Volume Rendering Research Articles

Segmental evaluation of predictive value of left atrial epicardial adipose tissue following catheter ablation for atrial fibrillation

BackgroundLeft atrial epicardial adipose tissue (LA-EAT) is associated with the recurrence of atrial tachyarrhythmias (AF/AT) after catheter ablation for atrial fibrillation (AF). However, no previous studies have assessed the predictive value of segment-specific LA-EAT volumes for AF/AT recurrence. This study aimed to assess the relationship between segmental LA-EAT volume and AF/AT recurrence. MethodsThis study included 350 consecutive patients who underwent initial AF ablation (53.7 % paroxysmal AF (PAF)). Preoperative multidetector row computed tomography assessed LA-EAT, categorized into three segments: anterior-EAT, posterior-EAT, and interatrial septal adipose tissue (IAS-AT). ResultsDuring a mean follow-up period of 351 ± 109 days, 56 patients (16.0 %) experienced AF/AT recurrence. The mean LA-EAT volume was 20.7 ± 11.1 ml and LA-EAT ≥26.8 ml was an independent risk factor for AF/AT recurrence (HR 2.21, 95 % confidence interval (CI): 1.24–3.93, P = 0.007). Receiver operating characteristic analyses revealed the area under the curve for IAS-AT was 0.669 (95 % CI: 0.596–0.743) with an optimal cut-off point of 1.3 ml (sensitivity 76.8 %; specificity 50.0 %), significantly outperforming the anterior- and posterior-EAT in predicting recurrent AF/AT. Multivariate analysis indicated IAS-AT was an independent predictor of AF/AT recurrence in patients with persistent AF (PeAF) (HR 3.52, 95 % CI: 1.52–8.13, P = 0.003), but not in patients with PAF. ConclusionsLA-EAT predicts AF/AT recurrence after AF ablation, with IAS-AT proving significantly more effective than other LA-EAT segments in predicting recurrence. Notably, IAS-AT emerged as an independent predictor of AF/AT recurrence in patients with PeAF but not in those with PAF.

Liver segmental volumes and their relationship with 5-year prognostication.

This study aimed to analyze the predictive value of caudate to right lobe ratio (CRL-R) and liver segmental volume ratio (LSVR) for chronic liver disease (CLD) on routine abdominal CT scans and their association with 5-year decompensation- and transplant-free survival. This retrospective study included 108 patients without CLD and 98 patients with biopsy-proven CLD. All patients underwent abdominal CT scans between 03/2015 and 08/2017. Patients with CLD were divided into three groups: early CLD (F0-F2; eCLD; n = 40), advanced CLD (F3-F4; aCLD; n = 20), and aCLD with clinically significant portal hypertension (aCLDPH; n = 38). CRL-R and LSVR were compared between groups using Kruskal-Wallis test and ROC analysis to determine cutoff-values. 5-year decompensation- and transplant-free survival were assessed by Kaplan-Meier curve analysis. CRL-R and LSVR were significantly different between all groups (p < 0.001). A CRL-R cutoff-value of > 0.99 predicted aCLD with a sensitivity of 69% and a specificity of 80% (AUC = 0.75, p < 0.001), while LSVR > 0.37 had a sensitivity of 67% and a specificity of 84% (AUC = 0.80, p < 0.001). CLD-patients with both CRL-R > 0.99 and LSVR > 0.37 had a significantly lower probability of 5-year decompensation-free survival (31%) as well as lower probability of 5-year transplant-free survival (41%) than those with a CRL-R < 0.99 and/or LSVR < 0.37 (70%, 62%, p = 0.006, p = 0.038). CRL-R and LSVR showed a high predictive value for CLD on routine abdominal CT scans. In patients with CLD, both CRL-R and LSVR may be combined and are associated with 5-year decompensation-free and transplant-free survival.

The Bony Nasal Cavity and Paranasal Sinuses of Big Felids and Domestic Cat: A Study Using Anatomical Techniques, Computed Tomographic Images Reconstructed in Maximum-Intensity Projection, Volume Rendering and 3D Printing Models.

