T2-weighted Images Research Articles

Deep learning radiomics model based on contrast-enhanced MRI for distinguishing between tuberculous spondylitis and pyogenic spondylitis.

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) to differentiate between tuberculous spondylitis (TS) and pyogenic spondylitis (PS) using contrast-enhanced MRI (CE-MRI). A retrospective approach was employed, enrolling patients diagnosed with TS or PS based on pathological examination at two centers. Clinical features were evaluated to establish a clinical model. Radiomics and deep learning (DL) features were extracted from contrast-enhanced T1-weighted images and subsequently fused. Following feature selection, radiomics, DL, combined DL-radiomics (DLR), and a deep learning radiomics nomogram (DLRN) were developed to differentiate TS from PS. Performance was assessed using metrics including the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). A total of 147 patients met the study criteria. Center 1 comprised the training cohort with 102 patients (52 TS and 50 PS), while Center 2 served as the external test cohort with 45 patients (17 TS and 28 PS). The DLRN model exhibited the highest diagnostic accuracy, achieving an AUC of 0.994 (95% CI: 0.983-1.000) in the training cohort and 0.859 (95% CI: 0.744-0.975) in the external test cohort. Calibration curves indicated good agreement for DLRN, and decision curve analysis (DCA) demonstrated it provided the greatest clinical benefit. The CE-MRI-based DLRN showed robust diagnostic capability for distinguishing between TS and PS in clinical practice.

Pituitary Adenomas in Children: : Specific Imaging Features According to Hormonal Secretion.

Pituitary adenomas are much rarer in children than in adults. We aimed to analyze their imaging characteristics in this age group and to compare them according to the hormonal secretion. We conducted an observational monocentric retrospective study on clinical and imaging data. We analyzed imaging features before surgery or drug treatment of pituitary adenomas in children confirmed by histopathology or hormonal secretion. We assessed tumoral signal intensity, volume and aggressiveness, and compared it according to the hormonal secretion. We included 31 children (13 lactotroph (42%), 8corticotroph (26%), 5somatotroph (16%), 5plurihormonal adenomas (16%) including 1 non-secreting macroadenoma) with a median age of 13years (range 2-16years-old), without age or sex difference between secretion types. Lactotroph and somatotroph adenomas were larger than corticotroph adenomas (p = 0.007) and were more aggressive (p = 0.01). They also had higher signal intensity on T2-weighted images (p = 0.04). T1 signal intensity was similar between the groups on pre and post-contrast images (lower enhancement than the normal pituitary). No non-secreting micro-adenoma became clinically significant enough to lead to a pathological confirmation or specific treatment. Genetic research was conducted on 20children, finding MEN1 mutations in 80% of the patients. Pituitary adenomas in children are rare but should be considered when facing similar imaging features than in adults. Imaging characteristics may allow to suspect the hormonal secretion.

A ConvLSTM-based model for predicting thermal damage during laser interstitial thermal therapy.


Accurate prediction of thermal damage extent is essential for effective and precise thermal therapy, especially in brain laser interstitial thermal therapy (LITT). Immediate postoperative contrast-enhanced T1-weighted imaging (CE-T1WI) is the primary method for clinically assessing in vivo thermal damage after image-guided LITT. CE-T1WI reveals a hyperintense enhancing rim surrounding the target lesion, which serves as a key radiological marker for evaluating the thermal damage extent. Although widely used in clinical practice, traditional thermal damage models rely on empirical parameters from in vitro experiments, which can lead to inaccurate predictions of thermal damage in vivo. Additionally, these models predict only two tissue states (damaged or undamaged), failing to capture three tissue states observed on post-CE-T1WI images, highlighting the need for improved thermal damage prediction methods. This study proposes a novel Convolutional Long Short-Term Memory (ConvLSTM)-based model that utilizes intraoperative temperature distribution history data measured by magnetic resonance temperature imaging (MRTI) during LITT to predict the enhancing rim on post-CE-T1WI images. This method was implemented and evaluated on retrospective data from 56 patients underwent brain LITT.
Main results:
The proposed model effectively predicts the enhancing rim on postoperative images, achieving an average Dice Similarity Coefficient (DSC) of 0.82 (±0.063) on the test dataset. Furthermore, it generates real-time predicted thermal damage area variation trends that closely resemble those of the traditional thermal damage model, suggesting potential for real-time prediction of thermal damage extent. This method could provide a valuable tool for visualizing and assessing intraoperative thermal damage extent.&#xD.

