High-resolution T1 Research Articles

Diagnostic value of 11C-Methionine PET-CT imaging in persistent or recurrent Cushing's disease after surgery.

Equivocal or negative pituitary magnetic resonance imaging (MRI) findings raise a significant challenge in the management of persistent or recurrent Cushing's disease (CD), compromising the chances of success of a further transsphenoidal surgery (TSS). The aim of our study was to determine the diagnostic utility of 11C-methionine (MET) positron emission tomography coupled with computerized tomography (PET/CT) in localizing the residual or relapsing corticotroph adenoma. We retrospectively analyzed the results of 11C-MET PET/CT performed in two tertiary medical centers between May 2002 and November 2023 in 22 patients with a persisting or recurrent CD after initial TSS and equivocal or negative pituitary MRI findings. In 15 cases, images obtained from MET PET/CT were also co-registered with high-resolution 3D T1 or FLAIR MRI pituitary imaging. All patients had a proven CD at the time of first surgery. Twenty-two patients were included (18 females and 4 males), with a mean age of 36 years at diagnosis. Initial tumors were mostly microadenomas with a mean maximum tumor diameter of 6.5 mm. Thirteen out of the 22 patients had a suspect anomaly on conventional MRI that could however not be clearly distinguished from postsurgical changes, while the remaining 9 had a negative MRI. A maximal metabolic activity in the suspected area of the tumor residue (SUVmaxT) was found in 14 patients (63.5%; 5/9 patients with negative pituitary MRI and 9/13 with equivocal findings). On the basis of positive imaging, repeat TSS was performed in 12 patients and was successful in 7, while Gamma Knife radiosurgery (GKRS) was performed in 2 patients allowing remission in both (total remission rate of 64%). Among the 5 patients not cured by TSS, the presence of a corticotroph adenoma in the resected tissue was found in 3 cases. Therefore, when positive, 11C-MET PET/CT had a detection rate accuracy of 86% (12/14). Out of the 8 PET-negative patients, exploratory TSS was performed in 2, with no remission, and GKRS was performed in another two patients, with long-term remission in one. 11C-Methionine PET/CT imaging can provide valuable diagnostic information to detect a corticotroph microadenoma in more than half of patients with persistent or recurrent CD and equivocal or negative MRI findings, thereby allowing targeted TSS or radiosurgery with a global success rate of 64% in the selected subgroup with positive imaging.

Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation

PurposeThis work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification.MethodsA total of 76 supratentorial meningioma patients treated with radiotherapy were studied. Gross tumor volumes (GTVs) were contoured by a single experienced radiation oncologist on high-resolution contrast-enhanced T1 MRI scans (T1ce), and both T1 and T1ce images were utilized for segmentation. SPU-Net, an adaptation of U-Net incorporating spherical image projection to map 2D images onto a spherical surface, was proposed. As an equivalence of a nonlinear image transform, projections enhance locoregional details while maintaining the global field of view. By employing multiple projection centers, SPU-Net generates various GTV segmentation predictions, the variance indicating the model’s uncertainty. This uncertainty is quantified on a pixel-wise basis using entropy calculations and aggregated through Otsu’s method for a final segmentation.Results/conclusionThe SPU-Net model poses an advantage over traditional U-Net models by providing a quantitative method of displaying segmentation uncertainty. Regarding segmentation performance, SPU-Net demonstrated comparable results to a traditional U-Net in sensitivity (0.758 vs. 0.746), Dice similarity coefficient (0.760 vs. 0.742), reduced mean Hausdorff distance (mHD) (0.612 cm vs 0.744 cm), and reduced 95% Hausdorff distance (HD95) (2.682 cm vs 2.912 cm). SPU-Net not only is comparable to U-Net in segmentation performance but also offers a significant advantage by providing uncertainty quantification. The added SPU-Net uncertainty mapping revealed low uncertainty in accurate segments (e.g., within GTV or healthy tissue) and higher uncertainty in problematic areas (e.g., GTV boundaries, dural tail), providing valuable insights for potential manual corrections. This advancement is particularly valuable given the complex extra-axial nature of meningiomas and involvement with dural tissue. The capability to quantify uncertainty makes SPU-Net a more advanced and informative tool for segmentation, without sacrificing performance.

Mapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics

Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136 patients with pituitary tumors, annotated for tumor segmentation and accompanied by clinical, radiological and pathological metadata. This diverse dataset captures variations in tumor size, location, and pathological activity, essential for understanding this complex condition. Expert annotations of both the tumor and adjacent carotid arteries ensure precise delineation, facilitating the development of automated segmentation algorithms. Our initiative addresses the need for standardized data in pituitary oncology, fosters collaboration and innovation, and enables the development and benchmarking of workflows that utilize pituitary radiomics for treatment planning and outcome prediction.

Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation

Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by catabolism and abnormal energy metabolism preceding onset of motor symptoms, and previous studies indicated that the disease progression of ALS involves hypothalamic atrophy. Very limited weight loss is observed in patients with PLS, which raises the question of whether there are also less hypothalamic alterations. The purpose of this study was to quantitatively investigate the hypothalamic volume in a group of PLS patients and to compare it with ALS and controls. Recently, we have introduced automatic hypothalamic quantification method based on the use of convolutional neural network (CNN) to reduce human variability and enhance analysis robustness. This CNN of U-Net architecture was applied for automatic segmentation of the hypothalamus and intracranial volume (ICV) to allow adjustments of the hypothalamic volume between subjects with different head sizes respectively. Automatic segmentation and volumetric analysis were performed in high resolution T1 weighted MRI volumes (acquired on a 1.5 T MRI scanner) of 46 PLS patients in comparison to 107 healthy controls and 411 `classical` ALS patients, respectively. Significant hypothalamic volume reduction was observed in PLS (818 ± 73 mm3) when compared to controls (852 ± 77 mm3); significant hypothalamic volume reduction was also confirmed in ALS (823 ± 84 mm3), in support of previous studies. No significant differences were found in normalized hypothalamic volumes between ALS patients and PLS patients at the group level. This unbiased CNN-based hypothalamus volume quantification study demonstrated similarly reduced hypothalamus volume in PLS and ALS patients, despite the clinical phenotypic differences.

Accelerated High-resolution T1- and T2-weighted Breast MRI with Deep Learning Super-resolution Reconstruction.

To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences. Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.5 Tesla MRI included T1w and T2w. Both sequences were acquired in standard resolution (T1S and T2S) and in low-resolution with following DL reconstructions (T1DL and T2DL). For DL reconstruction, two convolutional networks were used: (1) Adaptive-CS-Net for denoising with compressed sensing, and (2) Precise-Image-Net for resolution upscaling of previously downscaled images. Overall image quality was assessed using 5-point-Likert scale (from 1=non-diagnostic to 5=excellent). Apparent signal-to-noise (aSNR) and contrast-to-noise (aCNR) ratios were calculated. Breast Imaging Reporting and Data System (BI-RADS) agreement between different sequence types was assessed. A total of 47 patients were included (mean age, 58±11 years). Acquisition time for T1DL and T2DL were reduced by 51% (44 vs. 90s per dynamic phase) and 46% (102 vs. 192s), respectively. T1DL and T2DL showed higher overall image quality (e.g., 4 [IQR, 4-4] for T1S vs. 5 [IQR, 5-5] for T1DL, P<0.001). Both, T1DL and T2DL revealed higher aSNR and aCNR than T1S and T2S (e.g., aSNR: 32.35±10.23 for T2S vs. 27.88±6.86 for T2DL, P=0.014). Cohen k agreement by BI-RADS assessment was excellent (0.962, P<0.001). DL for denoising and resolution upscaling reduces acquisition time and improves image quality for T1w and T2w breast MRI.

Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females

Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30–60% of the risk for onset. While previous studies have separately associated reductions in gray matter volume (GMV) with childhood abuse and internalizing psychopathology (IP), it is unclear whether abuse and IP differ in their structural abnormalities, and which GMV features are related to abuse and IP at the individual level. In a pooled multisite, multi-investigator sample, 246 child and adolescent females between the ages of 8–18 were recruited into studies of interpersonal violence (IPV) and/or IP (i.e. posttraumatic stress disorder (PTSD), depression, and/or anxiety). Youth completed assessments for IP, childhood abuse history, and underwent high resolution T1 structural MRI. First, we characterized how differences in GMV associated with childhood abuse exposure depend on the presence or absence of IP using voxel-based morphometry (VBM). Next, we trained convolutional neural networks to predict individual psychopathology and abuse experience and estimated the strength and direction of importance of each structural feature in making individual-level predictions using Shapley values. Shapley values were aggregated across the entire cohort, and the top 1% of feature clusters with the highest importance are reported. At a group-level, VBM analyses identified widespread decreases in GMV across the prefrontal cortex, insula, and hippocampus in youth with IP, while abuse experience was specifically associated with increased GMV in the cingulate cortex and supramarginal gyrus. Further, interactions between IP and severity of abuse were identified in the ventral and dorsal prefrontal cortex, anterior cingulate cortex, and thalamus. After extensive training, model tuning, and model evaluation, the neural networks performed above chance when predicting IP (63% accuracy) and abuse experiences (71% accuracy) at the level of the individual. Interestingly, structural regions with the highest importance in making individual IP predictions had a high degree of overlap with group-level patterns. We have identified unique structural correlates of childhood abuse and IP on both the group and individual level with a high degree of overlap, providing evidence that IP and trauma exposure may uniquely and jointly impact child and adolescent structural neurodevelopment. Feature learning may offer power and novelty above and beyond traditional group-level approaches to the identification of biomarkers and a movement towards individualized diagnosis and treatment.

Reliability of Central Vein Sign Imaging With 3T FLAIR* in a Multicenter Study.

The central vein sign (CVS) is a diagnostic imaging biomarker for multiple sclerosis (MS). FLAIR* is a combined MRI contrast that provides high conspicuity for CVS at 3 Tesla (3T), enabling its sensitive and accurate detection in clinical settings. This study evaluated whether CVS conspicuity of 3T FLAIR* is reliable across imaging sites and MRI vendors and whether gadolinium (Gd) contrast increases CVS conspicuity. A cross-sectional, multicenter study recruited adults referred for possible diagnosis of MS at 10 sites. FLAIR* contrast was generated using high-resolution T2*-weighted (acquired pre- and post-injection of Gd) and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) brain images at 3T from two MRI vendors. Lesions and veins were segmented to compute lesion-to-vein contrast-to-noise ratio (CNRlesion-to-vein), a quantitative measure of CVS conspicuity. CNRlesion-to-vein measures for pre- and post-Gd FLAIR* were compared across sites and vendors. Eighty-seven participants from nine sites were included in the analysis. There was no significant difference in mean CNRlesion-to-vein between sites for pre-Gd (p-value=0.07) or post-Gd (p-value=0.27) FLAIR*. There were also no significant differences between vendors for pre-Gd (p-value=0.10) or post-Gd (p-value=0.31) FLAIR*. Patient-level pairwise differences in CNRlesion-to-vein between pre-Gd and post-Gd FLAIR* revealed a significant increase for post-Gd FLAIR* (p-value<0.001). CVS conspicuity on 3T FLAIR* is consistent across imaging sites and MRI vendors. Moreover, Gd-based contrast agent significantly improved CVS conspicuity on 3T FLAIR*. These findings support the implementation of FLAIR* in clinical settings for MS.

Exploring habenular structural connectivity in Parkinson's disease: insights from 7T MRI study.