This study aims to develop three-dimensional printing models of the bony nasal cavity and paranasal sinuses of big and domestic cats using reconstructed computed tomographic images. This work included an exhaustive study of the osseous nasal anatomy of the domestic cat carried out through dissections, bone trepanations and sectional anatomy. With the use of OsiriX viewer, the DICOM images were postprocessed to obtaining maximum-intensity projection and volume-rendering reconstructions, which allowed for the visualization of the nasal cavity structures and the paranasal sinuses, providing an improvement in the future anatomical studies and diagnosis of pathologies. DICOM images were also processed with AMIRA software to obtain three-dimensional images using semiautomatic segmentation application. These images were then exported using 3D Slicer software for three-dimensional printing. Molds were printed with the Stratasys 3D printer. In human medicine, three-dimensional printing is already of great importance in the clinical field; however, it has not yet been implemented in veterinary medicine and is a technique that will, in the future, in addition to facilitating the anatomical study and diagnosis of diseases, allow for the development of implants that will improve the treatment of pathologies and the survival of big felids.

Anatomical and subcortical invasiveness in diffuse low-grade astrocytomas differ between IDH status and provide prognostic information.

Diffuse astrocytomas preferentially infiltrate eloquent areas affecting the outcome. A preoperative understanding of isocitrate dehydrogenase (IDH) status may offer opportunities for specific targeted therapies impacting treatment management. The aim of this study was to analyze clinical, topographical, radiological in WHO 2 astrocytomas with different IDH status and the long-term patient's outcome. A series of confirmed WHO 2 astrocytoma patients (between 2005 and 2015) were retrospectively analyzed. MRI sequences (FLAIR) were used for tumor volume segmentation and to create a frequency map of their locations into the Montreal Neurological Institute (MNI) space. The Brain-Grid (BG) system (standardized radiological tool of intersected lines according to anatomical landmarks) was used as an overlay for infiltration analysis of each tumor. Long-term follow-up was used to perform a survival analysis. Forty patients with confirmed IDH status (26 IDH-mutant, IDHm/14 IDH-wild type, IDHwt) according to WHO 2021 classification were included with a mean follow-up of 7.8 years. IDHm astrocytomas displayed a lower number of BG-voxels (P < 0.05) and were preferentially located in the anterior insular region. IDHwt group displayed a posterior insular and peritrigonal location. IDHwt group displayed a shorter OS compared with IDHm (P < 0.05), with the infiltration of 7 or more BG-voxels as an independent factor predicting a shorter OS. IDHm and IDHwt astrocytomas differed in preferential location, number of BG-voxels and OS at long follow-up time. The number of BG-voxels affected the OS in IDHwt was possibly reflecting higher tumor invasiveness. We encourage the systematic use of alternative observational tools, such as gradient maps and the Brain-Grid analysis, to better detect differences of tumor invasiveness in diffuse low-grade gliomas subtypes.

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT

PurposeThe purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening. Materials and methodsPatients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses. ResultsA total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20–85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5–1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80–98), 76 % specificity (95 % CI: 62–88) and an AUC of 0.87 (95 % CI: 0.79–0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79–0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images. ConclusionRadiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.

Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis.

Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm. Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs. Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05). Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.

Application of Artificial Intelligence in Cone-Beam Computed Tomography for Airway Analysis: A Narrative Review.

Cone-beam computed tomography (CBCT) has emerged as a promising tool for the analysis of the upper airway, leveraging on its ability to provide three-dimensional information, minimal radiation exposure, affordability, and widespread accessibility. The integration of artificial intelligence (AI) in CBCT for airway analysis has shown improvements in the accuracy and efficiency of diagnosing and managing airway-related conditions. This review aims to explore the current applications of AI in CBCT for airway analysis, highlighting its components and processes, applications, benefits, challenges, and potential future directions. A comprehensive literature review was conducted, focusing on studies published in the last decade that discuss AI applications in CBCT airway analysis. Many studies reported the significant improvement in segmentation and measurement of airway volumes from CBCT using AI, thereby facilitating accurate diagnosis of airway-related conditions. In addition, these AI models demonstrated high accuracy and consistency in their application for airway analysis through automated segmentation tasks, volume measurement, and 3D reconstruction, which enhanced the diagnostic accuracy and allowed predictive treatment outcomes. Despite these advancements, challenges remain in the integration of AI into clinical workflows. Furthermore, variability in AI performance across different populations and imaging settings necessitates further validation studies. Continued research and development are essential to overcome current challenges and fully realize the potential of AI in airway analysis.

