= 0013).
The responsiveness of pulmonary vasculature to treatment, quantified by non-contrast CT, correlated with hemodynamic and clinical parameters.
Quantitative assessment of pulmonary vascular changes in response to treatment, as measured by non-contrast CT, demonstrated correlations with hemodynamic and clinical parameters.

The purpose of this study was to evaluate brain oxygen metabolism states in preeclampsia patients via magnetic resonance imaging, and to identify the factors that affect cerebral oxygen metabolism in preeclampsia.
The study sample consisted of 49 women with preeclampsia (mean age 32.4 years, range 18-44 years), 22 pregnant, healthy controls (mean age 30.7 years, range 23-40 years), and 40 non-pregnant healthy controls (mean age 32.5 years, range 20-42 years). Utilizing a 15-T scanner, quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent (BOLD) magnitude-based oxygen extraction fraction (OEF) mapping were employed to calculate brain oxygen extraction fraction (OEF) values. To analyze the distinctions in OEF values across brain regions between the groups, a voxel-based morphometry (VBM) approach was employed.
When comparing the average OEF values amongst the three groups, a notable difference was observed in diverse areas of the brain, including the parahippocampus, the frontal lobe's gyri, calcarine sulcus, cuneus, and precuneus.
The values were found to be statistically significant (less than 0.05), after controlling for multiple comparisons. check details The average OEF values for the preeclampsia group were significantly greater than those for the PHC and NPHC groups. The bilateral superior frontal gyrus, in addition to the bilateral medial superior frontal gyrus, demonstrated the most extensive size of the specified brain areas. The OEF values for these areas were 242.46, 213.24, and 206.28 in the preeclampsia, PHC, and NPHC groups, respectively. Likewise, the OEF values displayed no significant differences across the NPHC and PHC categories. A positive correlation was established through correlation analysis between OEF values in brain regions like the frontal, occipital, and temporal gyri and the factors of age, gestational week, body mass index, and mean blood pressure in the preeclampsia group.
This JSON schema offers a set of ten sentences, each different from the original, as requested (0361-0812).
Utilizing whole-brain voxel-based morphometry, we observed a higher oxygen extraction fraction (OEF) in preeclampsia patients in comparison to control participants.
Through whole-brain VBM techniques, we determined that individuals with preeclampsia showed elevated oxygen extraction fractions when compared to healthy controls.

We hypothesized that deep learning-driven CT image standardization could improve the accuracy of automated hepatic segmentation, leveraging deep learning algorithms across diverse reconstruction methods.
Dual-energy CT scans of the abdomen, which included contrast enhancement and were reconstructed using various methods—filtered back projection, iterative reconstruction, optimal contrast settings, and monoenergetic images at 40, 60, and 80 keV—were gathered. A deep learning algorithm for image conversion of CT scans was designed to provide standardized output, incorporating 142 CT examinations (128 for training purposes and 14 for subsequent refinement). Forty-three computed tomography (CT) examinations, conducted on 42 patients (average age 101 years), comprised the test data. In the realm of commercial software, MEDIP PRO v20.00 stands out as a notable program. Employing 2D U-NET, MEDICALIP Co. Ltd. developed liver segmentation masks that incorporate liver volume data. As a benchmark, the original 80 keV images were employed. The paired method facilitated our successful completion of the task.
Determine the segmentation performance by examining the Dice similarity coefficient (DSC) and the relative difference in liver volume compared to ground truth, pre and post-image standardization. To evaluate the alignment between the segmented liver volume and the ground truth volume, the concordance correlation coefficient (CCC) was employed.
The CT images, originally assessed, exhibited inconsistent segmentation outcomes that were, at times, inadequate. check details A significant enhancement in Dice Similarity Coefficient (DSC) for liver segmentation was observed using standardized images, compared to the original images. While the original images yielded a DSC range of 540% to 9127%, the standardized images demonstrated a considerably higher DSC range of 9316% to 9674%.
Ten unique sentences, structurally distinct from the original, are returned in this JSON schema, which lists the sentences. A significant decrease in the liver volume difference ratio was evident after the conversion to standardized images. The original range spanned from 984% to 9137%, whereas the standardized range was 199% to 441%. In every protocol, image conversion yielded an enhancement in CCCs, evolving from the original -0006-0964 to the standardized 0990-0998 metric.
Improvements in automated hepatic segmentation using CT images, reconstructed by different techniques, are possible with deep learning-based CT image standardization. Deep learning-powered CT image conversion may contribute to a more generalizable segmentation network.
Deep learning-based CT image standardization procedures can lead to enhanced performance metrics for automated hepatic segmentation utilizing CT images reconstructed through different methods. Deep learning's application to converting CT images might boost the generalizability of the segmentation network.

