Anatomical Magnetic Resonance Imaging Research Articles

Cortical thinning is associated with brain pulsatility in older adults: An MRI and NIRS study

Aging is accompanied by global brain atrophy occurring unequally across the brain. Cortical thinning is seen with aging with a larger loss in the frontal and temporal subregions. We explored the link between regional cortical thickness and regional cerebral pulsatility. Sixty healthy individuals were divided into two age groups, young (aged 19–31) and older (aged 65–75) adults. Each participant underwent a near-infrared spectroscopy (NIRS) scan to index regional brain pulsatility from cerebral pulse-transit-time-to-the peak-of-the-pulse (PTTp), an anatomical magnetic resonance imaging (MRI) and a phase-contrast MRI (PC-MRI) scan to measure arterial and cerebrospinal fluid (CSF) pulsatility. In older adults, the greatest association between cerebral pulsatility and cortical thickness was found in superior and middle temporal and superior, middle and inferior frontal areas, which are the regions perfused first by the internal carotid arteries. This association dropped in the postcentral and superior parietal regions. These findings suggest higher brain pulsatility as a potential risk factor contributing to cortical thinning for some brain regions more than others.

The effect of groupness constraint on the sensitivity and specificity of canonical correlation analysis, a multi-modal anatomical and functional MRI study

While neuroimaging studies reveal that several brain regions may participate in multiple groups (networks), this group overlap is neglected in multi-modal data fusion frameworks. Indeed, it is not clear how much “information” is lost due to this negligence. To study this issue, we present a group-structured sparse canonical correlation analysis (gssCCA) technique by utilizing groupness and sparsity constraints in a unified fusion framework. The approach allows to: 1) compare structures of disjoint and overlapping groups (networks); and 2) consider appropriate levels of overlap among groups. We use simulations to investigate the performance of the proposed approach and compare overlapping gssCCA, disjoint gssCCA, and ssCCA. The gains of considering an overlapping groupness constraint are significant since it can increase detection sensitivity and specificity of associations between multi-modal datasets. They also demonstrate that, even when the ROIs are assumed to be disjoint, the lost structural information is less than the conditions that we do not have any groupness information. We also apply the methods to experimental anatomical magnetic resonance imaging (MRI) and resting-state functional MRI (rs-fMRI) data of schizophrenia (SZ) patients and control subjects (CS). The results show that the first pair of canonical variates (CVs) capture better classification accuracy between SZ and CS with a correlation of 88 % (p-value of less than 1 × 10−6). The extracted CVs show the most correlated clusters between anatomical and functional datasets. These clusters explain differences between the two groups in their own modality that are maximally correlated with the corresponding clusters in the other modality.

Topographic volume-standardization atlas of the human brain

Specific anatomical patterns are seen in various diseases affecting the brain. Clinical studies on the topography of pathologies are often limited by the absence of a normalization of the prevalence of pathologies to the relative volume of the affected anatomical structures. A comprehensive reference on the relative volumes of clinically relevant anatomical structures serving for such a normalization, is currently lacking. The analyses are based on anatomical high-resolution three-dimensional T1-weighted magnetic resonance imaging data of 30 healthy Caucasian volunteers, including 14 females (mean age 37.79 years, SD 13.04) and 16 males (mean age 38.31 years, SD 16.91). Semi-automated anatomical segmentation was used, guided by a neuroanatomical parcellation algorithm differentiating 96 structures. Relative volumes were derived by normalizing parenchymal structures to the total individual encephalic volume and ventricular segments to the total individual ventricular volume. The present investigation provides the absolute and relative volumes of 96 anatomical parcellation units of the human encephalon. A larger absolute volume in males than in females is found for almost all parcellation units. While parenchymal structures display a trend towards decreasing volumes with increasing age, a significant inverse effect is seen with the ventricular system. The variances in volumes as well as the effects of gender and age are given for each structure before and after normalization. The provided atlas constitutes an anatomically detailed and comprehensive analysis of the absolute and relative volumes of the human encephalic structures using a clinically oriented parcellation algorithm. It is intended to serve as a reference for volume-standardization in clinical studies on the topographic prevalence of pathologies.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses.