Impact of antenatal corticosteroids on subcortical volumes in preterm infants at term-equivalent age: A retrospective observational study

ObjectiveAntenatal corticosteroids (ACS) is a well-established treatment for women at risk of preterm birth that improves neonatal outcomes. However, several concerns have been raised regarding the potential long-term adverse effects of ACS on the offspring’s developing brain. Here we investigated the association between ACS and subcortical segmental volumes in preterm infants at term-equivalent age. Study designThis retrospective observational study was conducted using the clinical data of preterm singleton infants born between 220/7 and 336/7 gestational weeks at Nagoya University Hospital in 2014–2020. Subcortical volumes of the bilateral thalami, caudate nuclei, putamens, pallidums, hippocampi, amygdalae, and nuclei accumbens were evaluated using an automated segmentation tool, Infant FreeSurfer, and compared between neonates exposed to a single course of ACS (n = 46) and those who were not (n = 13) by multiple linear regression analysis (covariates: postmenstrual age at magnetic resonance imaging, infant sex, and gestational age at birth). We compared each subcortical volume stratified by gestational age at birth (<28 vs. ≥28 gestational weeks). ResultsMultivariate analyses revealed significantly smaller volumes in the bilateral amygdalae (left, p < 0.03; right, p < 0.03) and caudate nuclei (left, p < 0.03; right, p = 0.04) in neonates with ACS. Significantly smaller volumes in these regions were observed only in neonates born at 28 weeks of gestation or later. ConclusionsACS was associated with smaller volumes of the bilateral amygdalae and caudate nuclei at term-equivalent age. This association was observed exclusively in infants born at 28 weeks of gestation or later.

Development and validation of a perivascular space segmentation method in multi-center datasets

BackgroundPerivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies. MethodsWe annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images. ResultsIn the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region. ConclusionThe mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.

Diagnostic accuracy of magnetic resonance imaging to predict peripartum hysterectomy and neonatal mortality in total placenta previa: A retrospective cohort study

ObjectivesTo assess the reliability of placental magnetic resonance imaging measurements in predicting peripartum hysterectomy and neonatal outcomes in patients with total placenta previa. Study DesignThis retrospective cohort study, conducted at a single tertiary center, identified 372 pregnant women diagnosed with placenta previa. 277 singleton pregnancies that met the inclusion criteria and were diagnosed with total placenta previa in the third trimester were divided into two groups according to whether a placental MRI was performed. Two radiologists analyzed the MRI findings of 150 pregnant women with total placenta previa. Measurements were conducted for the placental volume of the upper and lower uterine sectors, cervical canal length, and cervical canal dilatation. A comparison was made between the surgical progression of these pregnant women and 127 pregnant women with total placenta previa who did not undergo an MRI. After pathological examination, 122 (63.2%) of 193 pregnant women diagnosed with placenta accreta spectrum underwent peripartum total abdominal hysterectomy. The results were compared using logistic regression analysis. ResultsReduced placental volume in the upper uterine segment and increased volume in the lower uterine segment significantly correlated with a higher probability of peripartum hysterectomy (cut-off: ≤343.4 and ≥ 403.4 cm3; OR: 0.993, 95 % CI: 0.990–0.995 and OR: 1.007, 95 % CI: 1.005–1.009, respectively). Shortened cervical canal length and increased dilatation raise the risk of peripartum hysterectomy (cut-off: ≤34, ≥11 mm; OR: 0.82, 95 % CI: 0.77 − 0.88 and OR: 1.7, 95 % CI: 1.4 − 2.1, respectively). The risk of neonatal death is 32 times higher in those < 34 weeks than in those 34 weeks or higher (95 % CI: 4.2–250, p = 0.001). ConclusionsPlacental MRI significantly contributes to predicting peripartum total abdominal hysterectomy and neonatal mortality in patients with total placenta previa associated with placenta accreta spectrum.

