Three-dimensional T1-weighted Magnetic Resonance Imaging Research Articles

An improved 3D-UNet-based brain hippocampus segmentation model based on MR images

ObjectiveAccurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results. Methods: Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network. Results: The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively. Conclusion: The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.

Lower synaptic density and its association with cognitive dysfunction in patients with obsessive-compulsive disorder

BackgroundUnderstanding synaptic alteration in obsessive-compulsive disorder (OCD) is crucial for elucidating its pathological mechanisms, but in vivo research on this topic remains limited.AimsThis study aimed to identify the synaptic density...

Altered cortical thickness and structural covariance networks in chronic low back pain

BackgroundDespite regional brain structural changes having been reported in patients with chronic low back pain (CLBP), the topological properties of structural covariance networks (SCNs), which refer to the organization of the SCNs, remain unclear. This study applied graph theoretical analysis to explore the alterations of the topological properties of SCNs, aiming to comprehend the integration and separation of SCNs in patients with CLBP. MethodsA total of 38 patients with CLBP and 38 healthy controls (HCs), balanced for age and sex, were scanned using three-dimensional T1-weighted magnetic resonance imaging. The cortical thickness was extracted from 68 brain regions, according to the Desikan–Killiany atlas, and used to reconstruct the SCNs. Subsequently, graph theoretical analysis was employed to evaluate the alterations of the topological properties in the SCNs of patients with CLBP. ResultsIn comparison to HCs, patients with CLBP had less cortical thickness in the left superior frontal cortex. Additionally, the cortical thickness of the left superior frontal cortex was negatively correlated with the Visual Analogue Scale scores of patients with CLBP. Furthermore, patients with CLBP, relative to HCs, exhibited lower global efficiency and small-worldness, as well as a longer characteristic path length. This indicates a decline in the brain's capacity to transmit and process information, potentially impacting the processing of pain signals in patients with CLBP and contributing to the development of CLBP. In contrast, there were no significant differences in the clustering coefficient, local efficiency, nodal efficiency, nodal betweenness centrality, or nodal degree between the two groups. ConclusionsFrom the regional cortical thickness to the complex brain network level, our study demonstrated changes in the cortical thickness and topological properties of the SCNs in patients with CLBP, thus aiding in a better understanding of the pathophysiological mechanisms of CLBP.

Altered volume of the amygdala subregions in patients with chronic low back pain.

Neuroimaging studies have suggested a pivotal role for the amygdala involvement in chronic low back pain (CLBP). However, the relationship between the amygdala subregions and CLBP has not yet been delineated. This study aimed to analyze whether the amygdala subregions were linked to the development of CLBP. A total of 45 patients with CLBP and 45 healthy controls (HCs) were included in this study. All subjects were asked to complete a three-dimensional T1-weighted magnetic resonance imaging (3D-T1 MRI) scan. FreeSurfer 7.3.2 was applied to preprocess the structural MRI images and segment the amygdala into nine subregions. Afterwards, comparisons were made between the two groups in terms of the volumes of the amygdala subregions. Correlation analysis is utilized to examine the relationship between the amygdala subregion and the scale scores, as well as the pain duration in patients with CLBP. Additionally, logistic regression was used to explore the risk of the amygdala and its subregions for CLBP. In comparison to HCs, patients with CLBP exhibited a significant enlargement of the left central nucleus (Ce) and left cortical nucleus (Co). Furthermore, the increased volume of the left Ce was associated with a higher risk of CLBP. Our study suggests that the left Ce and left Co may be involved in the pathophysiological processes of CLBP. Moreover, the volume of the left Ce may be a biomarker for detecting the risk of CLBP.

Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network.

Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson's correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.

Higher cerebral blood flow on four-dimensional flow magnetic resonance imaging in young women.

We investigated the reduction in regional brain volume and cerebral blood flow (CBF) with aging and explored potential sex differences in healthy brains. Three-dimensional (3D) T1-weighted magnetic resonance imaging (MRI), time-of-flight magnetic resonance angiography, and four-dimensional (4D) flow MRI were performed on 129 healthy volunteers aged 22-92 years. The brains of healthy volunteers were segmented into 21 subregions using 3D T1-weighted MRI and CBFs in 16 major intracranial arteries were measured using 4D flow MRI. The cortical gray matter volume decreased linearly with aging, whereas the cerebral white matter volume increased until the 40s and then decreased, and the subcortical gray matter volume changed little with aging. The cortical gray matter volume was significantly associated with the total CBF of the major intracranial arteries distal to the circle of Willis; however, the cerebral white matter and subcortical gray matter volumes were not. Generally, women have higher total CBF than men, particularly in their 40s and younger, despite the smaller intracranial volume and smaller diameters of intracranial arteries than men. This may contribute to the higher incidence of subarachnoid hemorrhage due to cerebral aneurysms and migraine in women.