PD is marked by both motor and non-motor symptoms, with its pathophysiology involving many neural pathways and brain regions beyond the dopaminergic system. While mainly gray matter changes have been noted, white matter changes also exist in PD. Habenula, known for its role in reward processing, mood regulation, motor functions, and cognition, is of interest due to its connection to mood disorders in PD. This study aims to explore diffusion metrics and structural connectivity changes in the habenula of newly diagnosed PD patients using 7T MRI. 84 PDs and 38 HCs were recruited from Maastricht University Medical Centre. Clinical, demographic, and total Beck Depression Inventory (BDI) scores were recorded. A 7T brain MRI was conducted. Diffusion metrics and structural connectivity were evaluated. The mean diffusion metrics of Hb were not significantly different between the groups. However, in PD patients, there was an increase in mean structural connectivity from the right Hb to the right hippocampus (p = 0.006) and the right fusiform gyrus (p = 0.007). On the left side, enhanced connectivity was observed with the left pallidum (p = 0.040) and left accumbens (p = 0.009). In the PD group, a significant correlation was found between the BDI total score and increased structural connectivity from the right Hb to the left cingulate isthmus (R2 = 0.090, p = 0.003). This pioneering study examines diffusion metrics and structural connectivity of Hb in PD patients using high-resolution 7T MRI. Our findings highlight the habenula's potential role in PD pathophysiology, with altered connectivity suggesting early neurodegenerative or compensatory processes. These results underscore the importance of the habenula as a biomarker for PD and its potential as a therapeutic target.

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes). In this work, we present a super-resolution convolutional neural network, based on previous single-image MRI super-resolution work, that can reconstruct a HR image from 2D LR slices in multiple planes in order to facilitate the extraction of structural biomarkers from routine scans. A multilevel densely connected super-resolution convolutional neural network (mDCSRN) was adapted to take two perpendicular LR scans (e.g., coronal and axial) as tensors and reconstruct a 3D HR image. A training set of 90 HR T1 pediatric head scans from the Adolescent Brain Cognitive Development (ABCD) study was used, with 2D LR images simulated through a downsampling pipeline that introduces motion artifacts, blurring, and registration errors to make the LR scans more realistic to routinely acquired ones. The outputs of the model were compared against simple interpolation in two steps. First, the quality of the reconstructed HR images was assessed using the peak signal-to-noise ratio and structural similarity index compared to baseline. Second, the precision of structure segmentation (using the autocontouring software Limbus AI) in the reconstructed versus the baseline HR images was assessed using mean distance-to-agreement (mDTA) and 95% Hausdorff distance. Three datasets were used: 10 new ABCD images (dataset 1), 18 images from the Children's Brain Tumor Network (CBTN) study (dataset 2) and 6 "real-world" follow-up images of a pediatric head and neck cancer patient (dataset 3). The proposed mDCSRN outperformed simple interpolation in terms of visual quality. Similarly, structure segmentations were closer to baseline images after 3D reconstruction. The mDTA improved to, on average (95% confidence interval), 0.7 (0.4-1.0) and 0.8 (0.7-0.9) mm for datasets 1 and 3 respectively, from the interpolation performance of 6.5 (3.6-9.5) and 1.2 (1.0-1.3) mm. We demonstrate that deep learning methods can successfully reconstruct 3D HR images from 2D LR ones, potentially unlocking datasets for retrospective study and advancing research in the long-term effects of pediatric cancer. Our model outperforms standard interpolation, both in perceptual quality and for autocontouring. Further work is needed to validate it for additional structural analysis tasks.

Locus coeruleus tau is linked to successive cortical tau accumulation.

We investigated the hypothesis that tau burden in the locus coeruleus (LC) correlates with tau accumulation in cortical regions according to the Braak stages and examined whether the relationships differed according to cortical amyloid beta (Aβ) deposition. One hundred and seventy well-characterized participants from an ongoing cohort were included. High-resolution T1, tau positron emission tomography (PET), and amyloid PET were obtained. LC tau burden was significantly linked to global tau in neocortical regions, as was tau in both early Braak stage (stage I/II) and later Braak stage areas. This relationship was significant only in Aβ-positive individuals. While LC tau did not directly impact memory, it was indirectly associated with delayed memory through mediation or moderation pathways. The findings from living human brains support the idea that LC tau closely relates to subsequent cortical tau accumulation, particularly among individuals with pathological Aβ accumulation, and identify LC tau burden as a promising indicator of cognitive trajectories of AD. Tau burden in the LC was significantly associated with cortical tau accumulation. Tau burden in SN or PPN showed no association with cortical tau accumulation. LC tau burden was serially associated with Braak stages. The tau-LC and cortical tau relationship was significant only in the Aβ-positive group. Cortical amyloid's impact on memory worsens with higher tau accumulation in the LC.