The influence of tramadol on bowel function: A randomised, placebo-controlled trial.

Tramadol is a weak opioid used to treat moderate pain. Stronger opioids inhibit gastrointestinal function, but little is known about the gastrointestinal effects of tramadol. Our aim was to investigate if tramadol causes opioid-induced bowel dysfunction (OIBD). Twenty healthy male participants (mean age 24 [range 20-31] years) were included. Tramadol (extended-release formulation, 200 mg/day) or placebo was administered for 10days in two study periods separated by 3weeks. Gastrointestinal transit times and segmental volume, motility and water content were investigated with the 3D-transit system and magnetic resonance imaging. Bowel movements and gastrointestinal symptoms were recorded daily. Tramadol prolonged colonic transit time (34 h vs. 25 h, p< 0.001) and increased small bowel motility (p< 0.01) and water content (p= 0.002) compared to placebo. Across all days of treatment, tramadol reduced the number of mean daily bowel movements (p= 0.001) and increased mean stool consistency (p= 0.006). Gastrointestinal symptom scores increased with tramadol (indigestion: +358%, p= 0.01; constipation: +475%, p= 0.01). Additionally, more participants fulfilled the diagnostic criteria for constipation after tramadol treatment compared to placebo (40% vs. 0%, p< 0.001). This study showed that tramadol treatment is associated with OIBD, and management of constipation and other bowel symptoms should, therefore, be prioritised when treating pain patients with tramadol.

The influence of the molecular structure of binary block‐type styrene‐butadiene rubber on dynamic mechanical properties under normal and high‐pressure conditions

AbstractThrough anionic two‐stage polymerization, block‐type styrene‐butadiene rubber (SSBR) with systematically regulable low‐temperature loss peaks is successfully prepared. The influence of 1,2‐Bd unit in the soft segment of block‐type SSBR on mechanical properties, dynamic mechanical properties, and microscopic morphology is studied, while pressure factors are introduced into dynamic mechanical studies. The block‐type SSBR with a soft segment containing 21.9 wt% of 1,2‐Bd exhibits the highest tensile strength, elongation at break, and tear strength. Under atmospheric pressure, the soft segment 1,2‐Bd unit influences the dynamic mechanical properties by altering the chain segment's motion loss capability. As the mass fraction of 1,2‐Bd increases from 21.9 to 58.6 wt%, tanδmax increases, the glass transition temperature of the soft segment increases from −59.0 to −18.9°C. Additionally, influenced by thermodynamic compatibility, the microscopic morphology transitions from strong separation to weak separation. Pressure affects dynamic mechanical properties by altering the free volume of the chain segments. Pressure reduces tanδmax, increases Tg, and raises the average Tg of the soft segment by 11°C. The K value of the linear equation representing the relationship between the soft segment Tg and the mass fraction of 1,2‐Bd is unaffected by pressure.

Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning.

Time-of-flight magnetic resonance angiography (TOF-MRA) is a non-contrast technique used to visualize neurovascular. However, manual reconstruction of the volume render (VR) by radiologists is time-consuming and labor-intensive. Deep learning-based (DL-based) vessel segmentation technology may provide intelligent automation workflow. To evaluate the image quality of DL vessel segmentation for automatically acquiring intracranial arteries in TOF-MRA. A total of 394 TOF-MRA scans were selected, which included cerebral vascular health, aneurysms, or stenoses. Both our proposed method and two state-of-the-art DL methods are evaluated on external datasets for generalization ability. For qualitative assessment, two experienced clinical radiologists evaluated the image quality of cerebrovascular diagnostic and visualization (scoring 0-5 as unacceptable to excellent) obtained by manual VR reconstruction or automatic convolutional neural network (CNN) segmentation. The proposed CNN outperforms the other two DL-based methods in clinical scoring on external datasets, and its visualization was evaluated by readers as having the appearance of the radiologists' manual reconstructions. Scoring of proposed CNN and VR of intracranial arteries demonstrated good to excellent agreement with no significant differences (median, 5.0 and 5.0, P ≥ 12) at healthy-type scans. All proposed CNN image quality were considered to have adequate diagnostic quality (median scores > 2). Quantitative analysis demonstrated a superior dice similarity coefficient of cerebrovascular overlap (training sets and validation sets; 0.947 and 0.927). Automatic cerebrovascular segmentation using DL is feasible and the image quality in terms of vessel integrity, collateral circulation and lesion morphology is comparable to expert manual VR without significant differences.

