A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image.

BACKGROUND AND OBJECTIVE: This study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images. METHODS: In this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process. RESULTS: We conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.

Computational imaging for rapid detection of grade-I cerebral small vessel disease (cSVD).

An early identification and subsequent management of cerebral small vessel disease (cSVD) grade 1 can delay progression into grades II and III. Machine learning algorithms have shown considerable promise in medical image interpretation automation. An experimental cross-sectional study aimed to develop an automated computer-aided diagnostic system based on AI (artificial intelligence) tools to detect grade 1-cSVD with improved accuracy. Patients with Fazekas grade 1 cSVD on Non-Contrast Magnetic Resonance Imaging (MRI) Brain of age >40 years of both genders were included. The dataset was pre-processed to be fed into a 3D convolutional neural network (CNN) model. A 3D stack with the shape (120, 128, 128, 1) containing axial slices from the brain magnetic resonance image was created. The model was created from scratch and contained four convolutional and three fully connected (FC) layers. The dataset was preprocessed by making a 3D stack, and normalizing, resizing, and completing the stack was performed. A 3D-CNN model architecture was designed to train and test preprocessed images. We achieved an accuracy of 93.12 % when 2D axial slices were used. When the 2D slices of a patient were stacked to form a 3D image, an accuracy of 85.71 % was achieved on the test set. Overall, the 3D-CNN model performed very well on the test set. The earliest and the most accurate diagnosis from computational imaging methods can help reduce the huge burden of cSVD and its associated morbidity in the form of vascular dementia.

Assessment of Age-Related Differences in Lower Leg Muscles Quality Using Radiomic Features of Magnetic Resonance Images.

Sarcopenia, characterised by a decline in muscle mass and strength, affects the health of the elderly, leading to increased falls, hospitalisation, and mortality rates. Muscle quality, reflecting microscopic and macroscopic muscle changes, is a critical determinant of physical function. To utilise radiomic features extracted from magnetic resonance (MR) images to assess age-related changes in muscle quality, a dataset of 24 adults, divided into older (male/female: 6/6, 66-79 years) and younger (male/female: 6/6, 21-31 years) groups, was used to investigate the radiomics features of the dorsiflexor and plantar flexor muscles of the lower leg that are critical for mobility. MR images were processed using MaZda software for feature extraction. Dimensionality reduction was performed using principal component analysis and recursive feature elimination, followed by classification using machine learning models, such as support vector machine, extreme gradient boosting, and naïve Bayes. A leave-one-out validation test was used to train and test the classifiers, and the area under the receiver operating characteristic curve (AUC) was used to evaluate the classification performance. The analysis revealed that significant differences in radiomic feature distributions were found between age groups, with older adults showing higher complexity and variability in muscle texture. The plantar flexors showed similar or higher AUC than the dorsiflexors in all models. When the dorsiflexor muscles were combined with the plantar flexor muscles, they tended to have a higher AUC than when they were used alone. Radiomic features in lower-leg MR images reflect ageing, especially in the plantar flexor muscles. Radiomic analysis can offer a deeper understanding of age-related muscle quality than traditional muscle mass assessments.

Basal inferoseptal segment is highly susceptible to deformation in the clinical spectrum of transthyretin-derived amyloid cardiomyopathy.

Aims: While the prevalence of transthyretin-derived amyloid cardiomyopathy (ATTR-CM) is on the rise, detailed understanding of its morphological and functional characteristics within the left ventricle (LV) across heart failure (HF) remains limited. Methods and results: Utilizing two-dimensional (2D) speckle-tracking echocardiography, we assessed longitudinal strain (LS) in 63 histology-confirmed ATTR-CM patients. Additionally, cardiac magnetic resonance (CMR) images measured native T1 and extracellular volume (ECV), compared with LS across 18 LV segments. Patients were categorized into three groups based on HF status: Group 1 (no HF symptoms), Group 2 (HF with preserved LV ejection fraction), and Group 3 (HF with reduced LV ejection fraction). LS analysis unveiled susceptibility to deformation in the basal inferoseptal segment, persisting even in asymptomatic cases. CMR demonstrated increasing native T1 deviation, particularly evident in segments distant from the inferoseptal region. Contrastingly, maximal ECV was consistently observed in the basal and mid-ventricular inferior-septum, even in asymptomatic individuals. Segmental LS decline correlated with ECV expansion but not with native T1 values. Conclusion: Our findings suggest that the inferoseptal segment is highly susceptible to amyloid infiltration, and 2D speckle-tracking echocardiography and CMR may serve as a valuable tool for its early detection.