Prediction of anxious depression using multimodal neuroimaging and machine learning

Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.

Simulation of the occipital transtentorial approach incorporating visualization of the cerebellar tentorium using three-dimensional computed tomography angiography and gadolinium-enhanced T1-weighted magnetic resonance imaging: technical note

The occipital transtentorial approach (OTA) is one of the useful approaches to the lesions of the pineal region, dorsal brainstem, and supracerebellar region. However, a wide operative field is sometimes difficult to obtain due to the tentorial sinus and bridging veins. This study evaluated the usefulness of preoperative simulation of OTA, specifically including the cerebellar tentorium in 9 patients. All patients underwent computed tomography angiography and venography and gadolinium-enhanced three-dimensional T1-weighted magnetic resonance images (Gd-3D-T1WI). The images were fused, and the cerebellar tentorium, vessels, and tumor were manually extracted from Gd-3D-T1WI to obtain the simulation images. Visualization of the cerebellar tentorium could discriminate between bridging veins from the occipital lobe and cerebellum, and recognize the site of bridging to the tentorial sinus and variants which may interfere with the tentorial incision. Simulation of the tentorial incision was also possible based on the relationships between the tumor, tentorial sinus, bridging vein, and cerebellar tentorium. The simulation suggested that safe tentorial incision was difficult in two sides because of the crossed tentorial sinus draining the left basal vein and draining veins from the glioblastoma. The OTA was performed in eight cases, and no difficulty was experienced in the tentorial incision in all cases. The simulation findings of the bridging vein and tentorial sinus were consistent with the intraoperative findings. Preoperative simulation including the cerebellar tentorium is useful for determining the optimum and safe side and required extent of the tentorial incision necessary for tumor resection with the OTA.

Brain structural changes in alternating hemiplegia of childhood using single-case voxel-based morphometry analysis.

Alternating hemiplegia of childhood (AHC) is a rare neurodevelopmental disease caused by ATP1A3 mutations. Using voxel-based morphometry (VBM) analysis, we compared an AHC patient cohort with controls. Additionally, with single-case VBM analysis, we assessed the associations between clinical severity and brain volume in patients with AHC. To investigate structural brain changes in gray matter (GM) and white matter (WM) volumes between 9 patients with AHC and 20 age-matched controls, VBM analysis was performed using three-dimensional T1-weighted magnetic resonance imaging. Single-case VBM analysis was also performed on nine patients with AHC to investigate the associations between the respective volumes of GM/WM differences and the motor level, cognitive level, and status epilepticus severity in patients with AHC. Compared with controls, patients with AHC showed significant GM volume reductions in both hippocampi and diffuse cerebellum, and there were WM reductions in both cerebral hemispheres. In patients with AHC, cases with more motor dysfunction, the less GM/WM volume of cerebellum was shown. Three of the six cases with cognitive dysfunction showed a clear GM volume reduction in the insulae. Five of the six cases with status epilepticus showed the GM volume reduction in hippocampi. One case had severe status epilepticus without motor dysfunction and showed no cerebellar atrophy. With single-case VBM analysis, we could show the association between region-specific changes in brain volume and the severity of various clinical symptoms even in a small sample of subjects.

Brain networks and epilepsy development in patients with Alzheimer disease.