Comprehensive sex-specific and age-dependent analysis of 4D-flow MRI assessed aortic blood flow-related parameters in normal subjects using single-vendor MR systems and single-vendor software.

Aortic blood flow characterization by 4D flow MRI is increasingly performed in aneurysm research. A limited number of studies have established normal values that can aid the recognition of abnormal flow at an early stage. This study aims to establish additional sex-specific and age-dependent reference values for flow-related parameters in a large cohort of healthy adults. 212 volunteers were included, and 191 volunteers completed the full study protocol. All underwent 4D flow MRI of the entire aorta. Quantitative values for velocity, vorticity, helicity, as well as total, circumferential, and axial wall shear stress [WSS] were determined for the aortic root [AoR], ascending aorta [AAo], aortic arch [AoA], descending [DAo], suprarenal [SRA], and infrarenal aorta [IRA]. Vorticity and helicity were indexed for segment volume (mL). The normal values were estimated per sex- and age-group, where significant differences between males (M) and females (F) were found only for specific age groups. More specifically, the following variables were significantly different after applying the false discovery rate correction for multiple testing: 1) velocity in the AAo and DAo in the 60-70 years age group (mean±SD: (M) 47.0 ± 8.2cm/s vs. (F) 38.4 ± 6.9cm/s, p=0.001 and, (M) 55.9 ± 9.9cm/s vs. (F) 46.5 ± 5.5cm/s, p=0.002), 2) normalized vorticity in AoR in the 50-59 years age group ((M) 27539 ± 5042s-1mL-1 vs. (F) 30849 ± 7285s-1mL-1, p=0.002), 3) axial WSS in the Aao in the 18-29 age group ((M) 1098 ± 203 mPa vs. (F) 921 ± 121 mPa, p=0.002). Good to strong negative correlations with age were seen for almost all variables, in different segments, and for both sexes. This study describes reference values for aortic flow-related parameters as acquired by 4D flow MRI. We observed limited differences between males and females. A negative relationship with age was seen for almost all flow-related parameters and segments.

Construction of low-resistance nano-transport channels with UiO-66-(NH2)2 “transmembrane” in polyamide layer structure for efficient nanofiltration

Metal-organic frameworks (MOFs) is always a research hotspot as the modifier for fabrication of thin-film nanocomposite (TFN) membrane due to their excellent chemical tunability and suitable pore size. However, the traditional strategy for the involvement of MOFs into interfacial polymerization (IP) process has its limitations, as the addition of MOFs only serve to modulate the structure of the polyamide (PA) layer and to optimize the transport path of water molecules, rather than play an important role in selectively sieving solutes. To solve the problem that MOF embedded under the PA layer cannot directly participate in solute sieving, this study designed a “transmembrane” structure of MOF in the PA layer. Here, the feasibility of 1,3,5-Benzenetricarbonyl chloride modified UiO-66-(NH2)2 was verified based on Gibbs free energy calculations. By adding chlorinated UiO-66-(NH2)2 nanomaterials in the dual IP process, a high-throughput nanofiltration membrane was innovatively constructed. In contrast to conventional thin-film composite membrane prepared by only one IP process, the sieving mechanism no longer relied solely on the free volume of the PA chain segments. Instead, it was determined by the combination of the pore channels of UiO-66-(NH2)2 and the matrix gaps. Based on density functional theory (DFT), the calculated diffusion energy barriers of H2O and Na2SO4 in UiO-66-(NH2)2 and PA matrices reveal the reasons for the improvement of water flux and changes in retention rate of TFN membranes. In addition, the gap size between PA-MOFs was determined by comparing the solvation and de-solvation structures of dye molecules. This work provides a new theoretical basis for explaining the changes in the separation performance of TFN membranes, and offers new ideas for the development of novel TFN membrane structures.