Diagnosis of Sacroiliitis Through Semi-Supervised Segmentation and Radiomics Feature Analysis of MRI Images.

Sacroiliitis is a hallmark of ankylosing spondylitis (AS), and early detection plays an important role in managing the condition effectively. MRI is commonly used for diagnosing sacroiliitis, traditional methods often depend on subjective interpretation or limited automation which can introduce variability in diagnoses. The integration of semi-supervised segmentation and radiomics features may reduce reliance on expert interpretation and the need for large annotated datasets, potentially enhancing diagnostic workflows. To develop a diagnostic model for sacroiliitis and bone marrow edema (BME) using semi-supervised segmentation and radiomics analysis of MRI images. Retrospective cohort study. A total of 257 patients (161 males, 93 females; age 11-74 years), including 155 sacroiliitis and 175 BME patients. A total of 514 sacroiliac joint (SIJ) MRI images are analyzed, with 359 used for training and 155 for testing. 3.0 T, spin echo T1-weighted imaging (T1WI) and short-tau inversion recovery (STIR). SIJ segmentation is automated using the semi-supervised segmentation-based Unimatch framework. Manual delineation of SIJ regions of interest (ROIs) on T1WI images by an experienced radiologist (W.M., 10-year experience) served as the reference standard for segmentation performance evaluation. Radiomics features from T1WI and STIR are used to train machine learning models, including support vector machine (SVM), logistic regression (LR), and light gradient boosting machine (LightGBM), for sacroiliitis and BME detection. Performance is assessed using area under the curve (AUC), sensitivity, specificity, and accuracy. The Dice coefficient is used to assess the performance of the semi-supervised segmentation model on SIJ segmentation. Performance is evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). The Unimatch model achieves an average Dice coefficient of 0.859 for SIJ segmentation. AUCs for sacroiliitis detection are 0.84 (LR), 0.86 (SVM), and 0.78 (LightGBM), while for BME detection, AUCs are 0.73 (LR), 0.76 (SVM), and 0.70 (LightGBM). This study demonstrates that semi-supervised segmentation combined with radiomics features and machine learning models provides a promising approach for diagnosis of sacroiliitis and BME. This study aimed to improve the diagnosis of sacroiliitis and bone marrow edema in patients with ankylosing spondylitis. The researchers used a method that automatically segments MRI images and analyzes features from those images. By applying machine learning, they created models to help detect sacroiliitis and bone marrow edema more accurately. The results show that this approach can effectively assist in identifying these conditions, with the best accuracy for sacroiliitis and bone marrow edema reaching 81.2% and 74.2%, respectively. This method could help doctors make better decisions, offering a promising tool for improving diagnosis in clinical settings. 3 TECHNICAL EFFICACY: Stage 2.

WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis

This paper introduces the Welsh Advanced Neuroimaging Database (WAND), a multi-scale, multi-modal imaging dataset comprising in vivo brain data from 170 healthy volunteers (aged 18–63 years), including 3 Tesla (3 T) magnetic resonance imaging (MRI) with ultra-strong (300 mT/m) magnetic field gradients, structural and functional MRI and nuclear magnetic resonance spectroscopy at 3 T and 7 T, magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS), together with trait questionnaire and cognitive data. Data are organised using the Brain Imaging Data Structure (BIDS). In addition to raw data, we provide brain-extracted T1-weighted images, and quality reports for diffusion, T1- and T2-weighted structural data, and blood-oxygen level dependent functional tasks. Reasons for participant exclusion are also included. Data are available for download through our GIN repository, a data access management system designed to reduce storage requirements. Users can interact with and retrieve data as needed, without downloading the complete dataset. Given the depth of neuroimaging phenotyping, leveraging ultra-high-gradient, high-field MRI, MEG and TMS, this dataset will facilitate multi-scale and multi-modal investigations of the healthy human brain.