This study aimed to investigate the association between brain networks and epilepsy development in patients with Alzheimer disease (AD). We enrolled patients newly diagnosed with AD at our hospital who underwent three-dimensional T1-weighted magnetic resonance imaging at the time of AD diagnosis and included healthy controls. We obtained the cortical, subcortical, and thalamic nuclei structural volumes using FreeSurfer and applied graph theory to obtain the global brain network and intrinsic thalamic network based on the structural volumes using BRAPH. We enrolled 25 and 56 patients with AD with and without epilepsy development, respectively. We also included 45 healthy controls. The global brain network differed between the patients with AD and healthy controls. The local efficiency (2.026 vs. 3.185, p=.048) and mean clustering coefficient (0.449 vs. 1.321, p=.024) were lower, whereas the characteristic path length (0.449 vs. 1.321, p=.048) was higher in patients with AD than in healthy controls. Both global and intrinsic thalamic networks were significantly different between AD patients with and without epilepsy development. In the global brain network, local efficiency (1.340 vs. 2.401, p=.045), mean clustering coefficient (0.314 vs. 0.491, p=.045), average degree (27.442 vs. 41.173, p=.045), and assortative coefficient (-0.041 vs. -0.011, p=.045) were lower, whereas the characteristic path length (2.930 vs. 2.118, p=.045) was higher in patients with AD with epilepsy development than in those without. In the intrinsic thalamic network, the mean clustering coefficient (0.646 vs. 0.460, p=.048) was higher, whereas the characteristic path length (1.645 vs. 2.232, p=.048) was lower in patients with AD with epilepsy development than in those without. We found that the global brain network differs between patients with AD and healthy controls. In addition, we demonstrated significant associations between brain networks (both global brain and intrinsic thalamic networks) and epilepsy development in patients with AD.

Alterations in Gray Matter Morphology and Functional Connectivity in Adult Patients with Newly Diagnosed Untreated Hypothyroidism.

Background: Untreated adult hypothyroidism may be associated with cognitive and emotional impairment, but the precise underlying neuropathological mechanism is unknown. We investigated the brain morphological and functional abnormalities associated with cognition and emotion in hypothyroidism. Methods: This is a cross-sectional observational study. Forty-four newly diagnosed adult hypothyroid patients and 54 well-matched healthy controls (HCs) were enrolled. All participants underwent three-dimensional T1-weighted imaging and resting-state functional magnetic resonance imaging (MRI). Morphological and seed-based functional connectivity (FC) analyses were performed to compare the intergroup differences. Neuropsychological tests, including the Montreal Cognitive Assessment (MoCA) Scale, 24-item Hamilton Depression Rating Scale (HAMD-24), and Hamilton Anxiety Rating Scale (HAMA) were administered. Thyroid function test and blood lipid levels were measured. Correlations were computed between neuropsychological and biochemical measures with neuroimaging indices. Sensitive morphological or functional neuroimaging indicators were identified using receiver operating characteristic (ROC) analysis. Results: Compared with HCs, hypothyroid patients demonstrated lower total and subdomain scores on the MoCA and higher HAMD-24 and HAMA scores. Morphological analysis revealed the hypothyroid patients had significantly reduced gray matter (GM) volumes in the right superior frontal gyrus, superior temporal gyrus, left dorsolateral superior frontal gyrus, middle frontal gyrus, and supplementary motor area as well as significantly increased GM volumes in the bilateral cerebellar Crus I and left precentral gyrus. Furthermore, seed-based FC analysis of hypothyroid patients showed increased FC between the right cerebellar Crus I and left precentral gyrus, triangular part of the inferior frontal gyrus, and angular gyrus of the inferior parietal lobe. The language scores of the MoCA were positively correlated with Jacobian values of the left supplementary motor area (r = 0.391, p = 0.046) and precentral gyrus (r = 0.401, p = 0.039). ROC analysis revealed FC value between cerebellar Crus I and angular gyrus could differentiate groups with relatively high accuracy (sensitivity: 75%, specificity: 77.8%, area under the curve: 0.794 [CI 0.701-0.888], p < 0.001). Conclusions: Untreated adult-onset hypothyroidism may be associated with impaired cognition and anxiety or depression. GM morphological alterations and FC of the cerebellum with subregions of the frontal and parietal lobes may represent key neuropathological mechanisms underlying the cognitive deterioration and mood dysregulation observed in hypothyroid adults. Clinical Trial Registration Number: chiCTR2000028966.

Aberrant structural and functional alterations in postpartum depression: a combined voxel-based morphometry and resting-state functional connectivity study.

