Magnetic Resonance Imaging Analysis Research Articles

Correlations of Characteristics With Tissue Involvement in Knee Gouty Arthritis: Magnetic Resonance Imaging Analysis

Correlations of Characteristics With Tissue Involvement in Knee Gouty Arthritis: Magnetic Resonance Imaging Analysis

Neuroanatomical alterations in brain disorder: A magnetic resonance imaging analysis

Background: Bipolar disorder is a serious mental disorder caused by strong mood fluctuations, affecting 2% of the world's population. People with bipolar disorder experience both manic and depressive episodes, which can lead to suicidal thoughts and changes in appetite, activity, and focus. There are different subtypes of bipolar disorder, with cyclothymic disorder being the milder of the two. Bipolar I is characterized by manic periods, while bipolar II is marked by hypomanic and significant depressive episodes. Bipolar disorder is a biological, genetic, and environmental condition that can affect anyone at any age. Methods: In this study, we enrolled a total of 49 bipolar disorder (BD) participants to 44 healthy control (HC) subjects. Bipolar disorder patients were enlisted with their consent using web portals, medical facilities, and local marketing. MRI contraindications, medical history, self-reported using a mood-altering medication, inadequate vision, and left-handedness were among the exclusion criteria. The various tissue structures which are: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) were examined using voxel-based morphometry (VBM) to ascertain these changes. We employed statistical parametric mapping (SPM) for both image processing and statistical analysis. Results: Our findings demonstrate that individuals with bipolar disorder showed a substantial volume decrease in both gray matter (GM) and white matter (WM), with gray matter (GM) and white matter (WM) volume decreases being more noticeable in the frontal, temporal, parietal, and occipital lobes. Additionally, individuals with bipolar disorder showed no CSF volume in any of the brain regions. Conclusion: Our VBM research backs up earlier studies that link changes in brain structure, particularly gray matter and white matter, to bipolar disorder. These discoveries shed more information on the neurology of bipolar disorder and could guide the creation of more potent diagnostic and treatment modalities.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7years). A researcher, under expert guidance, used in-house software to delineate regions of interest (ROIs) for hand muscles, bones, and synovitis, generating a dataset with 27,255 pixels with corresponding TICs (muscle: 11,413, bone: 8502, synovitis: 7340). One experienced musculoskeletal radiologist performed ground truth segmentation of enhanced pannus in the joint bounding box on the 10th DCE phase, or around 5min after contrast injection. Data preprocessing included median filtering for noise reduction, phase-only correlation algorithm for motion correction, and contrast-limited adaptive histogram equalization for improved image contrast and noise suppression. TIC intensity values were normalized using zero-mean normalization. A DL model with dilated causal convolution and SELU activation function was developed for enhanced pannus segmentation, tested using leave-one-out cross-validation. 407 joint bounding boxes were manually segmented, with 129 synovitis masks. On the pixel-based level, the DL model achieved sensitivity of 85%, specificity of 98%, accuracy of 99% and precision of 84% for enhanced pannus segmentation, with a mean Dice score of 0.73. The false-positive rate for predicting cases without synovitis was 0.8%. DL-measured enhanced pannus volume strongly correlated with ground truth at both pixel-based (r = 0.87, p < 0.001) and patient-based levels (r = 0.84, p < 0.001). Bland-Altman analysis showed the mean difference for hand joints at the pixel-based and patient-based levels were -9.46 mm3 and -50.87 mm3, respectively. Our DL-based DCE-MRI TIC shape analysis has the potential for automatic segmentation and quantification of enhanced synovium in the hands of RA patients.

Radiofrequency lesion in atrial wall: how variable is it? 9 Tesla MRI analysis of RF lesion volume in a swine model