Imaging Findings of Human Papillomavirus-Positive and Human Papillomavirus-Negative Oropharyngeal Squamous Cell Carcinoma Associated with Recurrence

Objectives: This study aimed to compare the imaging findings associated with the recurrence of HPV-positive and HPV-negative oropharyngeal squamous cell carcinoma (OPSCC). Methods: In total, 68 patients (51 men; mean age, 64.4 years; age range, 41–86 years; 48 HPV-positive patients and 20 HPV-negative patients) with histopathologically proven OPSCC who underwent CT, MRI, and 18F-FDG-PET/CT before treatment between October 2014 and July 2022 were enrolled in this study. The imaging findings were retrospectively evaluated and statistically compared. Results: HPV-positive OPSCC had a significantly lower recurrence rate compared with that of HPV-negative OPSCC (p < 0.01). Among HPV-positive OPSCCs, patients with recurrence were considerably older than those without recurrence (p < 0.05); however, the T and N categories did not differ between the two groups. Meanwhile, among HPV-negative OPSCCs, the T and N categories were associated with recurrence (p < 0.05). Furthermore, the attenuation on contrast-enhanced CT (p < 0.05) and signal intensity on contrast-enhanced T1-weighted images (p < 0.05) of nodal metastases were significantly lower in recurrence patients compared to those in nonrecurrence patients. Cystic change in nodal metastases in HPV-positive and HPV-negative OPSCCs were similar in patients with and without recurrence. Conclusions: The T and N categories were associated with recurrence in HPV-negative OPSCC but not in HPV-positive OPSCC. Prognostic factors differed significantly between HPV-positive and HPV-negative OPSCC.

MRI features of Stewart-Treves syndrome: a case series and systematic review.

To comprehensively summarize the magnetic resonance imaging (MRI) features of angiosarcomas presenting as Stewart-Treves syndrome (STS) through a retrospective case series and systematic review of previous publications. We identified five patients with STS from our institutional database and 25 patients with STS from 15 publications through a systemic review. We reviewed the MR features of 30 patients with STS, including five males and 25 females with a mean age of 59.5years. The tumors most commonly involved both the cutis and subcutis (17/25, 68.0%); the remaining tumors were limited to the cutis or subcutis. Multiple tumors were observed in more than half of the cases (16/27, 59.3%), and most of the tumors had poorly defined margins (26/28, 92.9%). The most common signal intensities of the tumors on T1-weighted images were intermediate (16/19, 84.2%), and the remainder were a mixture of intermediate and high, with a predominance of intermediate signal intensity. The signal intensities of the tumors on T2-weighted images were intermediate in seven cases (7/13, 53.8%), a mixture of intermediate and high in five cases (5/13, 38.5%), and a mixture of intermediate and low in one case (1/13, 7.7%). Available diffusion-weighted images from four institutional cases showed restricted diffusion of the tumors with mean apparent diffusion coefficient (ADC) values ranging from 0.77 × 10-3 mm2/s to 0.96 × 10-3 mm2/s. Typical MRI features of angiosarcomas in STS were superficially located in multiple masses with ill-defined margins. Internal signal intensity was intermediate on T1-weighted images, and intermediate or a mixture of intermediate and high on T2-weighted images. ADC values obtained from the limited number of cases were low.

Layering hyperintensity in T1-weighted magnetic resonance imaging predicts gallbladder sludge: a retrospective cohort and diagnostic accuracy study in patients with significant liver disease.