Occipital condyle syndrome due to small-cell lung carcinoma: A case report.

Skull base metastases, including those from small-cell lung carcinoma (SCLC), can present with various syndromes depending on the site of involvement, such as orbital syndrome, parasellar syndrome, middle fossa syndrome, jugular foramen syndrome, and occipital condyle syndrome (OCS). One such example is OCS, which consists of unilateral occipital headache accompanied with ipsilateral hypoglossal palsy. This case report describes a 51-year-old man initially diagnosed with OCS, which led to the discovery of systemic bone metastases from SCLC. Magnetic resonance imaging showed lesions in the occipital condyle and hypoglossal canal, while positron emission tomography-computed tomography identified a lung mass and widespread metastases. SCLC is highly aggressive and metastatic, with the bone being a common site of spread. In this case, the OCS preceded the diagnosis of the underlying malignancy. Prompt diagnosis and treatment are crucial, as patients with OCS often have advanced disease. This case highlights the importance of considering SCLC as a potential etiology for OCS, given the propensity for bone metastases. Early recognition and evaluation of OCS is essential to initiate appropriate management.

A single sequence MRI-based deep learning radiomics model in the diagnosis of early osteonecrosis of femoral head.

Purpose: The objective of this study was to create and assess a Deep Learning-Based Radiomics model using a single sequence MRI that could accurately predict early Femoral Head Osteonecrosis (ONFH). This is the first time such a model was used for the diagnosis of early ONFH. Its simpler than the previously published multi-sequence MRI radiomics based method, and it implements Deep learning to improve on radiomics. It has the potential to be highly beneficial in the early stages of diagnosis and treatment planning. Methods: MRI scans from 150 patients in total (80 healthy, 70 necrotic) were used, and split into training and testing sets in a 7:3 ratio. Handcrafted as well as deep learning features were retrieved from Tesla 2 weighted (T2W1) MRI slices. After a rigorous selection process, these features were used to construct three models: a Radiomics-based (Rad-model), a Deep Learning-based (DL-model), and a Deep Learning-based Radiomics (DLR-model). The performance of these models in predicting early ONFH was evaluated by comparing them using the receiver operating characteristic (ROC) and decision curve analysis (DCA). Results: 1,197 handcrafted radiomics and 512 DL features were extracted then processed; after the final selection: 15 features were used for the Rad-model, 12 features for the DL-model, and only 9 features were selected for the DLR-model. The most effective algorithm that was used in all of the models was Logistic regression (LR). The Rad-model depicted good results outperforming the DL-model; AUC = 0.944 (95%CI, 0.862-1.000) and AUC = 0.930 (95%CI, 0.838-1.000) respectively. The DLR-model showed superior results to both Rad-model and the DL-model; AUC = 0.968 (95%CI, 0.909-1.000); and a sensitivity of 0.95 and specificity of 0.920. The DCA showed that DLR had a greater net clinical benefit in detecting early ONFH. Conclusion: Using a single sequence MRI scan, our work constructed and verified a Deep Learning-Based Radiomics Model for early ONFH diagnosis. This strategy outperformed a Deep learning technique based on Resnet18 and a model based on Radiomics. This straightforward method can offer essential diagnostic data promptly and enhance early therapy strategizing for individuals with ONFH, all while utilizing just one MRI sequence and a more standardized and objective interpretation of MRI images.

Massive, Retroperitoneal, Well-Differentiated Liposarcoma in Second-Trimester Pregnancy: A Report of a Rare Case.

Retroperitoneal liposarcoma during pregnancy is rare and poses significant diagnostic challenges, even for experienced specialists. We present a case report of a 27-year-old female patient, 15 weeks pregnant, who was admitted to the hospital due to a massive retroperitoneal liposarcoma. The patient underwent surgical resection of the tumor. Postoperative pathology confirmed a diagnosis of well-differentiated liposarcoma. Although liposarcoma during pregnancy is rare and challenging to diagnose, CT or MRI plays a crucial role in its detection. The recurrence rate depends on the pathological stage, histological grade, and ability to resect the tumor.

Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.