Abnormal topological structure of structural covariance networks based on fractal dimension in noise induced hearing loss

The topological attributes of structural covariance networks (SCNs) based on fractal dimension (FD) and changes in brain network connectivity were investigated using graph theory and network-based statistics (NBS) in patients with noise-induced hearing loss (NIHL). High-resolution 3D T1 images of 40 patients with NIHL and 38 healthy controls (HCs) were analyzed. FD-based Pearson correlation coefficients were calculated and converted to Fisher’s Z to construct the SCNs. Topological attributes and network hubs were calculated using the graph theory. Topological measures between groups were compared using nonparametric permutation tests. Abnormal connection networks were identified using NBS analysis. The NIHL group showed a significantly increased normalized clustering coefficient, normalized characteristic path length, and decreased nodal efficiency of the right medial orbitofrontal gyrus. Additionally, the network hubs based on betweenness centrality and degree centrality were both the right transverse temporal gyrus and left parahippocampal gyrus in the NIHL group. The NBS analysis revealed two subnetworks with abnormal connections. The subnetwork with enhanced connections was mainly distributed in the default mode, frontoparietal, dorsal attention, and somatomotor networks, whereas the subnetwork with reduced connections was mainly distributed in the limbic, visual, default mode, and auditory networks. These findings demonstrate the abnormal topological structure of FD-based SCNs in patients with NIHL, which may contribute to understand the complex mechanisms of brain damage at the network level, providing a new theoretical basis for neuropathological mechanisms.

Time-efficient, high-resolution 3T whole-brain relaxometry using Cartesian 3D MR Spin TomogrAphy in Time-Domain (MR-STAT) with cerebrospinal fluid suppression.

Current three-dimensional (3D) MR Spin TomogrAphy in Time-Domain (MR-STAT) protocols use transient-state, gradient-spoiled gradient-echo sequences that are prone to cerebrospinal fluid (CSF) pulsation artifacts when applied to the brain. This study aims to develop a 3D MR-STAT protocol for whole-brain relaxometry that overcomes the challenges posed by CSF-induced ghosting artifacts. We optimized the flip-angle train within the Cartesian 3D MR-STAT framework to achieve two objectives: (1) minimization of the noise level in the reconstructed quantitative maps, and (2) reduction of the CSF-to-white-matter signal ratio to suppress CSF-associated pulsation artifacts. The optimized new sequence was tested on a gel/water phantom for accuracy evaluation of the quantitative maps, and on healthy volunteers to explore the effectiveness of the CSF artifact suppression and robustness of the new protocol. An optimized sequence with high parameter-encoding capability and low CSF signal response was proposed and validated in the gel/water phantom experiment. From in vivo experiments with 5 volunteers, the proposed CSF-suppressed sequence produced quantitative maps with no CSF artifacts and showed overall greatly improved image quality compared with the baseline sequence. Statistical analysis indicated low intersubject and interscan variability for quantitative parameters in gray matter and white matter (1.6%-2.4% for T1 and 2.0%-4.6% for T2), demonstrating the robustness of the new sequence. We present a new 3D MR-STAT sequence with CSF suppression that effectively eliminates CSF pulsation artifacts. The new sequence ensures consistently high-quality, 1-mm3 whole-brain relaxometry within a rapid 5.5-min scan time.

Plasma p-tau181 and GFAP reflect 7T MR-derived changes in Alzheimer's disease: A longitudinal study of structural and functional MRI and MRS.