Layering hyperintensity in the gallbladder is frequently observed on T1-weighted (T1w) magnetic resonance imaging (MRI), but its association with hepatobiliary disorders is not well understood. This study aimed to evaluate the prevalence of T1w layering in the gallbladder and its correlation with ultrasound (US) findings and patient characteristics in a cohort with significant liver disease. A single-center study from 2015 to 2022 included patients who underwent MRI and abdominal US within one week. Exclusion criteria were poor imaging quality and prior cholecystectomy. MRI findings were correlated with US and analyzed against patient characteristics. Among 415 patients (mean age 58.3 ± 14.8 years; mean BMI 28.0 ± 4.5kg/m²), 67% had abnormal liver function tests, with high prevalences of cirrhosis (n = 260), transjugular intrahepatic portosystemic shunt (TIPS) (n = 233), and choledocholithiasis (n = 106). T1w layering was observed in 56% (n = 232) and associated with higher BMI (p = 0.001) and with cholecystolithiasis (p < 0.001), but not with age, sex, or liver disease indicators. T1w layering was predictive of gallbladder sludge on US (odds ratio 17.2, 95% confidence interval 9.87-31.44, p < 0.001), with a sensitivity of 92.7% but moderate specificity (57.9%). T1w layering on MRI strongly predicts gallbladder sludge detected on US and is associated with increased BMI in this cohort of patients with liver disease. However, the moderate specificity requires cautious interpretation, and our findings suggest that T1w layering may serve as a complementary diagnostic tool.

Inclusion of tumor periphery in radiomics analysis of magnetic resonance images does not improve predictionsof preoperative therapy response in patients with rectal cancer.

To evaluate the advantages of including versus excluding the tumor periphery and combining diffusion-weighted imaging (DWI) with T2-weighted imaging (T2w) for outcome predictions of preoperative radio(chemo)therapy in rectal cancer. Four analysis strategies, based on two segmentation methods and two magnetic resonance imaging (MRI) sequences, were evaluated in 106 patients examined with pretreatment MRI. One segmentation method included the tumor periphery in the region of interest (ROI) encompassing the whole tumor (wROI), considered as the reference segmentation approach, and one included only the central part (cROI). Relevant radiomics imaging features were extracted from either T2w alone or from both T2w and DWI and used by a machine learning algorithm for the prediction of pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and disease recurrence. The area under the curve (AUC) was the performance measure. AUCs were compared with a bootstrapping method based on 104 bootstraps. cROI applied to both T2w and DWI provided the highest numerical prediction of pCR (AUC 0.76), however, not significantly superior to the other strategies (p ≥ 0.138). cROI applied to both T2w and DWI also yielded the highest numerical prediction of NAR score (AUC 0.84), showing advantages over wROI-based analysis strategies (AUC 0.66 and 0.69; p ≤ 0.008). When compared to cROI applied to T2w alone (AUC 0.73), the benefit was borderline statistically significant (p = 0.053). For prediction of disease recurrence, no differences were found between the analysis strategies. Inclusion of the tumor periphery in radiomic analysis of magnetic resonance images does not improve predictions of the preoperative therapy response in patients with rectal cancer. Excluding tumor periphery while adding DWI to T2w improves prediction of the NAR score, although it does not affect pCR or recurrence prediction.

Relationship between medial tibial stress syndrome and the adipose tissue along the posteromedial tibial border and the crural chiasma

ABSTRACT Objectives Medial tibial stress syndrome (MTSS) is an overuse injury characterized by pain along the posteromedial tibial border. This region contains several soft tissues, including adipose tissue and the tibialis posterior and flexor digitorum longus tendons. However, few studies have investigated whether these tissues exhibit abnormalities in MTSS. This study aimed to use MRI to evaluate abnormalities in the bone marrow or soft tissues of patients with MTSS and those with a history of medial tibial pain. Methods Eighteen patients with MTSS, 20 uninjured athletes, and 17 individuals with a history of medial tibial pain underwent T1-weighted, T2-weighted, and short T1 inversion recovery imaging. The presence of bone marrow, periosteal, and peritendinous edema and abnormalities in the adipose tissue along the posteromedial tibial border were evaluated. Fisher’s exact test was applied to determine the relationship between abnormalities and clinical status. Results Patients with MTSS exhibited abnormalities in the adipose tissue (27.3%), peritendinous edema (22.7%), bone marrow edema (22.7%), and periosteal edema (59.1%). However, the incidence of these abnormalities showed no statistically significant differences between the three groups. Athletes showed abnormalities in the bone marrow, periosteum or soft tissues, regardless of their clinical status. Conclusion Various tissues, including the adipose tissue and peritendon along the posteromedial tibial border, may contribute to MTSS, highlighting its complex pathology. Imaging assessments are important for diagnosing MTSS and should complement physical examination and evaluation of the patient’s history.

