Age estimation by volumetric analysis of teeth using cone beam computed tomography.

OBJECTIVES: To evaluate the feasibility of using the pulp volume (Pv) to total volume (Tv) ratio (Pv:Tv), obtained from cone beam computed tomography (CBCT) scans of single-rooted teeth, for age estimation in a Brazilian population sample. METHODS: After obtaining approval from the ethics committee, the study commenced by applying inclusion criteria to screen CBCT scans, resulting in a probability-based sample of participants aged 18 years and older (ranging from 18 to 82 years, with a mean age of 46.44 years). A total of 517 single-rooted teeth, including maxillary central incisors (CI), mandibular canines (C), and mandibular first premolars (FP), were chosen based on excellent agreement values (> 0.9). Pv and Tv measurements were conducted using semi-automatic segmentation with ITK-SNAP 3.8 software. Statistical analysis was performed using Jamovi software, with a significance level set at 5% (α = 0.05). RESULTS: A strong negative correlation (r > -0.7) was observed between chronological age and the Pv:Tv ratio across all examined teeth. However, when conducting regression analysis with Pv:Tv data and chronological age as the independent variable, only the mandibular FP teeth exhibited a normal distribution. The resulting linear model demonstrated moderate predictive value (approximately 64%) in explaining the variance in chronological age, but caution should be exercised when interpreting these findings. CONCLUSIONS: The method of measuring individual tooth volume using CBCT to estimate chronological age via Pv:Tv has been demonstrated as effective and reproducible within the Brazilian population sample.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

An Open-source Image Analysis Toolbox for Quantitative Retinal Optical Coherence Tomography Angiography.

Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability. Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology. Material and Methods: In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret's diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index. Results: Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7. Conclusion: The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations.

A comparative study of the effectiveness of photogrammetric versus manual anthropometric measurements.

BACKGROUND: The accurate measurement of the human body is essential when it comes to designing agricultural tools and equipment that can effectively accommodate and interact with individuals when performing a task. The traditional method for measuring an individual's body measurements is highly complex and requires two or more skilled individuals and reliable measurement tools. Finding a new approach that is speedier, more precise, and less expensive than current methods is therefore necessary. OBJECTIVE: This study aims to develop an inexpensive novel photogrammetric anthropometric measurement setup that can extract the dimensions of an individual subject irrespective of their body shape. METHODS: This study involved the creation of a setup comprising four cameras for a 360° photoshoot of human subjects to calibrate and test the developed measurement setup for capturing photos of human subjects and compare the results with manual measurements. RESULTS: Ten different body dimensions were measured using the setup. There was a significant correlation between the manual and photogrammetric measurement methods (0.943 <  r <  0.997). The highest absolute error recorded was 1.87% . CONCLUSION: The photogrammetric method for collecting anthropometric data is a reliable substitute for manual measurements across diverse populations. The results indicate that this low-cost approach is highly precise and reliable, with strong correlation to manual measurements. Multiview photogrammetry proves effective for individuals of various body shapes, making it a versatile option for data collection.

[Digital transformation of a routine histopathology lab : Dos and don'ts!] / Digitalisierung der histopathologischen Routinediagnostik : Dos and Don'ts!

The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.

Three-dimensional aortic geometry mapping via registration of non-gated contrast-enhanced or gated and respiratory-navigated MR angiographies.