Abstract Background Little is known about radiofrequency (RF) lesion volume (rLV). Most of animal or ex-vivo studies quantify the size of RF lesions size using width and/or depth, while electrophysiologists use manufacturers’ indirect indices (such as Ablation Index or Lesion Size Index). Purpose We aimed to evaluate rLV by high resolution 9-Tesla magnetic resonance imaging (MRI), in ex-vivo swine model. We studied the variability of rLV and extra-cardiac effects in two groups of swine, one treated with low energy (LE)-long application RF (20W-50 sec) and another treated with high energy (HE)-short application RF (50 W-20 sec). Methods This was a prospective observational study on 18 piglets, that were anesthetized, mechanically ventilated, and divided into LE (n = 9) and HE (n = 9) groups (Figure 1). An irrigated contact force catheter was used to perform RF applications in 4 largely separated spots: 2 on right atrium and 2 on left atrium wall. RF parameters were monitored during RF. Each animal was sacrificed after 5-7 days, and RF lesion samples were excised and acquired by 9 MRI. Results 70 RF applications were performed (36 in LE group and 34 in HE group). MRI was performed on 26 samples of LE group and 28 of HE group (Figure 2). The median RF lesion volume was 89.3 mm3 (IQR 47.9-175.9). Regarding lesion volume, the HE groups showed 35% higher volume than LE group (mean lesion volume 86.2 mm3 vs 116.3 mm3, p = 0.119). HE group also showed more complications (0 vs 7, p=0.02) and more extracardiac lesion (14 vs 18, p=0.613). Conclusions There was a wide and unpredictable variability of RF lesion on atrial wall, even under controlled experimental conditions, which could explain arrhythmias recurrences. The variability seems greatest, although not statistically significant, during HE applications. The dose/effect relationship needs to be carefully studied for treatments of cardiac arrhythmias for both RF and other forms of energy available today.Study flow-chartComparison between groups

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences.

Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

In situ temperature determination using magnetic resonance spectroscopy thermometry for noninvasive postmortem examinations.

Magnetic resonance spectroscopy (MRS) thermometry offers a noninvasive, localized method for estimating temperature by leveraging the temperature-dependent chemical shift of water relative to a temperature-stable reference metabolite under suitable calibration. Consequentially, this technique has significant potential as a tool for postmortem MR examinations in forensic medicine and pathology. In these examinations, the deceased are examined at a wide range of body temperatures, and MRS thermometry may be used for the temperature adjustment of magnetic resonance imaging (MRI) protocols or for corrections in the analysis of MRI or MRS data. However, it is not yet clear to what extent postmortem changes may influence temperature estimation with MRS thermometry. In addition, N-acetylaspartate, which is commonly used as an in vivo reference metabolite, is known to decrease with increasing postmortem interval (PMI). This study shows that lactate, which is not only present in significant amounts postmortem but also has a temperature-stable chemical shift, can serve as a suitable reference metabolite for postmortem MRS thermometry. Using lactate, temperature estimation in postmortem brain tissue of severed sheep heads was accurate up to 60 h after death, with a mean absolute error of less than 0.5°C. For this purpose, published calibrations intended for in vivo measurements were used. Although postmortem decomposition resulted in severe metabolic changes, no consistent deviations were observed between measurements with an MR-compatible temperature probe and MRS thermometry with lactate as a reference metabolite. In addition, MRS thermometry was applied to 84 deceased who underwent a MR examination as part of the legal examination. MRS thermometry provided plausible results of brain temperature in comparison with rectal temperature. Even for deceased with a PMI well above 60 h, MRS thermometry still provided reliable readings. The results show a good suitability of MRS thermometry for postmortem examinations in forensic medicine.

Characteristics and prognostic value of cardiac magnetic resonance strain analysis in patients with different phenotypes of heart failure

BackgroundStrain analysis of cardiac magnetic resonance imaging (CMR) is important for the prognosis of heart failure (HF). Herein, we aimed to identify the characteristics and prognostic value of strain analysis revealed by CMR in different HF phenotypes.MethodsParticipants with HF, including HF with reduced ejection fraction, HF with mildly reduced ejection fraction, and HF with preserved ejection fraction, and controls were enrolled. The baseline information and clinical parameters of participants were collected, and echocardiography and CMR examination were performed. Three-dimensional strain analysis was performed in the left ventricle, right ventricle, left atrium, and right atrium using CMR. A multifactor Cox risk proportional model was established to assess the influencing factors of cardiovascular adverse events in patients with HF.ResultsDuring a median follow-up of 999 days (range: 616–1334), 20.6% of participants (73/354) experienced adverse events (HF readmission and/or cardiovascular death). Univariable Cox regression revealed that a 1% increase in left atrial global longitudinal strain (LAGLS) was associated with a hazard ratio (HR) of 1.21 [95% confidence interval (CI):1.15–1.28; P &amp;lt; 0.001]. Left ventricular global circumferential strain (LVGCS) (HR, 1.18; 95% CI: 1.12–1.24; P &amp;lt; 0.001), and left ventricular global longitudinal strain (LVGLS) (HR, 1.27; 95% CI: 1.20–1.36; P &amp;lt; 0.001) were also associated with HF hospitalizations and cardiovascular deaths. Among clinical variables, hypertension (HR, 2.11; 95% CI: 1.33–13.36; P = 0.002), cardiomyopathy (HR, 2.26; 95% CI: 1.42–3.60; P &amp;lt; 0.001) were associated with outcomes in univariable analysis. Multivariable analyses revealed that LAGLS (95% CI: 1.08–1.29; P &amp;lt; 0.001), LVGLS (95% CI:1.08–1.29; P &amp;lt; 0.001) and LVGCS (95% CI: 1.19–1.51; P &amp;lt; 0.001) were significantly associated with outcomes. Among clinical variables, hypertension (95% CI: 1.09–3.73; P &amp;lt; 0.025) remained a risk factor.ConclusionCMR plays an obvious role in phenotyping HF. Strain analysis, particularly left atrial and left ventricular strain analysis (LAGLS, LVGLS, and LVGCS) has good value in predicting adverse outcome events.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes

This study introduces an ensemble classification model designed to categorize Alzheimer’s disease (AD) into four distinct classes—mild dementia, no dementia, moderate dementia, and very mild dementia—using Magnetic Resonance Imaging (MRI). The proposed model entitled the Ensemble Classification Model to Predict Alzheimer's Incidence as Multiple Classes (PAIMC) that integrates a six-dimensional analysis of MR images, encompassing entropies, Fractal Dimensions, Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), morphological features, and Local Binary Patterns. A four-fold multi-label cross-validation approach was employed on a benchmark dataset to evaluate the model's performance. Quantitative analysis reveals that PAIMC consistently achieves superior Decision Accuracy, F-Score, Specificity, Sensitivity Recall, and Precision metrics compared to existing state-of-the-art models. For instance, PAIMC's Decision Accuracy and Precision outperform the second-best model by a notable margin across all folds. The model also demonstrates a significant improvement in Sensitivity Recall and Specificity, reinforcing its efficacy in the multi-class classification of AD stages. A novel data diversity assessment measure was developed and utilized, further confirming the robustness of the PAIMC model. The results underscore the potential of PAIMC as a highly accurate tool for AD classification in clinical settings.

Periosteal wrapping of the hamstring tendon autograft improves graft healing and prevents tunnel widening after anterior cruciate ligament anatomic reconstruction.

The periosteum is a readily available tissue at the hamstring harvest site that could be utilized to enhance graft healing and prevent tunnel widening without additional cost or morbidity. This study aimed to compare graft healing using magnetic resonance imaging (MRI) and functional clinical outcome scores in a matched cohort of patients who underwent anterior cruciate ligament (ACL) reconstruction with hamstring autografts with or without periosteal augmentation. Forty-eight patients who underwent ACL reconstruction (ACLR) were prospectively enrolled: 25 with standard ACLR (ST-ACLR) and 23 with periosteal augmented grafts (PA-ACLR). The same surgical techniques, fixation methods, and postoperative protocol were used in both groups. Signal-to-noise quotient (SNQ), graft healing at the bone-graft interface, graft signal according to the Howell scale, and femoral tunnel widening were evaluated using MRI after 1 year of follow-up. International knee documentation score (IKDC), Lysholm, Tegner activity scale, and visual analog scale for pain were used for functional evaluation at a minimum of 2years postoperative. The mean SNQ of the proximal part of the graft was 9.6 ± 9.2 and 2.9 ± 3.3 for the ST-ACLR and PA-ACLR groups, respectively (P = 0.005). The mean femoral tunnel widening was 30.3% ± 18.3 and 2.3% ± 9.9 for the ST-ACLR, PA-ACLR groups, respectively (P < 0.001). Complete graft tunnel healing was observed in 65% and 28% of cases in the PA-ACLR and ST-ACLR groups, respectively. Both groups showed marked improvements in functional scores, with no statistically significant differences. Periosteal wrapping of hamstring tendon autografts is associated with better graft healing and maturation and lower incidence of femoral tunnel widening based on MRI analysis 1year after ACL reconstruction. However, patient-reported outcomes and measured laxity were similar between the two groups at 2years follow up. Trail registration number: PACTR202308594339018, date of registration: 1/5/2023, retrospectively registered at the Pan African Clinical Trial Registry (pactr.samrc.ac.za) database.

