Visualization of flow dynamics in the portal circulation using 320-detector-row computed tomography: a feasibility study

Abstract

Multidetector row computed tomography (CT) scanners perform dynamic scanning and have a wide scan range. Time-resolved three-dimensional CT (i.e., 4D CT) has recently enabled visualization of flow in neurovascular vessels. We hypothesized that 4D CT technology would be a useful and non-invasive method for visualizing the flow dynamics of the portal circulation. The aim of this study was to evaluate the technical feasibility of 4D CT for visualizing flow dynamics in the portal circulation using 320-detector-row CT. 4D CT images of 18 consecutive patients with portal circulation including gastrorenal shunt were retrospectively evaluated for their ability to generate flow dynamics of the portal circulation. Flow dynamics could be visualized by 4D CT in 68 of the 72 vessels in the portal vein, splenic vein, superior mesenteric vein, and gastrorenal shunt. Flow direction could not be identified in four vessels, all of them being superior mesenteric veins. Flow direction was recognized on 4D CT in the 68 vessels of the portal circulation. A preliminary validation study revealed that flow direction of all 19 vessels in the portal circulation had concordance between 4D CT and color Doppler ultrasound. 4D CT could visualize flow dynamics of the portal circulation.

MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study

“…The results of this study indicate that even extensive MRI pre-processing and homogenization of the MR images do not sufficiently address the variations in acquisition and reconstruction parameters. This is in line with studies published in recent years that investigated the reproducibility of MRI radiomics features in test-retest phantom data as well as in patient data of varying disease sites, and showed that, among others, the variations in acquisition and reconstruction parameters strongly influence the values (concordance) of radiomics features [ 24 , 27 , 28 , 29 , 49 , 50 , 51 , 52 ]. Shur et al [ 29 ] performed a test-retest 1.5T MRI phantom study using the same imaging protocol and showed that 20% of the examined features were not repeatable.…”

“…This is in line with studies published in recent years that investigated the reproducibility of MRI radiomics features in test-retest phantom data as well as in patient data of varying disease sites, and showed that, among others, the variations in acquisition and reconstruction parameters strongly influence the values (concordance) of radiomics features [ 24 , 27 , 28 , 29 , 49 , 50 , 51 , 52 ]. Shur et al [ 29 ] performed a test-retest 1.5T MRI phantom study using the same imaging protocol and showed that 20% of the examined features were not repeatable. A study on repeatability and reproducibility using a T2W pelvic phantom showed that radiomics features values are not only affected by varying acquisition parameters but also by the use of different MRI vendors and magnetic field strengths, wherein the reproducibility of the radiomic features is more affected by difference in MRI vendor than by difference in magnetic field strength [ 49 ].…”

“…Despite the promising potential of radiomics, several hurdles that impede the clinical implementation of radiomics models have been identified. One of these is the sensitivity of radiomics features to the variations in acquisition and reconstruction parameters across different imaging modalities [ 22 , 23 , 24 , 25 , 26 ], and some features were found not to be reproducible even in test-retest scenarios [ 27 , 28 , 29 ].…”

Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem

“…Some models are purely based on such a private dataset (e.g. Shamout, Shen, Wu, Kaku, Park, Makino, Jastrzȩbski, Witowski, Wang, Zhang, et al., 2021 , Castiglioni, Ippolito, Interlenghi, Monti, Salvatore, Schiaffino, Polidori, Gandola, Messa, Sardanelli, 2021 , Xia, Chen, Ren, Zhao, Wang, Jin, Zhou, Wang, Yan, Zhang, et al., 2021 ). But many authors utilise small private dataset together with larger public datasets.…”

Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image

“…The formula is also based on the evaluation formula and features proposed by Taha et al [ 47 ]. In this study, Intersection over Union (IoU) (as known as Jaccard coefficient), Accuracy (ACC), Area Under the ROC Curve (AUC), Dice coefficient, and Average Hausdorff Distance (AVGDIST) [ 48 ] were used as metrics. These metrics can be composed of the following four parameters: TP (true positive), TN (true negative), FP (false positive), and FN (false negative) by means of the values embedded in the segmentation.…”