Gender differences of the brain structures in young high-altitude Tibetans.

Gender differences in adaptation to high-altitude environments are evident, but the specific patterns in Tibetan brains remain unclear. We analyzed T1-weighted magnetic resonance imaging scans and neuropsychological test results from 61 male and 68 female Tibetans, with lowland Han participants as controls. Tibetan females had poorer performance than males in digit serial accumulation and forward digit span. Both Tibetan and Han males had significantly larger global gray matter volume and white matter volume than females, only Tibetan female brains contained a larger proportion of gray matter than male brains. Tibetan females (vs. males) had smaller regional gray matter volume in the left rostral middle frontal gyrus and pars opercularis and right caudal middle frontal gyrus, and gray matter volume in the left pars opercularis in all Tibetans had a significant positive correlation with forward digit span. Conversely, Tibetan females had greater cortical thickness in these regions, which negatively correlated with altitude. Han populations exhibited different gender-based patterns in gray matter volume and cortical thickness compared to Tibetans. These findings suggest that Tibetan female brains are more susceptible to high-altitude, and the observed gender differences in brain volume may relate to distinct neuropsychological performances.

Iron-Doxorubicin Hyaluronan Nanogel as an Effective Targeted Chemotherapy with Modulated Cardiotoxicity.

The complex of doxorubicin (Dox) and intracellular iron in cardiomyocytes generates reactive oxygen species (ROS), contributing to commonly observed cardiotoxicity. To enhance the anticancer potency and minimize the cardiotoxicity, here Dox was formulated into a hyaluronan (HA) nanogel using ferric ion (Fe3+) coordination to control the intracellular distribution and release of Dox. Taking advantage of the paramagnetic properties of iron and the fluorescence of Dox, we conveniently monitored the targeted delivery of the HA@Dox nanogel in murine breast tumors through both T1-weighted magnetic resonance imaging and fluorescence imaging. Compared with free Dox, HA@Dox nanogel affords a CD44-targeted delivery, lysosomal distribution, pH-responsive release, and significant tumor inhibition. Mechanistically, the lysosome-enriched HA@Dox produces ROS, causing lysosomal membrane permeabilization, which further promotes the intracellular Dox distribution. This HA@Dox nanogel not only provides a facile cancer-targeted delivery but also successfully relieves Dox toxicity, representing a potent delivery system for Dox.

Leiomyosarcomas of the inferior vena cava: diagnostic features on contrast-enhanced CT, ultrasonography and MRI

PurposeTo evaluate clinical presentation and imaging characteristics of leiomyosarcomas of the inferior vena cava (IVC LMS) using contrast-enhanced CT (CECT), ultrasonography (US), magnetic resonance imaging (MRI), and to identify features that facilitate early and accurate pre-operative diagnosis.Materials and methodsOur study enrolled 21 patients with pathologically confirmed IVC LMS from October 2015 to June 2022. All participants underwent CECT, and additionally, 12 participants had US examinations and 3 had MRI. Images were independently reviewed by two experienced radiologists. The clinical presentations and diagnostic characteristics were recorded.ResultsThe study involved 16 female and 5 male patients, with an average age of 55 ± 11 years (ranging from 34 to 80 years). Common clinical symptoms included abdominal pain, back pain, leg discomfort, abdominal distension, jaundice, and the presence of an abdominal mass. On CT scans, a large, lobulated, heterogeneous mass with progressive enhancement was typically seen in 13 of the 21 patients (61.9%). Ultrasonography revealed that IVC LMS typically presented as a lobulated, heterogeneous, hypoechoic mass. Color Doppler imaging evaluated lumen obstruction in 8 of the 12 patients (66.7%), and high velocity flow signals were detected by Pulsed wave Doppler in 4 of the 12 patients (33.3%). On MRI, IVC LMS presented as a heterogeneous mass that exhibited intermediate intensity on T1-weighted images, slightly high intensity on T2-weighted images and high intensity on diffusion-weighted images.ConclusionSeveral diagnostic characteristics on CECT, US and MRI could aid in the diagnosis of IVC LMS. The detection of a heterogeneous mass with progressive enhancement along the inferior vena cava on CECT was strongly indicative of IVC LMS. Both CT and US are effective in accurately indicating the location of the tumor within the IVC.