Patients who have undergone an ischemic stroke are statistically more likely to experience a second ischemic stroke event. Using perfluorobutane microbubble contrast-enhanced ultrasonography (CEUS), we investigated whether carotid plaque enhancement is associated with future recurrent stroke, and if such enhancement can refine stroke risk assessment beyond what is currently available with the Essen Stroke Risk Score (ESRS).
A prospective study involving patients with recent ischemic stroke and carotid atherosclerotic plaques, screened at our hospital between August 2020 and December 2020, comprised 151 individuals. Analysis was conducted on 130 of the 149 eligible patients who underwent carotid CEUS, these patients being followed up for 15 to 27 months or until stroke recurrence. The study explored if contrast-enhanced ultrasound (CEUS) findings of plaque enhancement are indicative of an increased risk of stroke recurrence, and if it could provide an additional benefit alongside existing endovascular stent-revascularization surgery (ESRS).
During the follow-up period, a total of 25 patients demonstrated recurrent stroke events, amounting to 192% of the observed group. Patients exhibiting plaque enhancement on contrast-enhanced ultrasound (CEUS) were found to have a significantly higher likelihood of experiencing recurrent stroke events (22 out of 73 patients, representing a 30.1% rate) compared to those not exhibiting such enhancement (3 out of 57 patients, or 5.3%), as indicated by an adjusted hazard ratio (HR) of 38264 (95% confidence interval [CI] 14975 to 97767).
A multivariable Cox proportional hazards model analysis revealed that carotid plaque enhancement significantly predicted recurrent stroke, independently. The hazard ratio for stroke recurrence in patients at high risk, in comparison to those at low risk, demonstrated a greater value (2188; 95% CI, 0.0025-3388) when plaque enhancement was incorporated into the ESRS, contrasting with the hazard ratio associated with the ESRS alone (1706; 95% CI, 0.810-9014). Plaque enhancement, added to the ESRS, effectively and appropriately reclassified upward 320% of the recurrence group's net.
Stroke recurrence in ischemic stroke patients was significantly and independently predicted by the enhancement of carotid plaque. Importantly, the inclusion of plaque enhancement increased the effectiveness of the ESRS's risk stratification protocol.
A substantial and independent predictor of stroke recurrence in ischemic stroke patients was the presence of carotid plaque enhancement. check details Subsequently, the incorporation of plaque enhancement yielded a more robust risk stratification capacity within the ESRS.

We describe the clinical and radiological characteristics of patients with B-cell lymphoma and COVID-19, showing migrating airspace opacities on repeated chest CT scans, while experiencing enduring COVID-19 symptoms.
From January 2020 to June 2022, seven adult patients with pre-existing hematologic malignancy and exhibiting migratory airspace opacities on multiple chest CT scans following COVID-19 infection at our hospital (5 female, 37-71 years old, median age 45) were selected for analysis of their clinical and CT features.
The COVID-19 diagnosis in all patients was preceded by a diagnosis of B-cell lymphoma, encompassing three instances of diffuse large B-cell lymphoma and four instances of follicular lymphoma, coupled with B-cell-depleting chemotherapy, including rituximab, administered within three months of their diagnosis. A median of 3 computed tomography (CT) scans was administered to patients during the follow-up period, which lasted a median of 124 days. Multifocal, patchy ground-glass opacities (GGOs) were evident in the peripheral lung fields of all patients' baseline CTs, with a particular concentration at the basal regions. In each patient, subsequent CT scans revealed the resolution of prior airspace opacities, accompanied by the emergence of new peripheral and peribronchial ground-glass opacities (GGOs) and consolidation in diverse anatomical sites. Throughout the follow-up timeframe, each patient displayed enduring COVID-19 symptoms, corroborated by positive polymerase chain reaction results from nasopharyngeal swabs, with cycle threshold values consistently below 25.
B-cell lymphoma patients, having received B-cell depleting therapy, experiencing prolonged SARS-CoV-2 infection and persistent symptoms, may show migratory airspace opacities on serial CT scans, mirroring the appearance of ongoing COVID-19 pneumonia.
Patients with COVID-19 and B-cell lymphoma who have undergone B-cell depleting therapy and are experiencing prolonged SARS-CoV-2 infection and persistent symptoms could show migratory airspace opacities on successive CT imaging studies, leading to a possible misdiagnosis of ongoing COVID-19 pneumonia.