Postpartum depression (PPD) is a severe postpartum psychiatric disorder with unclear pathogenesis. Previous neuroimaging studies have reported structural or functional alterations in areas associated with emotion regulation, cognitive disorder, and parenting behaviors of PPD. The primary goal of this investigation was to explore the presence of brain structural alterations and relevant functional changes in PPD patients. A total of 28 patients and 30 matched healthy postnatal women (HPW) underwent both three-dimensional T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Structural analysis was performed by voxel-based morphometry (VBM), followed by resting-state functional analysis using a seed-based whole-brain functional connectivity (FC) approach with abnormal gray matter volume (GMV) regions as seed. Compared with HPW, the PPD patients showed increased GMV in the left dorsolateral prefrontal cortex (DLPFC.L), the right precentral gyrus (PrCG.R), and the orbitofrontal cortex (OFC). In the PPD group, the DLPFC.L showed increased FC with the right anterior cingulate and paracingulate gyri (ACG.R) and the right middle frontal gyrus (MFG.R); the FC between the PrCG.R and the right median cingulate and paracingulate gyri (DCG.R) exhibited enhanced; the OFC showed increased FC with MFG.R and the left inferior occipital gyrus (IOG.L). In PPD, GMV of DLPFC.L was positively correlated with EDPS scores (r = 0.409 p = 0.031), and FC of PrCG.R-DCG.R was positively correlated with EDPS scores (r = 0.483 p = 0.020). Structural and functional damage of the DLPFC.L and OFC is associated with cognitive disorders and parenting behaviors in PPD, while structural abnormalities of the DLPFC.L and PrCG.R are involved in impaired executive function. The increased GMV of DLPFC.L may be a unique structural pathological mechanism of PPD related to the inability of PPD patients to withstand long-term parenting stress. These findings have important implications for understanding neural mechanisms in PPD.

Association between Cognitive Impairment and Hippocampal Subfield Volumes in Multiple System Atrophy.

This study aimed to explore morphological changes of hippocampal subfields in patients with multiple system atrophy (MSA) with and without cognitive impairment using FreeSurfer-automated segmentation of hippocampal subfield techniques and their relationship with cognitive function. We enrolled 75 patients with MSA classified as cognitively impaired MSA (MSA-CI, n = 40) and cognitively preserved MSA (MSA-CP, n = 35), as well as 68 healthy controls. All participants underwent three-dimensional volume T1-weighted magnetic resonance imaging. The hippocampal subfield volume was measured using FreeSurfer version 7.2 and compared among groups. Regression analyses were performed between the hippocampal subfield volumes and cognitive variables. Compared with healthy controls, the volume of the right cornu ammonis (CA) 2/3 was significantly lower in the MSA-CI group (P=0.029) and that of the left fimbria was significantly higher in the MSA-CP group (P=0.046). Results of linear regression analysis showed that the right CA2/3 volume was significantly correlated with the Frontal Assessment Battery score in patients with MSA (adjusted R 2 = 0.282, β = 0.227, and P=0.041). The hippocampal subfield volume decreased in patients with MSA-CI, even at the early disease stages. Specific structural changes in the hippocampus might be associated with cognitive deficits in MSA.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants.

Recent developments in image analysis have enabled an individual's brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Intravascular Large B-Cell Lymphoma (IVLBCL) Presenting with CNS Involvement in Patient with Chronic Lymphocytic Leukemia

Background: Intravascular lymphoma (IVL) is a rare, often fatal disease characterized by intraluminal proliferation of lymphoid cells within blood vessels. Intravascular Large B-Cell Lymphoma (IVLBCL) is frequently found in different organs, but the skin and central nervous system (CNS) are the most affected ones. Routine radiological examination and additional diagnostic tools, such as high-resolution, three-dimensional (3D) FLAIR sequence, T2-weighted (T2W) and T1-weighted (T1W) contrast-enhanced magnetic resonance imaging (MRI), MRI diffusion and perfusion imaging studies are useful to confirm the diagnosis in cases where it is not easy to perform histopathological examinations [1]. 1.2. Case Report: We present a clinical case of a 63-year-old female patient who presented with progressive cognitive deficit as mild motor aphasia, acalculia, agraphia, dyslexia and mild ideomotor apraxia without any signs of other organ involvement. It was known that the patient had 5-year anamnesis of chronic lymphocytic leukemia, stage II and splanchnic venous thrombosis 2 months ago. Blood biochemical analysis without any significant change, except leukocytosis in full blood count (leukocytes 15,2 x 10*9/L, where neutrophils were in normal range, but lymphocytes were increased). Head magnetic resonance imaging (MRI) showed pathological signal areas on the left parietal lobe with hypointense signal on contrast-enhanced T1-weighted, hyperintense signal on contrast-enhanced T2-weighted and FLAIR-weighted MRI images with restricted diffusion and slightly lower ADC value. Magnetic resonance perfusion imaging studies did not show an increase in cerebral blood flow or in cerebral blood volume. In this case, the patient’s diagnosis was made based on typical radiological features. 1.3. Conclusions: Intravascular large B-cell lymphoma is a rare subtype of extra nodal large B-cell lymphoma with an aggressive clinical course. The diagnosis is challenging, as it presents without any obvious tumour mass or lymphadenopathy. [2] As the clinical presentation is variable without specific characteristics, mostly biopsy or even autopsy confirms the definite diagnosis. But in some cases, the diagnosis can be made by typical radiological findings in the restricted availability of brain biopsy.