MPRAGElike: A novel approach to generate T1w images from multi-contrast gradient echo images for brain segmentation.

Brain segmentation and multi-parameter mapping (MPM) are important steps in neurodegenerative disease characterization. However, acquiring both a high-resolution T1w sequence like MPRAGE (standard input to brain segmentation) and an MPM in the same neuroimaging protocol increases scan time and patient discomfort, making it difficult to combine both in clinical examinations. A novel approach to synthesize T1w images from MPM images, named MPRAGElike, is proposed and compared to the standard technique used to produce synthetic MPRAGE images (synMPRAGE). Twenty-three healthy subjects were scanned with the same imaging protocol at three different 7T sites using universal parallel transmit RF pulses. SNR, CNR, and automatic brain segmentation results from both MPRAGElike and synMPRAGE were compared against an acquired MPRAGE. The proposed MPRAGElike technique produced higher SNR values than synMPRAGE for all regions evaluated while also having higher CNR values for subcortical structures. MPRAGE was still the image with the highest SNR values overall. For automatic brain segmentation, MPRAGElike outperformed synMPRAGE when compared to MPRAGE (median Dice Similarity Coefficient of 0.90 versus 0.29 and Average Asymmetric Surface Distance of 0.33 versus 2.93mm, respectively), in addition to being simple, flexible, and considerably more robust to low image quality than synMPRAGE. The MPRAGElike technique can provide a better and more reliable alternative to synMPRAGE as a substitute for MPRAGE, especially when automatic brain segmentation is of interest and scan time is limited.

Inter-reader agreement for diagnosing thymic cysts on chest MRI in two tertiary referral centers.

Despite the substantial role of chest MRI for the diagnosis and follow-up of thymic cysts, information about inter-reader agreement and optimal MR sequences is still limited. We aimed to investigate the inter-reader agreement for diagnosing thymic cysts using various combinations of MR sequences and to assess the effect of the addition of CT on inter-reader agreement. A total of 76 anterior mediastinal lesions (≤ 30mm) from two tertiary referral hospitals (55 from Institution A and 21 from Institution B) who underwent chest CT and contrast-enhanced chest MR were included. Internal and external reading sets consisted of different combinations of MR sequences (pre- and post-contrast T1-weighted, T2-weighted, subtracted images, and diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC] map) and CT. Four and three radiologists independently reviewed internal and external reading sets. The overall inter-reader agreement was moderate (κ = 0.50-0.57) for diagnosing cysts without significant differences between MR sequence combinations (all p-values > 0.05). The mean pairwise inter-reader agreement was the highest (κ = 0.65) when both the subtracted image and DWI/ADC map were provided. The addition of CT had no positive effect on the inter-reader agreement in the internal reading set (κ, from 0.57 to 0.50) but increased inter-reader agreement in the external reading set from moderate (κ = 0.48) to substantial (κ = 0.74). In conclusion, the overall inter-reader agreement for diagnosing thymic cysts on MRI was moderate. MR sequences including both the subtracted image and DWI/ADC map may be optimal in terms of inter-reader agreement.

Gray and white matter differences in the medial temporal lobe in late-life depression: a multimodal PET-MRI investigation.

Late-life depression (LLD) is characterized by medial temporal lobe (MTL) abnormalities. Although gray matter (GM) and white matter (WM) differences in LLD have been reported, few studies have investigated them concurrently. Moreover, the impact of aetiological factors, such as neurodegenerative and cerebrovascular burden, on tissue differences remains elusive. This prospective cross-sectional study involved 72 participants, including 33 patients with LLD (mean age 72.2 years, 23 female) and 39 healthy controls (HCs) (mean age 70.6 years, 24 female), who underwent clinical and positron emission tomography (PET)-magnetic resonance imaging (MRI) assessments. High-resolution 3D T1-weighted and T2-weighted FLAIR images were used to assess MTL GM volumes and white matter hyperintensities (WMHs), a proxy for cerebrovascular burden. Diffusion kurtosis imaging metrics derived from multishell diffusion MRI data were analyzed to assess WM microstructure in the following MTL bundles reconstructed using constrained spherical deconvolution tractography: uncinate fasciculus, fornix, and cingulum. Standardized uptake value ratio of 18F-MK-6240 in the MTL was used to assess Alzheimer's disease (AD) type tau accumulation as a proxy for neurodegenerative burden. Compared to HCs, patients with LLD showed significantly lower bilateral MTL volumes and WM microstructural differences primarily in the uncinate fasciculi bilaterally and right fornix. In patients with LLD, higher vascular burden, but not tau, was associated with lower MTL volume and more pronounced WM differences. LLD was associated with both GM and WM differences in the MTL. Cerebrovascular disease, rather than AD type tau-mediated neurodegenerative processes, may contribute to brain tissue differences in LLD.