BACKGROUND: The measurement of aortic dimensions and their evolution are key in the management of patients with aortic diseases. Manual assessment, the current guideline-recommended method and clinical standard, is subjective, poorly reproducible, and time-consuming, limiting the capacity to track aortic growth in everyday practice. Aortic geometry mapping (AGM) via image registration of serial computed tomography angiograms outperforms manual assessment, providing accurate and reproducible 3D maps of aortic diameter and growth rate. This observational study aimed to evaluate the accuracy and reproducibility of AGM on non-gated contrast-enhanced (CE-) and cardiac- and respiratory-gated (GN-) magnetic resonance angiographies (MRA). METHODS: Patients with thoracic aortic disease followed with serial CE-MRA (n = 30) or GN-MRA (n = 15) acquired at least 1 year apart were retrospectively and consecutively identified. Two independent observers measured aortic diameters and growth rates (GR) manually at several thoracic aorta reference levels and with AGM. Agreement between manual and AGM measurements and their inter-observer reproducibility were compared. Reproducibility for aortic diameter and GR maps assessed with AGM was obtained. RESULTS: Mean follow-up was 3.8 ± 2.3 years for CE- and 2.7 ± 1.6 years for GN-MRA. AGM was feasible in the 93% of CE-MRA pairs and in the 100% of GN-MRA pairs. Manual and AGM diameters showed excellent agreement and inter-observer reproducibility (ICC>0.9) at all anatomical levels. Agreement between manual and AGM GR was more limited, both in the aortic root by GN-MRA (ICC=0.47) and in the thoracic aorta, where higher accuracy was obtained with GN- than with CE-MRA (ICC=0.55 vs 0.43). The inter-observer reproducibility of GR by AGM was superior compared to manual assessment, both with CE- (thoracic: ICC= 0.91 vs 0.51) and GN-MRA (root: ICC=0.84 vs 0.52; thoracic: ICC=0.93 vs 0.60). AGM-based 3D aortic size and growth maps were highly reproducible (median ICC >0.9 for diameters and >0.80 for GR). CONCLUSION: Mapping aortic diameter and growth on MRA via 3D image registration is feasible, accurate and outperforms the current manual clinical standard. This technique could broaden the possibilities of clinical and research evaluation of patients with aortic thoracic diseases.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Complimentary Cardiac Computed Tomography Ventricular Volumetry-Derived Metrics of Severity in Patients with Ebstein Anomaly: Comparison with Echocardiography-Based Severity Indices.

Detailed three-dimensional cardiac segmentations using cardiac computed tomography (CT) data is technically feasible in patients with Ebstein anomaly, but its complementary role has not been evaluated. This single-center, retrospective study was aimed to evaluate the complementary role of cardiac CT ventricular volumetry in evaluating the severity of Ebstein anomaly. Preoperative cardiac CT ventricular volumetry was performed in 21 children with Ebstein anomaly. CT-based ventricular functional measures were compared between Carpentier types, and between definitive surgical repair types. The Celermajer severity index measured with echocardiography was correlated with CT-based functional parameters. Total right ventricle (RV) and functional RV (fRV) volumes, fRV fraction, fRV/left ventricle (LV) volume ratio, and end-diastolic CT severity index demonstrated statistically significant differences between Carpentier type A/B and Carpentier type C/D (p < 0.05). The Celermajer severity index measured with echocardiography showed a high positive correlation with the end-diastolic CT severity index (R = 0.720, p < 0.002). There were no statistically significant differences in both echocardiography- and CT-based functional measures between patients with biventricular repair and patients with one-and-a-half or univentricular repair (p > 0.05). Compared with echocardiography, cardiac CT ventricular volumetry can provide the severity of Ebstein anomaly objectively and may be used in select patients when echocardiographic results are inconclusive or inconsistent.

[Current MR imaging of cartilage in the context of knee osteoarthritis (part 1) : Principles and sequences]. / Aktuelle MRT-Bildgebung des Knorpels im Kontext der Gonarthrose (Teil 1) : Grundlagen und Sequenzen.

Magnetic resonance imaging (MRI) is the clinical method of choice for cartilage imaging in the context of degenerative and nondegenerative joint diseases. The MRI-based definitions of osteoarthritis rely on the detection of osteophytes, cartilage pathologies, bone marrow edema and meniscal lesions but currently a scientific consensus is lacking. In the clinical routine proton density-weighted, fat-suppressed 2D turbo spin echo sequences with echo times of 30-40 ms are predominantly used, which are sufficiently sensitive and specific for the assessment of cartilage. The additionally acquired T1-weighted sequences are primarily used for evaluating other intra-articular and periarticular structures. Diagnostically relevant artifacts include magic angle and chemical shift artifacts, which can lead to artificial signal enhancement in cartilage or incorrect representations of the subchondral lamina and its thickness. Although scientifically validated, high-resolution 3D gradient echo sequences (for cartilage segmentation) and compositional MR sequences (for quantification of physical tissue parameters) are currently reserved for scientific research questions. The future integration of artificial intelligence techniques in areas such as image reconstruction (to reduce scan times while maintaining image quality), image analysis (for automated identification of cartilage defects), and image postprocessing (for automated segmentation of cartilage in terms of volume and thickness) will significantly improve the diagnostic workflow and advance the field further.