Green synthesis and optimization of iron oxide nanoparticles as MRI contrast agent

Iron nanoparticles (FeNPs) derived from herbal plants are being explored as potential magnetic resonance imaging (MRI) contrast agents due to their biocompatibility and ability to perform targeted drug delivery. For the first time, the usage of various novel combinations of leaf extracts such as those of Azadirachta indica and Piper betle and extracts of two other Ayurvedic herbs – namely, Asparagus racemosus and Symplocos – was attempted for eco-friendly green synthesis of FeNPs. The nanoparticles were coated onto nanofibers made from a banana pseudo-stem (FENPs-NF) to enhance their biocompatibility. The synthesis process was optimized using response surface methodology, and techniques such as ultraviolet–visible (UV–Vis) spectroscopy, vibrating-sample magnetometry, atomic absorption spectroscopy (AAS), field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDX) and Fourier transform infrared spectroscopy (FT-IR) were used for characterization. Hemolytic and immune cell assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and MRI analysis were carried out. A peak at 325 nm observed in UV–Vis spectroscopy, AAS, FE-SEM, EDX and FT-IR proved the presence of FeNPs. Hemolytic and immune cell assays indicated biocompatibility, and the MTT assay (MCF-7) showed inhibition at 250 μg/ml. FeNPs and FeNPs-NF showed a good signal loss in MRI analysis at 1.5 T, proving their contrast agent nature. Thus, FeNPs synthesized from herbal plant sources may open a new door for potential application in the biomedical field as a chemical-free and biocompatible MRI contrast agent.

Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era.

Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment. Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model's performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p < 0.05 was employed to determine statistical significance. Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.

Assessment of inter- and intraobserver agreement for META score in distinguishing osteoporotic from multiple myeloma vertebral fractures.

To conduct an independent assessment of inter- and intraobserver agreement for the META score as a tool for differentiating osteoporotic vertebral fractures and multiple myeloma vertebral fractures. This is a retrospective observational study. The magnetic resonance imaging analysis was made by two independent spinal surgeons. We designated a Subjective assessment, in which the surgeon should establish a diagnostic classification for each vertebral fracture based on personal experience: secondary to osteoporosis, categorized as a benign vertebral fracture (BVF), or attributed to multiple myeloma, categorized a malign vertebral fracture (MVF). After a 90-day interval, both surgeons repeated the evaluations. For the next step, the observers should establish a diagnosis between BVF and MVF according to the META score system, and both observers repeated the evaluations after a 90-day interval. The intra and interobserver reliability of the Subjective evaluation was studied using the kappa (κ) test. Then, the META evaluations were paralleled using the intraclass correlation coefficient (ICC). A total of 220 patients who had the potential to participate in the study were initially enrolled, but after applying the exclusion criteria, 44 patients were included. Thirty-three patients had BVF, and 12 patients presented MVF. Interobserver agreement for both Subjective evaluations moments (initial and 90-days interval) found a slight agreement for both moments (0.35 and 0.40 respectively). Kappa test for both META evaluations moments (initial and 90-days interval) found a moderate interobserver agreement for both moments (0.54 and 0.48 respectively). It was observed that the ICC calculated for the Initial evaluation using META score was 0.680 and that in the 90-days interval was 0.726, indicating regular to good agreement. Kappa test for intraobserver agreements for the Subjective evaluation presented moderate agreement for both Surgeons. On the other side, Kappa test for intraobserver agreements for the META evaluation presented substantial agreement for both Surgeons. The Intraclass Correlation Coefficient of the META score found presented an almost perfect agreement for both Surgeons. Intra and interobserver agreement for both surgeons were unsatisfactory. The lack of consistent reproducibility by the same observer discourages and disfavors the routine use of the META score in clinical decision making, when potentially cases of multiple myeloma may be present.

Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.

In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established techniques, FreeSurfer-Aseg and FSL-FIRST, using three-dimensional T1-weighted MRI scans (n = 1447) procured from public databases. Our evaluation focused on the accuracy and reproducibility of these tools in estimating hippocampal volume. The findings suggest that both SynthSeg and TigerBx are on a par with Aseg and FIRST in terms of segmentation accuracy and reproducibility, but offer a significant advantage in processing speed, generating results in less than 1 min compared with several minutes to hours for the latter tools. In terms of Alzheimer's disease classification based on the hippocampal atrophy rate, SynthSeg and TigerBx exhibited superior performance. In conclusion, we evaluated the capabilities of two deep learning-based segmentation techniques. The results underscore their potential value in clinical and research environments, particularly when investigating neurological conditions associated with hippocampal structures.