Background: Axial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients. Methods: We retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians. Results: NegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01). Conclusions: The proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses. The CNN, fed with cine images and Fourier-based parametric images, achieved an area under the ROC curve of 0.86 (accuracy 0.79, F1 0.81, sensitivity 0.9, specificity 0.65, and negative (NPV) and positive (PPV) predictive values 0.83 and 0.77, respectively), for individual slice classification. Remarkably, it exhibited 1.0 prediction accuracy (F1 0.98, sensitivity 1.0, specificity 0.9, NPV 1.0, and PPV 0.97) in patient classification as a control or pathologic. The proposed approach represents a first step towards scar detection in contrast-free CMR images. Patient-level results suggest its preliminary potential as a screening tool to guide decisions regarding LGE-CMR prescription, particularly in cases where indication is uncertain.

Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images.

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from -17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from -6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility.

The difference of functional MR imaging in evaluating outcome of patients with diffuse and compact brain arteriovenous malformation.

Microsurgical resection is an effective method to treat brain arteriovenous malformations (BAVMs). Functional magnetic resonance imaging (fMRI) can evaluate the spatial relationship of nidus and eloquent. Diffuse BAVMs are related to poor outcomes postoperatively. The role of fMRI in evaluating outcomes in patients with different nidus types remains unclear. BAVM patients received microsurgical resection were included from a prospective, multicenter cohort study. All patients underwent fMRI evaluation preoperatively and were regularly followed up postoperatively. Diffuse BAVM is radiologically identified as nidus containing normal brain tissue interspersing between malformed vessels. Lesion-to-eloquent distance (LED) was calculated based on the relationship between nidus and eloquent. The primary outcome was 180-day unfavorable neurological status postoperatively. The risk of primary outcome was investigated within different BAVM nidus types. The LED's performance to predict poor outcome was evaluated using area under curve (AUC). 346 BAVM patients were included in this study. 93 (26.9%) patients were found to have a 180-day unfavorable outcome. Multivariate logistic analysis demonstrated LED (odd ratio [OR], 0.44; 0.34-0.57; P < 0.001) and mRS at admission (OR, 2.59; 1.90-3.54; P < 0.001) as factors of unfavorable outcome. Subgroup analysis showed LED and mRS at admission as factors of unfavorable outcome for patients with compact BAVMs (all P < 0.05), but not for patients with diffuse BAVMs. Subsequent analysis showed that LED performed poorly to predict the unfavorable outcome for patients with diffuse BAVMs, compared with patients with compact BAVMs (AUC as 0.69 vs. 0.86, P < 0.05). A larger cutoff value of LED to unfavorable outcome was found in patients with diffuse BAVMs (15 mm) compared with patients with compact BAVMs (4.7 mm). Usage of LED to evaluate postoperative outcome of patients with diffuse BAVMs differs from its use in patients with compact BAVMs. Specific assessment strategy considering BAVM nidus types could help improve patients' outcome. MITASREAVM cohort (unique identifier: NCT02868008, https://clinicaltrials.gov/study/NCT02868008?term=NCT02868008&rank=1 ).

Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population.

BACKGROUND: Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. METHODS: We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. RESULTS: Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. CONCLUSION: These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

Assessment of morphology and hemodynamics in a surgically clipped neck of a cerebral aneurysm: illustrative case.

BACKGROUND: Silent magnetic resonance angiography reduces metal artifacts, enabling clear visualization of the clipped neck following surgical clipping of cerebral aneurysms. This study aimed to delineate the morphology of the clipped neck complex in cerebral aneurysms using three-dimensional (3D) multifusion imaging of silent magnetic resonance angiography and fast spin echo magnetic resonance cisternography. Additionally, computational fluid dynamics analysis was utilized to evaluate the hemodynamics of the parent vessel at the clipped neck, allowing for a detailed assessment of hemodynamics at the clipped neck. OBSERVATIONS: The 3D multifusion image enabled visualization of the orientation and shape of the clip within the clipped neck complex, alongside the morphology of the parent vessel. In the hemodynamic analysis of the parent vessel at the clipped neck, areas of high-intensity magnitude of wall shear stress (WSSm) variation corresponding to the clip's contour, along with significant vector of wall shear stress (WSSv) variation related to vector directionality, were visualized in 3D. The intentional residual neck, coated with muscle grafts, was depicted as an area with low WSSm variation values and high WSSv variation values. LESSONS: Three-dimensional multifusion imaging, along with computational fluid dynamics analysis of the parent vessels, facilitated both the morphological and hemodynamic visualization and assessment of the clipped neck complex following neck clipping surgery for cerebral aneurysms. https://thejns.org/doi/10.3171/CASE24194.