Feasibility of Two-Screw Anterior Fixation for Odontoid Fractures in a Chinese Population: A Morphometric Study Based on Computed Tomography.

Background: To evaluate the feasibility of treating odontoid fractures in the Chinese population with two cortical screws based on computed tomography (CT) scans and describe a new measurement strategy to guide screw insertion in treating these fractures. Methods: A retrospective review of cervical computed tomographic scans of 128 patients (aged 18-76 years; men, 55 [43.0%]) was performed. The minimum external transverse diameter (METD), minimum external anteroposterior diameter (MEAD), maximum screw length (MSL), and screw projection back angle (SPBA) of the odontoid process were measured on coronal and sagittal CT images. Results: The mean values of METD and MEAD were 10.0 ± 1.1 mm and 12.0 ± 1.0 mm, respectively, in men and 9.2 ± 1.0 mm and 11.0 ± 1.0 mm, respectively, in women. Both measurements were significantly higher in men (p < 0.001). In total, 87 individuals (68%) had METD > 9.0 mm that could accommodate two 3.5-mm cortical screws. The mean MSL value and SPBA range were 34.4 ± 2.9 mm and 13.5°-24.2°, respectively, with no statistically significant difference between men and women. Conclusions: The insertion of two 3.5-mm cortical screws was possible for anterior fixation of odontoid fractures in 87 patients (68%) in our study, and there was a statistically significant difference between men and women.

Mean S1 inlet and outlet view angles are not safe for all individuals according to three-dimensional tomographic measurements.

Although there are many studies evaluating optimal inlet and outlet angles required for the correct placement of S1 iliosacral screws, there is no study evaluating reliability and feasibility of these angles for all individuals on three-dimensional (3D) anatomical models. A total of 100 women and 100 men were selected randomly. A vertical line was created according to long axis of the tomography device on which patient was lying in supine position. The automatized best-fit planes were created on superior and inferior endplates, anterior cortex including notch region and posterior cortex of first sacral vertebrae using 3D imaging software to measure mean inlet and outlet angles. We observed no statistically significant difference between gender groups in terms of inlet and outlet angles. Mean inlet view is obtained for anterior cortex of S1 in 22.5 ± 9.5° and for posterior cortex in 46.5 ± 9.3°. Mean fluoroscopic view angle of S1 for superior outlet is 40.3 ± 7.6 and for inferior outlet is 46.9 ± 8.8. Mean anterior and posterior S1 inlet view angles do not accurately visualize anterior cortex of 74 (37%) and posterior cortex of 66 (33%) individuals. Mean superior and inferior S1 outlet view angles do not accurately visualize superior endplate of 74 (37%) and inferior endplate of 56 (28%) individuals. Due to individual alterations of spatial position of sacrum, mean inlet and outlet view angles of S1 are not sufficient to visualize the iliosacral screws under fluoroscopy in many individuals.

Use of a Novel Deep Learning Open-Source Model for Quantification of Ki-67 in Breast Cancer Patients in Pakistan: A Comparative Study between the Manual and Automated Methods.

Introduction: Breast cancer is the most common cancer in women; its early detection plays a crucial role in improving patient outcomes. Ki-67 is a biomarker commonly used for evaluating the proliferation of cancer cells in breast cancer patients. The quantification of Ki-67 has traditionally been performed by pathologists through a manual examination of tissue samples, which can be time-consuming and subject to inter- and intra-observer variability. In this study, we used a novel deep learning model to quantify Ki-67 in breast cancer in digital images prepared by a microscope-attached camera. Objective: To compare the automated detection of Ki-67 with the manual eyeball/hotspot method. Place and duration of study: This descriptive, cross-sectional study was conducted at the Jinnah Sindh Medical University. Glass slides of diagnosed cases of breast cancer were obtained from the Aga Khan University Hospital after receiving ethical approval. The duration of the study was one month. Methodology: We prepared 140 digital images stained with the Ki-67 antibody using a microscope-attached camera at 10×. An expert pathologist (P1) evaluated the Ki-67 index of the hotspot fields using the eyeball method. The images were uploaded to the DeepLiif software to detect the exact percentage of Ki-67 positive cells. SPSS version 24 was used for data analysis. Diagnostic accuracy was also calculated by other pathologists (P2, P3) and by AI using a Ki-67 cut-off score of 20 and taking P1 as the gold standard. Results: The manual and automated scoring methods showed a strong positive correlation as the kappa coefficient was significant. The p value was <0.001. The highest diagnostic accuracy, i.e., 95%, taking P1 as gold standard, was found for AI, compared to pathologists P2 and P3. Conclusions: Use of quantification-based deep learning models can make the work of pathologists easier and more reproducible. Our study is one of the earliest studies in this field. More studies with larger sample sizes are needed in future to develop a cohort.

