) at 50-keV and 100-keV, and iodine maps were reconstructed using an iterative reconstruction algorithm, a vascular kernel (Bv40) and 0.6-mm/0.4-mm slice thickness/increment. Atherosclerotic plaques were segmented using semi-automatic software (Research Frontier, Siemens). Segmentation confirmation and risk stratification (low- vs high-risk) were performed by a board-certified cardiac radiologist. A total of 1674 radiomic features were extracted from each image using PyRadiomics (v2.2.0b1). For each feature, a t-test was performed between low- and high-risk plaques (p<0.05 considered significant). Feature reduction was performed with a clustering algorithm and 6 non-redundant features were input into a linear support vector machine (SVM). A leave-one-out cross-validation strategy was adopted and the area under the ROC curve (AUC) was computed. Twelve low-risk and 5 high-risk plaques were identified by the radiologist. A total of 80, 66, 183, and 48 out of 1674 features in 120-kV, 50-keV, 100-keV, and iodine map images were statistically significant. The SVM classified 16/17 plaques correctly in the 120-kV PCD-CT and 50-keV VMI images. The AUC was 0.967, 0.967, 0.917, and 0.833 in 120-kV, 50-keV, 100-keV, and iodine map images, respectively. A ML model using coronary PCD-CTA images at 120-kV and 50-keV best automatically differentiated low- and high-risk coronary plaques.
Radiomics is a promising mathematical tool for characterizing disease and predicting clinical outcomes from radiological images such as CT. Photon-counting-detector (PCD) CT provides improved spatial resolution and dose efficiency relative to conventional energy-integrating-detector CT systems. Since improved spatial resolution enables visualization of smaller structures and more details that are not typically visible at routine resolution, it has a direct impact on textural features in CT images. Therefore, it is of clinical interest to quantify the impact of the improved spatial resolution on calculated radiomic features and, consequently, on sample classification. In this work, organic samples (zucchini, onions, and oranges) were scanned on both clinical PCD-CT and EID-CT systems at two dose levels. High-resolution PCD-CT and routine-resolution EID-CT images were reconstructed using a dedicated sharp kernel and a routine kernel, respectively. The noise in each image was quantified. Fourteen radiomic features of relevance were calculated in each image for each sample and compared between the two scanners. Radiomic features were plotted pairwise to evaluate the resulting cluster separation of the samples by their type between PCD-CT and EID-CT. Thirteen out of 14 studied radiomic features were notably changed by the improved resolution of the PCD-CT system, and the cluster separation was better when assessing features derived from PCD-CT. These results show that features derived from high-resolution PCD-CT, which are subject to higher noise compared to EID-CT, may impact radiomics-based clinical decision making.
Purpose: We present photon-counting computed tomography (PCCT) imaging of contrast agent triplets similar in atomic number (Z) achieved with a high-flux cadmium zinc telluride (CZT) detector.
Approach: The table-top PCCT imaging system included a 330-μm-pitch CZT detector of size 8 mm × 24 mm2 capable of using six energy bins. Four 3D-printed 3-cm-diameter phantoms each contained seven 6-mm-diameter vials with water and low and high concentration solutions of various contrast agents. Lanthanum (Z = 57), gadolinium (Gd) (Z = 64), and lutetium (Z = 71) were imaged together and so were iodine (Z = 53), Gd, and holmium (Z = 67). Each phantom was imaged with 1-mm aluminum-filtered 120-kVp cone beam x rays to produce six energy-binned computed tomography (CT) images.
Results:K-edge images were reconstructed using a weighted sum of six CT images, which distinguished each contrast agent with a root-mean-square error (RMSE) of <0.29 % and 0.51% for the 0.5% and 5% concentrations, respectively. Minimal cross-contamination in each K-edge image was seen, with RMSE values <0.27 % in vials with no contrast.
Conclusion: This is the first preliminary demonstration of simultaneously imaging three similar Z contrast agents with a difference in Z as low as 3.
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