Leveraging Pretrained Transformers for Efficient Segmentation and Lesion Detection in Cone-Beam Computed Tomography Scans

IntroductionCone-beam computed tomography (CBCT) is widely used to detect jaw lesions, although CBCT interpretation is time-consuming and challenging. Artificial intelligence for CBCT segmentation may improve lesion detection accuracy. However, consistent automated lesion detection remains difficult, especially with limited training data. This study aimed to assess the applicability of pretrained transformer-based architectures for semantic segmentation of CBCT volumes when applied to periapical lesion detection. MethodsCBCT volumes (n = 138) were collected and annotated by expert clinicians using 5 labels – "lesion," "restorative material," "bone," "tooth structure," and "background." U-Net (convolutional neural network-based) and Swin-UNETR (transformer-based) models, pretrained (Swin-UNETR-PRETRAIN), and from scratch (Swin-UNETR-SCRATCH), were trained with subsets of the annotated CBCTs. These models were then evaluated for semantic segmentation performance using the Sørensen–Dice coefficient (DICE), lesion detection performance using sensitivity and specificity, and training sample size requirements by comparing models trained with 20, 40, 60, or 103 samples. ResultsTrained with 103 samples, Swin-UNETR-PRETRAIN achieved a DICE of 0.8512 for "lesion," 0.8282 for "restorative materials," 0.9178 for "bone," 0.9029 for "tooth structure," and 0.9901 for "background." “Lesion” DICE was statistically similar between Swin-UNETR-PRETRAIN trained with 103 and 60 images (P > .05), with the latter achieving 1.00 sensitivity and 0.94 specificity in lesion detection. With small training sets, Swin-UNETR-PRETRAIN outperformed Swin-UNETR-SCRATCH in DICE over all labels (P < .001 [n = 20], P < .001 [n = 40]), and U-Net in lesion detection specificity (P = .006 [n = 20], P = .031 [n = 40]). ConclusionsTransformer-based Swin-UNETR architectures allowed for excellent semantic segmentation and periapical lesion detection. Pretrained, it may provide an alternative with smaller training datasets compared to classic U-Net architectures.

Volumetric and textural analysis of PET/CT in patients with diffuse large B-cell lymphoma highlights the importance of novel MTVrate feature.

To investigate the prognostic value of clinical, volumetric, and radiomics-based textural parameters in baseline [18F]FDG-PET/CT scans of diffuse large B-cell lymphoma (DLBCL) patients. We retrospectively investigated baseline PET/CT scans and collected clinical data of fifty DLBCL patients. PET images were segmented semiautomatically to determine metabolic tumor volume (MTV), then the largest segmented lymphoma volume of interest (VOI) was used to extract first-, second-, and high-order textural features. A novel value, MTVrate was introduced as the quotient of the largest lesion's volume and total body MTV. Receiver operating characteristics (ROC) analyses were performed and 24-months progression-free survival (PFS) of low- and high-risk cohorts were compared by log-rank analyses. A machine learning algorithm was used to build a prognostic model from the available clinical, volumetric, and textural data based on logistic regression. The area-under-the-curve (AUC) on ROC analysis was the highest of MTVrate at 0.74, followed by lactate-dehydrogenase, MTV, and skewness, with AUCs of 0.68, 0.63, and 0.55, respectively which parameters were also able to differentiate the PFS. A combined survival analysis including MTV and MTVrate identified a subgroup with particularly low PFS at 38%. In the machine learning-based model had an AUC of 0.83 and the highest relative importance was attributed to three textural features and both MTV and MTVrate as important predictors of PFS. Individual evaluation of different biomarkers yielded only limited prognostic data, whereas a machine learning-based combined analysis had higher effectivity. MTVrate had the highest prognostic ability on individual analysis and, combined with MTV, it identified a patient group with particularly poor prognosis.