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time.

OBJECTIVES: To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). METHODS: CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. RESULTS: The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. CONCLUSIONS: IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.

Computerized Tomography Texture Analysis in the Differential Diagnosis of Intracranial Epidermoid and Arachnoid Cysts.

PURPOSE: This study evaluated the differences between arachnoid and epidermoid cysts in computerized tomography (CT) texture analysis (TA). MATERIAL AND METHODS: The study included 12 patients with intracranial epidermoid cysts and 26 patients with intracranial arachnoid cysts who were diagnosed with diffusion-weighted magnetic resonance imaging (DW-MRI) and who had undergone an unenhanced CT examination before treatment. The LIFEx application software was used to obtain texture features. Eighty-two texture features from 38 lesions were automatically calculated for each lesion. The Shapiro-Wilk test was used to test the normality of the scores, and the Mann-Whitney U Test was used to test the difference between the groups. Receiver operating characteristic (ROC) curves and multivariate logistic regression modeling examined the parameters' diagnostic performances. RESULTS: The median age of the patients was 53 years (range: 19-88 years). Eighty-two texture parameters were evaluated in the first order: gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), neighbor gray-tone difference matrix (NGTDM), and gray-level size zone matrix (GLSZM) groups. There was a statistically significant difference between the arachnoid cyst and the epidermoid cyst in the variables of compacity, compactness 1, compactness 2, sphericity, asphericity, sum average, coarseness, and low gray-level zone (p<0.05). According to the multiple logistic regression model, it was determined that the sum average in the GLCM group (B=-0.11; p=0.015), coarseness (B= 869.5; p=0.044) in the NGTDM group, and morphological sphericity (B=24.18; p=0.047) were the radiomics variables that increased the probability of epidermoid diagnosis. According to the classification table of the model, the sensitivity rate was found to be 83%, and the specificity rate was found to be 96%. Therefore, the probability of accurate model classification was 92%. CONCLUSION: CT TA is a method that can be applied with high diagnostic accuracy in the differential diagnosis of intracranial epidermoid and arachnoid cysts, especially in patients who cannot undergo an MRI examination.

Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease.

BACKGROUND: Varying degrees of partial volume error depending on the complexity of the endocardial borders are inevitable in threshold-based cardiac computed tomography (CT) ventricular volumetry. These errors can potentially be reduced by using a partial voxel interpolation (PVI) method, but this has not been tested for cardiac CT ventricular volumetry. OBJECTIVE: To evaluate the partial volume error-reducing effects of the PVI method in cardiac CT ventricular volumetry among patients with congenital heart disease (CHD). MATERIALS AND METHODS: The cardiac CT ventricular volumetry data were obtained from 55 patients (median age 12.0 years) with CHD. The ventricular and myocardial volumes, ejection fraction and ventricular mass-volume ratio were quantified and compared before and after the PVI method. The correlation between the myocardial volumes in the end-systolic and end-diastolic phases was tested. The effect of the PVI method on the classification of ventricular hypertrophy was evaluated. RESULTS: The indexed ventricular volumes after PVI were significantly smaller (7.4-11.5%) than those before PVI (P<0.001). In contrast, the indexed myocardial volumes were significantly larger (6.2-27.7%) after PVI (P<0.001). The ejection fractions and mass-volume ratios were significantly larger (1.6-2.2% and 19.7-42.5%, respectively) after PVI (P<0.001 and P<0.001, respectively). The indexed myocardial masses showed prominently high correlation between the end-systolic and end-diastolic phases (R, 0.961-0.990; P<0.001). The proportions of no and severe hypertrophy were significantly decreased (P<0.002) and increased (P<0.032), respectively, after the application of the PVI method. CONCLUSION: The PVI method can reduce partial volume error in cardiac CT ventricular volumetry among patients with CHD.