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

Neural biomarker of functional disability in major depressive disorder: A structural neuroimaging study

BackgroundMost patients with the major depressive disorder (MDD) have varying degrees of impaired social functioning, and functional improvement often lags behind symptomatic improvement. However, it is still unclear if certain neurobiological factors underlie the deficits of social function in MDD. The aim of this study was to investigate the biomarkers of social function in MDD using structural magnetic resonance imaging (MRI). Methods3T anatomical MRI was obtained from 272 subjects including 46 high-functioning (high-SF, Sheehan Disability Scale (SDS) rating < 18) and 63 low-functioning (low-SF, SDS score ≥ 18) patients with MDD and 163 healthy controls (HC). Voxel-based morphometry (VBM) was employed to locate brain regions with grey matter (GM) volume differences in relation to social function in MDD. Regions showing GM differences in relation to social function at baseline were followed up longitudinally in a subset of 38 patients scanned after 12-week treatment. ResultsVolume of right parahippocampal gyrus (rPHG) was significantly reduced in low-SF patients with MDD when compared to high-SF ones (FDR-corrected p < 0.05). Over 12 weeks of follow-up, though SF improved overall, the high and low-SF subgroups continued to differ in their SF, but had no progressive changes in PHG volume. LimitationsLimited functional assessment, high drop-out rate and median-based grouping method. ConclusionsGreater GM volume (GMV) of the rPHG may mark better social function in patients with MDD.

Noninvasive Assessment of Neurodevelopmental Disorders after In Utero Irradiation in Mice: An In Vivo Anatomical and Diffusion MRI Study.

In utero exposure to ionizing radiation can lead to cerebral alterations during adulthood. Using anatomical magnetic resonance imaging (MRI), it is possible to assess radiation-induced structural brain damage noninvasively. However, little is currently known about microstructure alterations in brain tissue. Therefore, the goal of this study was to establish, based on an original and robust pipeline of MRI image analysis, whether the long-term effects of in utero radiation exposure on brain tissue microstructure could be detected noninvasively. Pregnant C57BL/6N mice received a single dose of 1 Gy on gestation day 14.5, which led to behavioral impairments in adults. At 3 months old, in vivo MRI data were acquired from in utero irradiated and nonirradiated male mice. An MRI protocol was designed to assess the effects of radiation on the parameters of brain volume, non-Gaussian diffusion (ADC0, kurtosis and signature index) and anisotropic diffusion (fractional anisotropy and mean, axial, radial diffusivities and anisotropic signature index) in 10 key cerebral structures defined using an in-house atlas of the mouse brain. Based on the relative amplitude of these anatomical and microstructural changes, maps of the radiosensitivity of the brain to in utero irradiation were created. We observed microcephaly in irradiated mice with noticeably larger volume changes in the cortex and the corpus callosum. We also observed significantly lower ADC0, anisotropy fraction (sFA), radial diffusivity (sRD), as well as signature index (S-index and SI3) values, which are original markers sensitive to tissue microstructure alterations. All these changes together are in favor of a decreased cellular "imprint" and in some regions a reduced density in myelinated axons. A reduction in the number and complexity of myelinated axons was further revealed by myelin basic protein immunostaining. Combining anatomical and diffusion MRI is a promising approach to noninvasively investigate the radiosensitivity of local brain areas in adult mice after in utero irradiation in terms of microstructure.

Magnetic Resonance Imaging of Uterine Cervix: A Pictorial Essay.

Uterine cervix is the lower constricted part of uterus which is best evaluated by magnetic resonance imaging (MRI) due to its higher soft tissue and contrast resolution. The cervical cancer is a common gynecological cancer causing much morbidity and mortality especially in developing countries. Cervical carcinomas mainly occurs in reproductive age group with prognosis mainly depending on the extent of disease at the time of diagnosis, hence it is important to identify these cancerous lesions early and stage them accurately for optimal treatment. In this article, we will review the following: (1) the normal MRI anatomy of uterine cervix; (2) MRI protocol and techniques in evaluation of cervical lesions; (3) imaging of spectrum of various congenital abnormalities and pathologies affecting uterine cervix which ranges from congenital abnormalities to various benign lesions of cervix like nabothian cysts, tunnel cysts, cervicitis, cervical fibroid, and, lastly, endometriosis which usually coexists with adenomyosis; the malignant lesions include carcinoma cervix, adenoma malignum or direct extension from carcinoma endometrium or from carcinoma of vagina; (4) Accurately stage carcinoma of cervix using FIGO classification (2018); and (5) posttreatment evaluation of cervical cancers. MRI is the most reliable imaging modality in evaluation of various cervical lesions, identification of cervical tumors, staging of the cervical malignancy, and stratifying patients for surgery and radiation therapy. It also plays an important role in detection of local disease recurrence.

