Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study.

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured.

Contrast-optimized composite image derived from multigradient echo cardiac magnetic resonance imaging improves reproducibility of myocardial contours and T2* measurement.

OBJECTIVES: Reproducibility of myocardial contour determination in cardiac magnetic resonance imaging is important, especially when determining T2* values per myocardial segment as a prognostic factor of heart failure or thalassemia. A method creating a composite image with contrasts optimized for drawing myocardial contours is introduced and compared with the standard method on a single image. MATERIALS AND METHODS: A total of 36 short-axis slices from bright-blood multigradient echo (MGE) T2* scans of 21 patients were acquired at eight echo times. Four observers drew free-hand myocardial contours on one manually selected T2* image (method 1) and on one image composed by blending three images acquired at TEs providing optimum contrast-to-noise ratio between the myocardium and its surrounding regions (method 2). RESULTS: Myocardial contouring by method 2 met higher interobserver reproducibility than method 1 (P < 0.001) with smaller Coefficient of variance (CoV) of T2* values in the presence of myocardial iron accumulation (9.79 vs. 15.91%) and in both global myocardial and mid-ventricular septum regions (12.29 vs. 16.88 and 5.76 vs. 8.16%, respectively). CONCLUSION: The use of contrast-optimized composite images in MGE data analysis improves reproducibility of myocardial contour determination, leading to increased consistency in the calculated T2* values enhancing the diagnostic impact of this measure of iron overload.

Development of an Ex Vivo, Beating Heart Model for CT Myocardial Perfusion.

OBJECTIVE: To test the feasibility of a CT-compatible, ex vivo, perfused porcine heart model for myocardial perfusion CT imaging. METHODS: One porcine heart was perfused according to Langendorff. Dynamic perfusion scanning was performed with a second-generation dual source CT scanner. Circulatory parameters like blood flow, aortic pressure, and heart rate were monitored throughout the experiment. Stenosis was induced in the circumflex artery, controlled by a fractional flow reserve (FFR) pressure wire. CT-derived myocardial perfusion parameters were analysed at FFR of 1 to 0.10/0.0. RESULTS: CT images did not show major artefacts due to interference of the model setup. The pacemaker-induced heart rhythm was generally stable at 70 beats per minute. During most of the experiment, blood flow was 0.9-1.0 L/min, and arterial pressure varied between 80 and 95 mm/Hg. Blood flow decreased and arterial pressure increased by approximately 10% after inducing a stenosis with FFR ≤ 0.50. Dynamic perfusion scanning was possible across the range of stenosis grades. Perfusion parameters of circumflex-perfused myocardial segments were affected at increasing stenosis grades. CONCLUSION: An adapted Langendorff porcine heart model is feasible in a CT environment. This model provides control over physiological parameters and may allow in-depth validation of quantitative CT perfusion techniques.

Intermodel agreement of myocardial blood flow estimation from stress-rest myocardial perfusion magnetic resonance imaging in patients with coronary artery disease.

OBJECTIVES: The aim of this study was to assess the intermodel agreement of different magnetic resonance myocardial perfusion models and evaluate their correspondence to stenosis diameter. MATERIALS AND METHODS: In total, 260 myocardial segments were analyzed from rest and adenosine stress first-pass myocardial perfusion magnetic resonance images (1.5 T, 0.050 ± 0.005 mmol/kg body weight gadolinium; 122 segments in rest, 138 in stress) in 10 patients with suspected or known coronary artery disease. Signal intensity curves were calculated per myocardial segment, of which the contours were traced with QMASS MR V.7.6 (Medis, Leiden, the Netherlands), and exported to Matlab. Myocardial blood flow quantification was performed with distributed parameter, extended Toft, Patlak, and Fermi parametric models (in-house programs; Matlab R2013a; Mathworks Inc, Natick, MA). Modeling was applied after the signal intensity curves were corrected for spatial magnetic field inhomogeneity and contrast saturation. Overall and grouped perfusion values based on presence of coronary stenosis (>50% diameter reduction) at coronary computed tomography angiography at second generation dual-source computed tomography were compared between the perfusion models. RESULTS: Rest and stress myocardial perfusion estimates for all models were significantly related to each other (P < 0.001). The highest correlation coefficients were found between the extended Toft and Fermi models (R = 0.89-0.91) and low correlation coefficients between the distributed parameter and Patlak models (R = 0.66-0.68). The models resulted in significantly different perfusion estimates in stress (P = 0.03), but not in rest (P = 0.74). The differences in perfusion estimates in stress were caused by differences between the distributed parameter and Patlak models and between the Patlak and Fermi models (both P < 0.001). Significantly lower perfusion estimates were found for myocardial segments subtended by coronary arteries with versus without significant stenosis, but only for estimations produced by the extended Toft model (P = 0.04) and Fermi model (P = 0.01). There were no significant differences in rest perfusion values between models. CONCLUSIONS: Quantitative myocardial perfusion values in stress depend on the modeling method used to calculate the perfusion estimate. The difference in myocardial perfusion estimate with or without stenosis in the subtending coronary artery is most pronounced when the extended Toft or Fermi model is used.