Statistical shape analysis of corpus callosum in delusional disorder.

Neurobiological foundations of delusional disorder (DD) have been studied less with neuroimaging techniques when compared to other psychotic disorders. The present study aimed to delineate the neural substrates of DD by investigating neuroanatomical characteristics of the corpus callosum (CC) with statistical shape analysis (SSA) conducted on magnetic resonance images (MRI). Twenty (female:male=1:1) DSM-5 DD patients and 20 age- and gender-matched healthy individuals were included. High-resolution 3D T1 Turbo Field Echo MRI images were scanned with a 1.5 T MR device. The landmarks that were selected to determine the shape differences in CC were identified based on previous studies. Furthermore, constructed landmarks were determined and employed to better assess regional shape differences. There was no significant difference in the CC area in the mid-sagittal images between the DD patients and controls. However, DD patients exhibited a pattern of structural CC changes in various regions. The study findings emphasizes the variable subregional nature of CC in DD patients. Future SSA studies with larger samples could shed further light on DD etiology, diagnosis, classification and treatment options.

Supramaximal Resection for Glioblastoma: Redefining the Extent of Resection Criteria and Its Impact on Survival.

Glioblastomas (GBMs) are the most common and aggressive primary brain tumors, and despite advances in treatment, prognosis remains poor. The extent of resection has been widely recognized as a key factor affecting survival outcomes in GBM patients. The surgical principle of "maximal safe resection" has been widely applied to balance tumor removal and neurological function preservation. Historically, T1-contrast enhanced (T1CE) extent of resection has been the focus of research; however, the "supramaximal resection" concept has emerged, advocating for even greater tumor resection while maintaining neurological function. Recent studies have demonstrated potential survival benefits associated with resection beyond T1CE extent in GBMs. This review explores the developing consensus and newly established criteria for "supramaximal resection" in GBMs, with a focus on T2-extent of resection. Systematic reviews and meta-analyses on supramaximal resection are summarized, and the Response Assessment in Neuro-Oncology (RANO) resect group classification for extent of resection is introduced. The evolving understanding of the role of supramaximal resection in GBMs may lead to improved patient outcomes and more objective criteria for evaluating the extent of tumor resection.

Comparing Magnetic Resonance Fingerprinting (MRF) and the MAGiC Sequence for Simultaneous T1 and T2 Quantitative Measurements in the Female Pelvis: A Prospective Study.

The aim of this study was to explore the potential of magnetic resonance fingerprinting (MRF), an emerging quantitative MRI technique, in measuring relaxation values of female pelvic tissues compared to the conventional magnetic resonance image compilation (MAGiC) sequence. The study included 32 female patients who underwent routine pelvic MRI exams using anterior and posterior array coils on a 3T clinical scanner. Our findings demonstrated significant correlations between MRF and MAGiC measured T1 and T2 values (p < 0.0001) for various pelvic tissues, including ilium, femoral head, gluteus, obturator, iliopsoas, erector spinae, uterus, cervix, and cutaneous fat. The tissue contrasts generated from conventional MRI and synthetic MRF also showed agreement in bone, muscle, and uterus for both T1-weighted and T2-weighted images. This study highlights the strengths of MRF in providing simultaneous T1 and T2 mapping. MRF offers distinct tissue contrast and has the potential for accurate diagnosis of female pelvic diseases, including tumors, fibroids, endometriosis, and pelvic inflammatory disease. Additionally, MRF shows promise in monitoring disease progression or treatment response. Overall, the study demonstrates the potential of MRF in the field of female pelvic organ imaging and suggests that it could be a valuable addition to the clinical practice of pelvic MRI exams. Further research is needed to establish the clinical utility of MRF and to develop standardized protocols for its implementation in clinical practice.

NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner.

BACKGROUND: The Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies. METHODS: NEMA NU 1-2018 guidelines were applied to characterize the Spark's intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively. RESULTS: The limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%. CONCLUSIONS: NEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.