The Role of Advanced Post-processing Techniques in Computed Tomography Pulmonary Angiography for the Accurate Diagnosis of Pulmonary Thromboembolism: A Retrospective Study.

Background Computed tomography pulmonary angiography (CTPA) isthe standard diagnostic tool for evaluating patients with suspected pulmonary thromboembolism (PTE) in many institutions. This condition, whether acute or chronic, results in both partial and complete intraluminal filling defects, which exhibit sharp interfaces with intravascular contrast material. Acute PTE that leads to complete arterial occlusion may cause the affected artery to appear enlarged. Chronic PTE often manifests as complete occlusive disease in vessels that are smaller than the adjacent patent vessels. CT imaging with iodinated contrast medium is crucial for many CT applications, including vascular CT angiography and CTPA. A comprehensive review of a case necessitates an integrated approach known as volume visualization, wherein the entire case is treated as a volume of information to be thoroughly reviewed. Advanced post-processing 3D CT techniques, such as maximum intensity projection (MIP), volume rendering (VR), and minimum intensity projection (MinIP) images, are essential for the detailed detection and assessment of the pulmonary vasculature. Materials and methods In this retrospective study, data from 50 patients with suspected PTE were analyzed over a six-month period from March 15 to August 30, 2023, at Saveetha Medical College and Hospital. Patients were selected based on previously recorded clinical symptoms and elevated D-dimer levels. CTPA images, acquired using multi-detector CT imaging with iodinated contrast, were reviewed. Various post-processing techniques were employed, including multiplanar reconstruction (MPR), MIP, MinIP, and VR. The aim of this study was to evaluate the effectiveness of CTPA combined with advanced post-processing techniques in improving early detection, reducing diagnostic time, and increasing accuracy through the detailed visualization of the pulmonary arterial vasculature. Results The study included patients aged from 10 years to70 years, with the highest prevalence of PTE in the 21-35-year age group (46%). Males constituted 56% of the cases. CTPA with advanced post-processing techniques revealed filling defects in 90% of patients, confirming PTE. MPR, MIP, MinIP, and VR effectively highlighted anatomical structures and thrombi, enhancing diagnostic accuracy. These techniques demonstrated high accuracy in identifying PTE, emphasizing their critical role in the early diagnosis and management of thromboembolic events. Conclusion The findings of the study revealed a relatively high incidence of PTE especially in the 21-35-year age group with a slight male predominance. Thesignificant majority of the patients (90%) had filling defects on their CTPA scan. CTPA, in conjunction with the use of post-processing techniques, the localization of thromboembolism sites, as well as the measurement of thrombus width and length, and the calculation of the percentage of blockage were achieved more easily. This facilitatedaccurate diagnosis, leading to improved patient outcomes.

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images.

This article introduces a novel shallow 3-D self-supervised tensor neural network in quantum formalism for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as the 3-D quantum-inspired self-supervised tensor neural network (3-D-QNet). The underlying architecture of 3-D-QNet is composed of a trinity of volumetric layers, viz., input, intermediate, and output layers interconnected using an S -connected third-order neighborhood-based topology for voxelwise processing of 3-D medical image data, suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3-D-QNet is tailored and tested on the BRATS 2019 Brain MR image dataset and the Liver Tumor Segmentation Challenge (LiTS17) dataset extensively in our experiments. The 3-D-QNet has achieved promising dice similarity (DS) as compared with the time-intensive supervised convolutional neural network (CNN)-based models, such as 3-D-UNet, voxelwise residual network (VoxResNet), Dense-Res-Inception Net (DRINet), and 3-D-ESPNet, thereby showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.

Value of Dynamic Contrast-Enhanced MRI for Grade Group Prediction in Prostate Cancer: A Radiomics Pilot Study

Rationale and objectivesTo determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients. Materials and MethodsIn this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardization initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling. ResultsSeventy-three index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). Sixteen features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix [GLZLM]). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%. ConclusionDCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.