Magnetic Resonance Imaging of the Oral Cavity and Oropharynx.

Understanding oral cavity and oropharyngeal anatomy is important to identify various pathologies that may afflict them. This article reviews normal magnetic resonance imaging anatomy of these vital spaces and structures, with special attention to the complex musculature, mucosal surfaces, relevant osseous structures, salivary glands, and nerves. Anatomic awareness of these spaces and critical potential pathways for perineural tumoral spread are important to recognize to improve diagnostic evaluation and treatment.

Real-Time Magnetic Resonance Guided Focused Ultrasound for Painful Bone Metastases.

Bones are one of the most common sites of cancer metastasis, which usually causes pain and impairs quality of life. Radiation therapy combined with opioids is the standard treatment for painful bone metastases. This treatment achieves effective pain control in 60-74% of patients, but limited treatment choices with limited benefits are available for recurrent or residual painful bone metastases after radiotherapy. More than 40% of patients still experience moderate to severe bone pain after reirradiation. Magnetic resonance-guided focused ultrasound (MRgFUS) combines high-intensity focused ultrasound, which achieves thermal ablation of bone metastases and subsequent pain reduction, with real-time magnetic resonance (MR) thermometry to monitor the temperature of anatomic MR images, with an accuracy of 1 °C, spatial resolution of 1 mm, and temporal resolution within 3 s. As well as being increasingly used clinically for controlling metastatic bone pain, the use of MRgFUS for other diseases has also been tested. However, the use of MR software as a thermometer is the only technique available to verify the accuracy of the software and assure energy delivery. Here, we describe an efficient method of quality assurance we developed for thermal detection and energy delivery before each MRgFUS treatment and also propose a modified workflow to expedite the treatment course as well as to reduce patients' pain during the procedure.

Pulmonary blood volume estimation in mice by magnetic particle imaging and magnetic resonance imaging

This methodical work describes the measurement and calculation of pulmonary blood volume in mice based on two imaging techniques namely by using magnetic particle imaging (MPI) and cardiac magnetic resonance imaging (MRI). Besides its feasibility aspects that may influence quantitative analysis are studied. Eight FVB mice underwent cardiac MRI to determine stroke volumes and anatomic MRI as morphological reference for functional MPI data. Arrival time analyses of boli of 1 µl of 1 M superparamagnetic tracer were performed by MPI. Pulmonary transit time of the bolus was determined by measurements in the right and left ventricles. Pulmonary blood volume was calculated out of stroke volume, pulmonary transit time and RR-interval length including a maximal error analysis. Cardiac stroke volume was 31.7 µl ± 2.3 µl with an ejection fraction of 71% ± 6%. A sharp contrast bolus profile was observed by MPI allowing subdividing the first pass into three distinct phases: tracer arrival in the right ventricle, pulmonary vasculature, and left ventricle. The bolus full width at half maximum was 578 ms ± 144 ms in the right ventricle and 1042 ms ± 150 ms in the left ventricle. Analysis of pulmonary transit time revealed 745 ms ± 81 ms. Mean RR-interval length was 133 ms ± 12 ms. Pulmonary blood volume resulted in 177 µl ± 27 µl with a mean maximal error limit of 27 µl. Non-invasive assessment of the pulmonary blood volume in mice was feasible. This technique can be of specific value for evaluation of pulmonary hemodynamics in mouse models of cardiac dysfunction or pulmonary disease. Pulmonary blood volume can complement cardiac functional parameters as a further hemodynamic parameter.