SYNTHETIC CT GENERATION FROM T1-WEIGHTED KNEE MRIs USING A UNET

Despite recent evolution in magnetic resonance imaging (MRI) techniques for musculoskeletal applications, computed tomography (CT) remains the reference modality for the assessment of bone structure. The use of generative deep learning models, such as U-Nets, was shown to enable the synthesis of CT-like contrasts from MRI images. However, the development and validation of such tools have been hindered by the need for large datasets with paired CT and MRI acquisitions. In this preliminary work, we propose to train a U-Net, a supervised deep learning technique, to generate synthetic CT (sCT) knee images from three-dimensional T1-weighted MRI scans by leveraging a large knee dataset with paired acquisitions. The synthetic CT images were then assessed quantitatively and qualitatively. A cohort consisting of 249 patients (39.7±16.0 years old, 133 females) received both a knee MR examination (3T MAGNETOM Prismafit, Siemens Healthcare, Erlangen, Germany) and a CT scan (Revolution, General Electronics Healthcare). T1-weighted MR images (TR 700ms, TE 11ms, 0.5mm isotropic) were spatially registered to the down-sampled CT data (originally 0.3mm isotropic). To ensure a good voxel-to-voxel correspondence, the 99 best registered image pairs were selected and split between training (80%), testing (10%) and validation sets (10%). During training, 100 central slices were extracted for each orientation (axial, coronal and sagittal) and fed to a 2.5D network as stacks of three consecutive MRI slices. At inference, sCT slices were generated for each orientation, and the voxel-wise median across orientations was computed. Qualitatively, our method successfully generated images with a CT-like contrast exhibiting satisfactory levels of anatomical details, including bone contours, and the femoral and tibial physes. However, the sCT images looked generally oversmoothed compared to the original CT data, hindering the visualization of some of the bone trabeculae, especially in the epiphyses. Some anatomical details such as vascular canals were not depicted accurately. In terms of quantitative evaluation, our model achieved a mean average error of 167±23.1 and 49.5±6.76 Hounsfield Units (HU) in bone and soft tissue, respectively. In this preliminary work, we showed the feasibility of generating sCT images from T1-weighted MR data with a good level of anatomical details and quantitative HU estimation. Future work will focus on reducing the impact of registration errors to further improve the model accuracy.

Changes in vessel wall magnetic resonance contrast-enhancement in a patient with multiple cerebral infarction due to meningovascular neurosyphilis

A 46-year-old man with a history of generalized skin rash following physical contact with possible syphilis infection developed right upper and lower extremity ataxia and right lower extremity paresis. Brain magnetic resonance (MR) imaging revealed multiple areas of acute cerebral infarction mainly within the territories of the right superior cerebellar artery (SCA) and left anterior cerebral artery. The patient was diagnosed with meningovascular neurosyphilis based on positive results on syphilis testing of the serum and cerebrospinal fluid. MR angiography revealed decreased signal intensity in the proximal segment of the right SCA, and gadolinium-enhanced three-dimensional T1-weighted MR images showed an enhancement of the vessel wall in this segment of the artery. Signal intensity in the right SCA showed partial improvement following the completion of intravenous penicillin treatment, and contrast enhancement of the vessel wall disappeared simultaneously with clinical improvement. Alterations in cerebral vessel walls on contrast-enhanced MR imaging in cases of meningovascular neurosyphilis may reflect vascular inflammatory activity.

Alterations of Limbic Structure Volumes in Patients with Obstructive Sleep Apnea.