Cortex and White Matter of the Cerebral Hemispheres: Anatomical Correlations and Age-Related Changes Measured with Fractal Analysis

Aim. The aim of the present study was to determine the fractal dimension (FD) values of the cortex and white matter of the cerebral hemispheres using fractal analysis of two-dimensional magnetic resonance images, explore the anatomical correlations of the cortex and white matter FD, and study age-associated changes in the cortex and white matter. Methods. Two-dimensional brain magnetic resonance images of 100 apparently healthy individuals of both genders (44 males and 56 females) aged 18-86 years were studied. Five sections of each participant’s brain were selected (4 coronal and 1 axial). After image segmentation, the FD values and sectional areas of the cortex and white matter were determined. Fractal analysis was conducted using a two-dimensional variant of the box-counting method. Results. The FD values of the cortex and white matter varied across the five brain sections analyzed. Specifically, the cortex exhibited a decrease in the FD, whereas the white matter showed an increase in the FD in the coronal sections along the rostro-caudal direction. The FD values obtained from different sections displayed weak to moderate correlations. Additionally, no significant differences were observed in the FD values of the cortex and white matter between males and females. However, the sectional area values of the cortex and white matter were slightly higher in males as compared to females. Furthermore, a negative correlation was identified between the FD values of the cortex and white matter of the cerebral hemispheres, while sectional areas did not exhibit significant correlations. The cortex FD positively correlated with the gyrification index, whereas the white matter FD showed a negative correlation with this parameter. Additionally, both the FD and sectional area values of the cortex displayed strong and moderate negative correlations with age, respectively. In contrast, the FD and sectional area values of the white matter demonstrated weak negative correlations with age. Males showed stronger correlations of the studied parameters with age across the majority of the analyzed sections compared to females, although these differences were not statistically significant. Conclusions. In this study, a negative correlation was found between the cortex and white matter FD values, influenced by anatomical factors such as the degree of gyrification. While the cortex FD values significantly decreased with age, age-related changes in the white matter FD values were relatively weak.

Toward confident prostate cancer detection using ultrasound: a multi-center study.

Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue. Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods. PCa detection models achieve performance scores up to average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue. Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.

Ventriculovesical Shunting in a Patient with Leptomeningeal Carcinomatosis: An Alternative to VP Shunts. A Technique Report

AbstractLeptomeningeal carcinomatosis (LC) is a rare but serious complication when cancer cells infiltrate the meninges. It is most commonly associated with breast cancer, but only 5% of breast cancer patients develop it. Leptomeningeal carcinomatosis typically presents with headaches, mainly due to hydrocephalus, and the diagnosis involves a cytological analysis of cerebrospinal fluid (CSF) and/or magnetic resonance imaging (MRI) scans. The treatment of LC consists of a combination of intra-CSF chemotherapy, systemic therapy, radiation therapy, and/or supportive care, including CSF drainage. In the case herein reported, a technique known as ventriculovesical shunting was performed on a female patient with LC and breast cancer who had hydrocephalus due to this condition. This procedure is not as common as ventriculoperitoneal shunts, which can lead, in this case, to serious complications such as peritoneal carcinomatosis.

Exploring intricate connectivity patterns for cognitive functioning and neurological disorders: incorporating frequency-domain NC method into fMRI analysis.

This study extends the application of the frequency-domain new causality method to functional magnetic resonance imaging analysis. Strong causality, weak causality, balanced causality, cyclic causality, and transitivity causality were constructed to simulate varying degrees of causal associations among multivariate functional-magnetic-resonance-imaging blood-oxygen-level-dependent signals. Data from 1,252 groups of individuals with different degrees of cognitive impairment were collected. The frequency-domain new causality method was employed to construct directed efficient connectivity networks of the brain, analyze the statistical characteristics of topological variations in brain regions related to cognitive impairment, and utilize these characteristics as features for training a deep learning model. The results demonstrated that the frequency-domain new causality method accurately detected causal associations among simulated signals of different degrees. The deep learning tests also confirmed the superior performance of new causality, surpassing the other three methods in terms of accuracy, precision, and recall rates. Furthermore, consistent significant differences were observed in the brain efficiency networks, where several subregions defined by the multimodal parcellation method of Human Connectome Project simultaneously appeared in the topological statistical results of different patient groups. This suggests a significant association between these fine-grained cortical subregions, driven by multimodal data segmentation, and human cognitive function, making them potential biomarkers for further analysis of Alzheimer's disease.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PurposeTo evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. MethodsThis prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. Resultskep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). ConclusionThe inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.