Morphometric Analysis of Neocortical and Infratentorial Structures: Genetic and Environmental Insights from a Twin Neuroanatomical Study

Background and Objective: Brain morphometry is shaped by a complex interplay of genetic and environmental factors, including physiological and neuropsychiatric conditions. These influences can vary across distinct brain regions, yet the precise contributions of genetics and environment to regional variation in healthy brains remain poorly understood. This study examines the heritability of specific brain structures to provide deeper insights into their development. Materials and Methods: We studied 118 healthy adult twins from the Hungarian Twin Registry using T1-weighted magnetic resonance imaging (T1W MRI) and the volBrain pipeline for structural measurements. Results: In all regions, monozygotic (MZ) twins showed a higher resemblance than dizygotic (DZ) twins in total brainstem and cerebellar volumes, with significant heritability (A: 90.5–92.6%) and minimal unique environmental effects (E: &lt;1%). For supratentorial regions, regarding the total gray matter volume, all regions exhibited high heritability (A: 74.5–92.4%) and minimal environmental influence (E: &lt;1.5%). In average cortical thickness analysis, the frontal lobe, temporal lobe, and pre-central gyrus were influenced by shared and unique environmental factors (C: 63–66.5%; E: 33.4–37%), whereas genetics were more prominent in the parietal lobe, occipital lobe, and post-central gyrus (A: 67.7–85%; E: 15–32.3%). Conclusions: Genetics strongly influence cortical gray matter volume in supratentorial regions (both total and regional), as well as the total brainstem volume and the total and cortical gray matter volumes of the cerebellum in infratentorial regions. This genetic influence extends to the average cortical thickness of the parietal lobe, post-central gyrus, and occipital lobe, while the frontal lobe, temporal lobe, and pre-central gyrus are more affected by environmental factors. These findings emphasize the importance of understanding region-specific genetic and environmental contributions to brain structure, which could guide personalized therapeutic and preventive strategies for neurological conditions.

Associations of Postencephalitic Epilepsy Using Multi-Contrast Whole Brain MRI: A Large Self-Supervised Vision Foundation Model Strategy.

Postencephalitic epilepsy (PEE) is a severe neurological complication following encephalitis. Early identification of individuals at high risk for PEE is important for timely intervention. To develop a large self-supervised vision foundation model using a big dataset of multi-contrast head MRI scans, followed by fine-tuning with MRI data and follow-up outcomes from patients with PEE to develop a PEE association model. Retrospective. Fifty-seven thousand six hundred twenty-one contrast-enhanced head MRI scans from 34,871 patients for foundation model construction, and head MRI scans from 144 patients with encephalitis (64 PEE, 80 N-PEE) for the PEE association model. 1.5-T, 3-T, T1-weighted imaging, T2-weighted imaging, fluid attenuated inversion recovery, T1-weighted contrast-enhanced imaging. The foundation model was developed using self-supervised learning and cross-contrast context recovery. Patients with encephalitis were monitored for a median of 3.7 years (range 0.7-7.5 years), with epilepsy diagnosed according to International League Against Epilepsy. Occlusion sensitivity mapping highlighted brain regions involved in PEE classifications. Model performance was compared with DenseNet without pre-trained weights. Performance was assessed via confusion matrices, accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve (AUC). The DeLong test evaluated AUC between the two models (P < 0.05 for statistical significance). The PEE association model achieved accuracy, sensitivity, specificity, precision, F1 score, and AUC of 79.3% (95% CI: 0.71-0.92), 92.3% (95% CI: 0.80-1.00), 68.8% (95% CI: 0.55-0.87), 70.6% (95% CI: 0.61-0.90), 80.0% (95% CI: 0.71-0.93), and 81.0% (95% CI: 0.68-0.92), respectively. A significant AUC improvement was found compared to DenseNet (Delong test, P = 0.03). The association model focused on brain regions affected by encephalitis. Using extensive unlabeled data via self-supervised learning addressed the limitations of supervised tasks with limited data. The fine-tuned foundation model outperformed DenseNet, which was trained exclusively on task data. This research develops a model to assess the occurrence epilepsy after encephalitis, a severe brain inflammation condition. By using over 57,000 brain scans, the study trains a computer program to recognize patterns in brain images. The model analyzes whole-brain scans to identify areas commonly affected by the disease, such as the temporal and frontal lobes. It was tested on data from patients with encephalitis and showed better performance than older methods. The model can assess the risk of secondary epilepsy in patients with encephalitis, allowing doctors to intervene early and improve treatment outcomes for those affected by this condition. 4 TECHNICAL EFFICACY: Stage 1.