Cingulum bundle connectivity considering the double-layered cingulate cortex using diffusion-weighted MRI

The cingulum bundle (CB) is a fasciculus in the white matter surrounded by the cingulate cortex, which is generally subdivided into the anterior, middle, and posterior parts based on their function and cytology. Interestingly, the cingulate cortex can also be divided into inner and outer layers based on their gross anatomy. Herein, we analyzed the CB pathway by considering the double-layered cingulate cortex using magnetic resonance (MR) tractography data. We used 7.0T diffusion-weighted MRI data from the Human Connectome Project dataset, from which 32 individual datasets were selected. Anatomical MR images in each dataset were parcellated into the cortical subdivisions automatically using the FreeSurfer software. The seed positions in both layers of the cingulate white matter (CWM) for fiber tracking were determined based on the parcellated MR image data. The cingulate cortex with double layers was found in 46.5% of the coronal section in the cingulate cortex’s dorsal part, with many individual differences in the outer-layered CWM. Fiber extraction from each determined seed position in both layers of the CWM was carried out using the MRtrix software. The fiber tracking results indicated that the anterior and posterior areas demonstrate major cortical connectivity through the inner-layered CB and the superior area of the brain through the outer-layered CB. From the results, we designed a new CB connectivity model considering the double-layered cingulate cortex to provide a more organized insight into the function of CB function and its connectivity with the cerebral cortex.

Geometric and volumetric relationship between human lumbar vertebrae and "Black-bone" MRI-based models.

This study will examine the differences between human lumbar vertebrae, three-dimensional (3D) scans of these bones, 3D models based on 'Black-bone' magnetic resonance imaging (MRI) scans, and 3D-printed models. 3D mesh models were created from the "Black-bone" MRI data from two cadaveric human spines, and then 3D printed. Four models were analysed and compared: anatomic bones, 3D-scanned models, MRI models and 3D-printed models. There was no significant difference between when comparing the average of all measurements between all model types (p=0.81). The mean dice coefficient was 0.91 (SD 0.016) and the mean Hausdorff distance was 0.37mm (SD 0.04mm) when comparing the MRI model to the 3D-scanned model. The mean volumes for the MRI model and the 3D scanned model were 10.42 and 10.04ml (p=0.085), respectively. The 'Black-bone' MRI could be a valid radiation-free alternative to computed tomography for the 3D printing of lumbar spinal biomodels.

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging

This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from healthy controls. Preoperative rfMRI and anatomic magnetic resonance imaging scans were obtained from 63 pediatric patients with refractory epilepsy and 259 pediatric healthy controls. Latency maps of the temporal difference between rfMRI and the global mean signal were calculated using voxel-wise cross-covariance. Healthy control and epilepsy latency z score maps were pseudorandomized and partitioned into training data (60%), validation data (20%), and test data (20%). Healthy control individuals and patients with epilepsy were labeled as negative and positive, respectively. CNN models were then trained with the designated training data. Model hyperparameters were evaluated with a grid-search method. The model with the highest sensitivity was evaluated using unseen test data. Accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve were used to evaluate the ability of the model to classify epilepsy in the test data set. The model with the highest validation sensitivity correctly classified 74% of unseen test patients with 85% sensitivity, 71% specificity, F1 score of 0.56, and an area under the receiver operating characteristic curve of 0.86. Using rfMRI latency data, we trained a CNN model to classify patients with pediatric epilepsy from healthy controls with good performance. CNN could serve as an adjunct in the diagnosis of pediatric epilepsy. Identification of pediatric epilepsy earlier in the disease course could decrease time to referral to specialized epilepsy centers and thus improve prognosis in this population.

Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis.

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.

Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data

The Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in dementia and at-risk populations. In the present work, we compared single and multitask learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based prediction methods ADAS-Cog changes, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between the predicted and observed ADAS-Cog score changes in each diagnostic group, suggesting that T1-weighted MRI has a predictive value for evaluating cognitive decline in the entire AD continuum. We further studied whether correction of the differences in the magnetic field strength of MRI would improve the ADAS-Cog score prediction. The partial least square-based domain adaptation slightly improved the prediction performance, but the improvement was marginal. In summary, this study demonstrated that ADAS-Cog change could be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression due to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.

Clinical Usefulness of Preoperative MCA Anatomical Scanning MRI in Thrombectomy Therapy for Acute Anterior Circulation Vessel Occlusion.

