Task-based assessment on various optimization protocols of computed tomography Pulmonary Angiography examination

Reducing contrast-agent volume and radiation dose in CT with 90-kVp tube voltage, high tube current modulation, and advanced iteration algorithm

Increasing utilization of computed tomography (CT) has raised concerns regarding CT radiation dose and technology has been developed to achieve an appropriate balance between image quality, radiation dose, and the amount of contrast material. This study was planned to evaluate the image quality and radiation dose in pancreatic dynamic computed tomography (PDCT) with 90-kVp tube voltage and reduction of the standard amount of contrast agent, compared with 100-kVp PDCT of the research hospital's convention. Total of 51 patients with both CT protocols were included. The average Hounsfield units (HU) values of the abdominal organs and image noise were measured for objective image quality analysis. Two radiologists evaluated five categories of image qualities such as subjective image noise, visibility of small structure, beam hardening or streak artifact, lesion conspicuity and overall diagnostic performance for subjective image quality analysis. The total amount of contrast agent, radiation dose, and image noise decreased in the low-kVp group, by 24.4%, 31.7%, and 20.6%, respectively (p < 0.001). The intraobserver and interobserver agreements were moderate to substantial (k = 0.4-0.8). The contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and figure of merit of the almost organs except psoas muscle in the low-kVp group were significantly higher (p < 0.001). Except for lesion conspicuity, both reviewers judged that subjective image quality of the 90-kVp group was better (p < 0.001). With 90-kVp tube voltage, 25% reduced contrast agent volume with advanced iteration algorithm and high tube current modulation achieved radiation dose reduction of 31.7%, as well as better image quality and diagnostic confidence.

Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA)

Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.

Mechanical and Imaging Properties of a Clinical-Grade Kidney Phantom Based on Polydimethylsiloxane and Elastomer

Medical imaging phantoms are considered critical in mimicking the properties of human tissue for calibration, training, surgical planning, and simulation purposes. Hence, the stability and accuracy of the imaging phantom play a significant role in diagnostic imaging. This study aimed to evaluate the influence of hydrogen silicone (HS) and water (H₂O) on the compression strength, radiation attenuation properties, and computed tomography (CT) number of the blended Polydimethylsiloxane (PDMS) samples, and to verify the best material to simulate kidney tissue. Four samples with different compositions were studied, including samples S1, S2, S3, and S4, which consisted of PDMS 100%, HS/PDMS 20:80, H₂O/PDMS 20:80, and HS/H₂O/PDMS 20:40:40, respectively. The stability of the samples was assessed using compression testing, and the attenuation properties of sample S2 were evaluated. The effective atomic number of S2 showed a similar pattern to the human kidney tissue at 1.50 × 10⁻¹ to 1 MeV. With the use of a 120 kVp X-ray beam, the CT number quantified for S2, as well measured 40 HU, and had the highest contrast-to-noise ratio (CNR) value. Therefore, the S2 sample formulation exhibited the potential to mimic the human kidney, as it has a similar dynamic and is higher in terms of stability as a medical phantom.