We investigated the change in limbic structure volumes and intrinsic limbic network in patients with obstructive sleep apnea (OSA) compared to healthy controls. We enrolled 26 patients with OSA and 30 healthy controls. They underwent three-dimensional T1-weighted magnetic resonance imaging (MRI) on a 3 T MRI scanner. The limbic structures were analyzed volumetrically using the FreeSurfer program. We examined the intrinsic limbic network using the Brain Analysis with Graph Theory program and compared the groups' limbic structure volumes and intrinsic limbic network. There were significant differences in specific limbic structure volumes between the groups. The volumes in the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens in patients with OSA were lower than those in healthy controls (right amygdala, 0.102 vs. 0.113%, p = 0.004; right hippocampus, 0.253 vs. 0.281%, p = 0.002; right hypothalamus, 0.028 vs. 0.032%, p = 0.002; right nucleus accumbens, 0.021 vs. 0.024%, p = 0.019; left amygdala, 0.089 vs. 0.098%, p = 0.007; left basal forebrain, 0.020 vs. 0.022%, p = 0.027; left hippocampus, 0.245 vs. 0.265%, p = 0.021; left hypothalamus, 0.028 vs. 0.031%, p = 0.016; left nucleus accumbens, 0.023 vs. 0.027%, p = 0.002). However, there were no significant differences in network measures between the groups. We demonstrate that the volumes of several limbic structures in patients with OSA are significantly lower than those in healthy controls. However, there are no alterations to the intrinsic limbic network. These findings suggest that OSA is one of the risk factors for cognitive impairments.

Resting-state brain network in Parkinson's disease with different degrees of depression.

ObjectiveThe aim of this study is to explore the neural network mechanism of Parkinson’s disease (PD) with different degrees of depression using independent component analysis (ICA) of the functional connectivity changes in the forehead, limbic system, and basal ganglia regions.MethodsA total of 106 patients with PD were divided into three groups: PD with moderate-severe depression (PDMSD, n = 42), PD with mild depression (PDMD, n = 29), and PD without depression (PDND, n = 35). Fifty gender- and age-matched healthy subjects were recruited as a control group (HC). Three-dimensional T1-weighted image and resting-state functional magnetic resonance imaging (RS-fMRI) data were collected.ResultsDifferent functional connectivity was observed in the left precentral gyrus, right precuneus, right inferior frontal gyrus, right medial and paracingulate gyrus, left supplementary motor area, right brain insula, and the inferior frontal gyrus of the left orbit among the four groups (ANOVA, P < 0.05, Voxel size > 5). Both PDMD and PDMSD exhibited increased functional connectivity in the superior-posterior default-mode network (spDMN) and left frontoparietal network (LFPN); they also exhibited a decreased functional connectivity in the interior Salience Network (inSN) when compared with the PDND group. The functional connectivity within the inSN network was decreased in the PDMSD group when compared with the PDMD group (Alphasim correction, P < 0.05, voxel size > 5).ConclusionPD with different degrees of depression has abnormal functional connectivity in multiple networks, which is an important neurobiological basis for the occurrence and development of depression in PD. The degree of decreased functional connectivity in the inSN network is related to the degree of depression in patients with PD-D, which can be an imaging marker for PD to judge the severity of depression.

Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.

Short-term disability progression was predicted from a baseline evaluation in patients with multiple sclerosis (MS) using their three-dimensional T1-weighted (3DT1) magnetic resonance images (MRI). One-hundred-and-eighty-one subjects diagnosed with MS underwent 3T-MRI and were followed up for two to six years at two sites, with disability progression defined according to the expanded-disability-status-scale (EDSS) increment at the follow-up. The patients’ 3DT1 images were bias-corrected, brain-extracted, registered onto MNI space, and divided into slices along coronal, sagittal, and axial projections. Deep learning image classification models were applied on slices and devised as ResNet50 fine-tuned adaptations at first on a large independent dataset and secondly on the study sample. The final classifiers’ performance was evaluated via the area under the curve (AUC) of the false versus true positive diagram. Each model was also tested against its null model, obtained by reshuffling patients’ labels in the training set. Informative areas were found by intersecting slices corresponding to models fulfilling the disability progression prediction criteria. At follow-up, 34% of patients had disability progression. Five coronal and five sagittal slices had one classifier surviving the AUC evaluation and null test and predicted disability progression (AUC > 0.72 and AUC > 0.81, respectively). Likewise, fifteen combinations of classifiers and axial slices predicted disability progression in patients (AUC > 0.69). Informative areas were the frontal areas, mainly within the grey matter. Briefly, 3DT1 images may give hints on disability progression in MS patients, exploiting the information hidden in the MRI of specific areas of the brain.