Developmental change of brain volume in Rett syndrome in Taiwan.

OBJECTIVE: Rett syndrome (RTT) is characterized by neurological regression. This pioneering study investigated the effect of age on brain volume reduction by analyzing magnetic resonance imaging findings in participants with RTT, ranging from toddlers to adults. METHODS: Functional evaluation and neuroimaging were performed. All scans were acquired using a Siemens Tim Trio 3 T scanner with a 32-channel head coil. RESULTS: The total intracranial volume and cerebral white matter volume significantly increased with age in the control group compared with that in the RTT group (p < 0.05). Cortical gray matter volume reduction in the RTT group continued to increase in bilateral parietal lobes and left occipital lobes (p < 0.05). The differences in cortical gray matter volume between typically developing brain and RTT-affected brain may tend to continuously increase until adulthood in both temporal lobes although not significant after correction for multiple comparison. CONCLUSIONS: A significant reduction in brain volume was observed in the RTT group. Cortical gray matter volume in the RTT group continued to reduce in bilateral parietal lobes and left occipital lobes. These results provide a baseline for future studies on the effect of RTT treatment and related neuroscience research.

An end-to-end infant brain parcellation pipeline.

Objective: Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs. Methods: We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global regions of interest (ROIs) localization stage, a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries o the ROIs are refined for a more accurate parcellation. Results: We utilized the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods. Conclusion: Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

A Semi-Automatic Magnetic Resonance Imaging Annotation Algorithm Based on Semi-Weakly Supervised Learning.

The annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image annotation. However, the existing semi-automatic annotation algorithms based on deep learning have poor pre-annotation performance in the case of insufficient segmentation labels. In this paper, we propose a semi-automatic MRI annotation algorithm based on semi-weakly supervised learning. In order to achieve a better pre-annotation performance in the case of insufficient segmentation labels, semi-supervised and weakly supervised learning were introduced, and a semi-weakly supervised learning segmentation algorithm based on sparse labels was proposed. In addition, in order to improve the contribution rate of a single segmentation label to the performance of the pre-annotation model, an iterative annotation strategy based on active learning was designed. The experimental results on public MRI datasets show that the proposed algorithm achieved an equivalent pre-annotation performance when the number of segmentation labels was much less than that of the fully supervised learning algorithm, which proves the effectiveness of the proposed algorithm.

Three-dimensional magnetic resonance imaging-based statistical shape analysis and machine learning-based prediction of patellofemoral instability.

This study performed three-dimensional (3D) magnetic resonance imaging (MRI)-based statistical shape analysis (SSA) by comparing patellofemoral instability (PFI) and normal femur models, and developed a machine learning (ML)-based prediction model. Twenty (19 patients) and 31 MRI scans (30 patients) of femurs with PFI and normal femurs, respectively, were used. Bone and cartilage segmentation of the distal femurs was performed and subsequently converted into 3D reconstructed models. The pointwise distance map showed anterior elevation of the trochlea, particularly at the central floor of the proximal trochlea, in the PFI models compared with the normal models. Principal component analysis examined shape variations in the PFI group, and several principal components exhibited shape variations in the trochlear floor and intercondylar width. Multivariate analysis showed that these shape components were significantly correlated with the PFI/non-PFI distinction after adjusting for age and sex. Our ML-based prediction model for PFI achieved a strong predictive performance with an accuracy of 0.909 ± 0.015, and an area under the curve of 0.939 ± 0.009 when using a support vector machine with a linear kernel. This study demonstrated that 3D MRI-based SSA can realistically visualize statistical results on surface models and may facilitate the understanding of complex shape features.

Estimation of the rupture point of the craniovertebral junction intradural arteriovenous fistula with vessel wall magnetic resonance image and its pathological findings: A case report.