Computer-aided diagnosis based on 3D deep convolutional neural network system using novel 3D magnetic resonance imaging sequences for high-grade prostate cancer

Abstract Background With the rising incidence of prostate cancer (PCa), there is a global demand for assistive tools that aid in the diagnosis of high-grade PCa. This study aimed to develop a diagnostic support system for high-grade PCa using innovative magnetic resonance imaging (MRI) sequences in conjunction with artificial intelligence (AI). Materials and methods We examined image sequences of 254 patients with PCa obtained from diffusion-weighted and T2-weighted imaging, using novel MRI sequences before prostatectomy, to elucidate the characteristics of the 3-dimensional (3D) image sequences. The presence of PCa was determined based on the final diagnosis derived from pathological results after prostatectomy. A 3D deep convolutional neural network (3DCNN) was used as the AI for image recognition. Data augmentation was conducted to enhance the image dataset. High-grade PCa was defined as Gleason grade group 4 or higher. Results We developed a learning system using a 3DCNN as a diagnostic support system for high-grade PCa. The sensitivity and area under the curve values were 85% and 0.82, respectively. Conclusions The 3DCNN-based AI diagnostic support system, developed in this study using innovative 3D multiparametric MRI sequences, has the potential to assist in identifying patients at a higher risk of pretreatment of high-grade PCa.

Cortical structural degeneration and functional network connectivity changes in patients with subcortical vascular cognitive impairment.

To explore the structural basis of functional network connectivity (FNC) changes and early cortical degenerative patterns in subcortical vascular cognitive impairment (SVCI). We prospectively included SVCI cases and healthy controls (HCs). FNC alterations were evaluated using group-independent component analysis of resting-state functional MRI data. Cortical microstructural and macrostructural alterations were assessed using gray matter-based spatial statistics analysis with neurite orientation dispersion and density imaging and cortical thickness analysis with FreeSurfer software on T1-weighted images, respectively. Spearman correlation analyses were performed to assess relationships between FNC alterations and cortical microstructural/macrostructural alterations and between FNC, cortical thickness, or neurite density index (NDI)/orientation dispersion index (ODI) alterations and cognitive performance. Forty-six SVCI patients and 73 HCs were recruited. FNC analysis showed lower network connectivity between the visual network (VN) and sensorimotor network (SMN) in SVCI, positively correlated with information processing speed (p=0.008) and negatively with summary SVD score (p = 0.037). Cortical microstructural analyses exhibited a lower NDI, mainly in the VN and default mode network (DMN) areas (PFWE < 0.05, cluster > 100 voxels), and lower ODI, mainly in the SMN and DMN areas (PFWE < 0.05, cluster > 100 voxels) in SVCI, both of which were related to cognitive function (p < 0.05). However, cortical thickness did not differ between groups. Lower NDI in the lateral occipital cortex was linked to lower VN-SMN connectivity in SVCI (p = 0.002). Cortical microstructural alterations may serve as the basis for FNC changes in SVCI.