Clinical implications of non-steatotic hepatic fat fractions on quantitative diffusion-weighted imaging of the liver.

Diffusion-weighted imaging (DWI) is an important diagnostic tool in the assessment of focal liver lesions and diffuse liver diseases such as cirrhosis and fibrosis. Quantitative DWI parameters such as molecular diffusion, microperfusion and their fractions, are known to be affected when hepatic fat fractions (HFF) are higher than 5.5% (steatosis). However, less is known about the effect on DWI for HFF in the normal non-steatotic range below 5.5%, which can be found in a large part of the population. The aim of this study was therefore to evaluate the diagnostic implications of non-steatotic HFF on quantitative DWI parameters in eight liver segments. For this purpose, eleven healthy volunteers (2 men, mean-age 31.0) were prospectively examined with DWI and three series of in-/out-of-phase dual-echo spoiled gradient-recalled MRI sequences to obtain the HFF and T2*. DWI data were analyzed using the intravoxel incoherent motion (IVIM) model. Four circular regions (ø22.3 mm) were drawn in each of eight liver segments and averaged. Measurements were divided in group 1 (HFF ≤ 2.75%), group 2 (2.75< HFF ≤ 5.5%) and group 3 (HFF>5.5%). DWI parameters and T2* were compared between the three groups and between the segments. It was observed that the molecular diffusion (0.85, 0.72 and 0.49 × 10(-3) mm(2)/s) and T2* (32.2, 27.2 and 21.0 ms) differed significantly between the three groups of increasing HFF (2.18, 3.50 and 19.91%). Microperfusion and its fraction remained similar for different HFF. Correlations with HFF were observed for the molecular diffusion (r = -0.514, p<0.001) and T2* (-0.714, p<0.001). Similar results were obtained for the majority of individual liver segments. It was concluded that fat significantly decreases molecular diffusion in the liver, also in absence of steatosis (HFF ≤ 5.5%). Also, it was confirmed that fat influences T2*. Determination of HFF prior to quantitative DWI is therefore crucial.

Influence of the choice of software package on the outcome of semiquantitative MR myocardial perfusion analysis.

PURPOSE: To assess the repeatability and reproducibility of semiquantitative magnetic resonance (MR) perfusion analysis performed by using different software packages. MATERIALS AND METHODS: The study protocol was approved by the institutional ethics committee. Informed consent was obtained from each patient. Semiquantitative perfusion analysis was performed twice by two independent observers using four dedicated software packages. MR perfusion datasets originated from eight patients with known single-vessel disease who were scheduled for percutaneous coronary intervention (PCI) on the basis of coronary angiography findings. Each patient underwent two examinations: 1 day before and 1 day after PCI. Repeatability (intra- and interobserver agreements) and reproducibility (intersoftware agreement) were evaluated for perfusion upslope and myocardial perfusion reserve index with Student t test and Bland-Altman analyses. RESULTS: Intra- and interobserver agreements were good and comparable for repeated measurements within each individual software platform (mean differences < 6%, intraclass correlation coefficient [ICC] ≥ 0.68). However, the intersoftware variability was significant (limits of agreement ≥ 65%, ICC ≤ 0.67) such that the values produced with the different software packages are not interchangeable. CONCLUSION: The results indicate high repeatability within individual software but low reproducibility between different software packages, suggesting that within-group and/or sequential observation of semiquantitative perfusion parameters must be performed with the same software platform. Before semiquantitative perfusion analysis can be incorporated reliably into clinical studies, it is important to resolve the differences between the software packages.