Background: Arteriovenous fistulas (AVFs) of the craniocervical junction (CCJ) and intradural AVFs are often associated with aneurysms and varics, and it is sometimes difficult to identify the ruptured point on radiological images. We report a case in which vessel wall magnetic resonance image (VW-MRI) was useful for identifying the ruptured point at the CCJ AVF. Case Description: A 70-year-old man presented with a sudden onset of headache. He had Glasgow Coma Scale E4V5M6, world federation of neurosurgical societies (WFNS) Grade I. Fisher group 3 subarachnoid hemorrhage and hydrocephalus were found on head computed tomography. Cerebral angiography showed a spinal AVF at the C1 level of the cervical spine. Magnetic resonance image-enhanced motion sensitized driven equilibrium (MSDE-method showed an enhancing effect in part of the AVF draining vein, but the vascular architecture of this lesion was indeterminate. We performed continuous ventricular drainage for acute hydrocephalus and antihypertensive treatment. Cerebral angiography was performed 30days after the onset of the disease, and was revealed an aneurysmal structure in a portion of the AVF draining vein, which VW-MRI initially enhanced. On the 38th day after onset, he underwent direct surgery to occlude the AV fistula and dissect the aneurysmal structure. Histopathology showed that the aneurysmal structure was varices with lymphocytic infiltration, and hemosiderin deposition was observed near the varices. Conclusion: Recently, VW-MRI has been reported to show an association between the enhancement of varices in dural AVF and rupture cases. VW-MRI, especially the enhanced MSDE method, may be useful in estimating the ruptured point in arteriovenous shunt disease.

Magnetic resonance imaging findings in patients with dropped head syndrome.

BACKGROUND: Dropped head syndrome (DHS) is difficult to diagnose only by clinical examination. Although characteristic images on X-rays of DHS have been studied, changes in soft tissue of the disease have remained largely unknown. Magnetic resonance imaging (MRI) is useful for evaluating soft tissue, and we therefore performed this study with the purpose of investigating the characteristic signal changes of DHS on MRI by a comparison with those of cervical spondylosis. METHODS: The study involved 35 patients diagnosed with DHS within 6 months after the onset and 32 patients with cervical spondylosis as control. The signal changes in cervical extensor muscles, interspinous tissue, anterior longitudinal ligament (ALL) and Modic change on MRI were analyzed. RESULTS: Signal changes of cervical extensor muscles were 51.4% in DHS and 6.3% in the control group, those of interspinous tissue were 85.7% and 18.8%, and those of ALL were 80.0% and 21.9%, respectively, suggesting that the frequency of signal changes of cervical extensor muscles, interspinous tissue and ALL was significantly higher in the DHS group (p < 0.05). The presence of Modic change of acute phase (Modic type I) was also significantly higher in the DHS group than in the control group (p < 0.001). CONCLUSION: MRI findings of DHS within 6 months after the onset presented the characteristic signal changes in cervical extensor muscles, interspinous tissue, ALL and Modic change. Evaluation of MRI signal changes is useful for an objective evaluation of DHS.

Pre-and post-surgical non-functional pituitary adenomas and their relationship with high levels of serum glucose.

OBJECTIVES: This clinical, analytical, retro-prospective, auto-controlled, not randomized, and not blinded study, aimed to investigate the association of changes in the serum glucose levels with the pre-and-post changes in the size tumor in mm3 in the Non-Functional pituitary adenomas. METHODS: Pre-and post-surgical MRI, as well as the measurements in the serum glucose levels and immunohistochemical techniques were performed in all the patients in the study, with a mean followed-up until 208.57 days. A comparison was made between the reductions in tumor size of hormonally active pituitary adenomas (HSPAs) vs NFPAs. RESULTS: Seventy-four patients were included in this study, of whom, 46 were NFPAs. The decrease in the NFPAs tumor size after surgery was statistically significant (P ≤ 0.0001). The Mean of the differences of both type of tumors in mm3 were -9552 ± 10287. Pre-surgery, the mean of the HSPAs were 8.923 ± 2.078; and the NFPAs were 14.161 ± 1.912. The differences in the tumor size were statistically significant (p = 0.039). Post-surgical, the mean of the HSPAs were 2.079 ± 971, with a (p = 0.14): and the NFPAs were 4.609 ± 1.205. After surgery of the NFPAs, most of the patients-maintained serum levels ≤ 100 mg/dL, with a statistical significance (P ≤ 0.0003). CONCLUSION: This study demonstrates for the first time the correlation between the presence of pre-and post- surgical changes in the NFPAs, with modifications in the levels of serum glucose, and the comparison, pre- and post-surgical between the tumor size of HSPAs and NFPAs.