The aim of the present study was to evaluate the usefulness of the T2-weighted three-dimensional sequence method, known as "basi-parallel anatomical scanning (BPAS)-magnetic resonance imaging (MRI)," in demonstrating the running course of the obstructed middle cerebral artery (MCA) before acute mechanical thrombectomy. Patients whose M1 part and internal carotid artery (ICA) were occluded on preprocedural MRA, but well demonstrated on MCA anatomical scanning (MAS)-MRI were enrolled in this study. The MAS-MR images for patients in whom thrombectomy was performed were compared with the post-thrombectomy angiography. We compared the running course of the C1-M2 bifurcation on MAS-MRI and angiography after thrombectomy, and the results were classified into 3 groups (Excellent, Good, and Poor). A total of 13 patients (range: 54-89) were enrolled, among whom 12 underwent thrombectomy. We compared MAS-MRI and post-thrombectomy angiography in 10. On comparison between MAS-MRI and post-procedural angiography, visualization was excellent in six (60%) patients. The mean age was 75.7 years, ranging from 54 to 89, and 6 were males. 3 patients had ICA occlusion and seven had MCA occlusion. MAS-MRI was considered useful to clarify the running course of the MCA before acute mechanical thrombectomy.

Role of Magnetic Resonance Imaging in Defining Rectal Anatomical Landmarks and Primary Rectal Cancer Evaluation

In contemporary practice, magnetic resonance imaging (MRI) has become a useful tool in evaluating rectal and perirectal anatomy. It encourages accurate local staging of rectal cancer and has overall improved presurgical evaluation and planning, leading to reduce local recurrence and more sphincter conserving procedures. This article aims at providing a concise review of rectal MRI technique and anatomy and to relate this to local staging and surgical management. Locally generated images in our practice were reviewed and images that best depict different anatomical landmarks and pathologies were selected and presented. Also, PubMed database was searched for relevant and contemporary existing literature on the subject.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions. Statement of SignificanceThe study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computational brain model from experimental MRE data. The processed MRI, MRE, and tagged MRI data are publicly available at https://www.nitrc.org/projects/bbir/.

Hypermetabolism and impaired cerebrovascular reactivity beyond the standard MRI-identified tumor border indicate diffuse glioma extended tissue infiltration.

BackgroundDiffuse gliomas exhibit diffuse infiltrative growth, often beyond the magnetic resonance imaging (MRI)-detectable tumor lesion. Within this lesion, hypermetabolism and impaired cerebrovascular reactivity (CVR) are found, but its exact distribution pattern into the peritumoral environment is unknown. Our aim was to better characterize the extent of diffuse glioma tissue infiltration, beyond the visible lesion (ie, beyond the T1-contrast-enhancing lesion and/or T2/FLAIR-defined tumor border), with metabolic positron emission tomography (PET), and functional MRI CVR (blood oxygenation-level-dependent CVR [BOLD-CVR]) mapping.MethodsFrom a prospective glioma database, 18 subjects (19 datasets) with diffuse glioma (n = 2 with anaplastic astrocytoma, n = 10 with anaplastic oligodendroglioma, and n = 7 with glioblastoma) underwent a BOLD-CVR and metabolic PET study between February 2016 and September 2019, 7 of them at primary diagnosis and 12 at tumor recurrence. In addition, 19 matched healthy controls underwent an identical BOLD-CVR study. The tumor lesion was defined using high-resolution anatomical MRI. Volumes of interest starting from the tumor lesion outward up to 30 mm were created for a detailed peritumoral PET and BOLD-CVR tissue analysis. Student’s t test was used for statistical analysis.ResultsPatients with diffuse glioma exhibit impaired BOLD-CVR 12 mm beyond the tumor lesion (P = .02) with normalization of BOLD-CVR values after 24 mm. Metabolic PET shows a difference between the affected and contralateral hemisphere of 6 mm (P = .05) with PET values normalization after 12 mm.ConclusionWe demonstrate hypermetabolism and impaired CVR beyond the standard MRI-defined tumor border, suggesting active tumor infiltration in the peritumoral environment.

Imaging in chronic posterior dislocation of the elbow: A rare entity

The elbow is a complex joint, and traumatic dislocation of the elbow is the second most common major joint dislocation in adults more commonly occurring in athletes. Initial investigation of the suspected elbow dislocation includes radiographs in anteroposterior, lateral and oblique views. This case report describes chronic posterior dislocation in a 58-year-old woman and also describes the magnetic resonance imaging anatomy of the elbow joint. Timely intervention can alleviate symptoms and can prevent deformity and neurovascular sequelae.