Associations between longitudinal changes of plasma biomarkers and cerebral magnetic resonance (MR)-derived measurements in Alzheimer's disease (AD) remain unclear. In a study population (n=127) of healthy older adults and patients within the AD continuum, we examined associations between longitudinal plasma amyloid beta 42/40 ratio, tau phosphorylated at threonine 181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and 7T structural and functional MR imaging and spectroscopy using linear mixed models. Increases in both p-tau181 and GFAP showed the strongest associations to 7T MR-derived measurements, particularly with decreasing parietal cortical thickness, decreasing connectivity of the salience network, and increasing neuroinflammation as determined by MR spectroscopy (MRS) myo-inositol. Both plasma p-tau181 and GFAP appear to reflect disease progression, as indicated by 7T MR-derived brain changes which are not limited to areas known to be affected by tau pathology and neuroinflammation measured by MRS myo-inositol, respectively. This study leverages high-resolution 7T magnetic resonance (MR) imaging and MR spectroscopy (MRS) for Alzheimer's disease (AD) plasma biomarker insights. Tau phosphorylated at threonine 181 (p-tau181) and glial fibrillary acidic protein (GFAP) showed the largest changes over time, particularly in the AD group. p-tau181 and GFAP are robust in reflecting 7T MR-based changes in AD. The strongest associations were for frontal/parietal MR changes and MRS neuroinflammation.

Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation

There is growing interest in research on segmentation for the vestibular schwannoma (VS) and cochlea using high-resolution T2 (hrT2) imaging over contrast-enhanced T1 (ceT1) imaging due to the contrast agent side effects. However, the hrT2 imaging remains a problem of insufficient annotated data, which is fatal for building more robust segmentation models. To address the issue, recent studies have adopted unsupervised domain adaptation approaches that translate ceT1 images to hrT2 images. However, previous studies did not consider the size and visual characteristics of the target objects, such as VS and cochlea, during image translation. Specifically, those works simply performed normalization on the entire image without considering its significant impact on the quality of the translated images. These approaches tend to erase the small target objects, making it difficult to preserve the structure of these objects when generating pseudo-target images. Furthermore, they may also struggle to accurately reflect the unique style of the target objects within the images. Therefore, we propose a target-aware unsupervised domain adaptation framework, designed for translating target objects, each tailored to their unique visual characteristics and size using target-aware normalization. We demonstrate the superiority of the proposed framework on a publicly available challenge dataset. Codes are available at https://github.com/Bokyeong-Kang/TANQ.

The association between precuneus cortex thickness and mild behavioral impairment in patients with mild stroke.

The objective of this research was to examine the association between precuneus cortex thickness and mild behavioral impairment (MBI) in patients with mild stroke. Seventy-two patients were evaluated by high-resolution 3T magnetic resonance and the mild behavioral impairment checklist (MBI-C). To determine the association between precuneus cortex thickness and MBI, we adjusted for demographics, vascular risk factors, and laboratory examination indicators in logistic regression analysis. In addition, we used mendelian randomization to further study the association through genetic databases. Of the 72 mild stroke patients in this study, 26 had MBI. We found a strong negative connection between precuneus cortex thickness and MBI after adjusting for any confounding variables. In patients with an initial mild stroke, the thinner the precuneus cortex, the higher the risk of MBI (OR: 0.02; 95% CI: 0.00-0.39; P < 0.05). Our study has uncovered a significant negative association between the thickness of the precuneus cortex and MBI. This finding provides a novel viewpoint for the radiological diagnosis of MBI, thereby augmenting the contribution of imaging to the diagnostic process of MBI and advancing the prediction of dementia. Specifically, in patients who have suffered mild stroke, a reduction in the cortical thickness of the precuneus has been pinpointed as crucial radiographic evidence of preclinical cognitive impairment. This insight could potentially facilitate earlier detection and intervention strategies for cognitive decline.

NIMG-26. GLIOBLASTOMA TISSUE INVASION MAPPING WITH NOVEL HYPOXIA-TARGETED BLOOD OXYGENATION-LEVEL DEPENDENT MRI

Abstract BACKGROUND Glioblastoma is a devastating incurable malignant primary brain tumor exhibiting an erratic growth pattern and brain tissue invasion exceeding the typically depicted contrast-enhancing lesion on MRI. This behaviour is strongly associated with the presence of aberrant vasculature. Advanced clinical neuroimaging techniques, however, have not provided convincing imaging markers to determine glioblastoma tissue infiltration and monitor treatment effects. We investigated a new imaging technique based on blood oxygenation-level dependent (BOLD) MRI contrast with precise hypoxic respiratory targeting in order to better determine tumor tissue invasion not restricted to the typically depicted focal glioblastoma lesion on contrast-enhanced MRI. METHODS A computer-controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during echo-planar-imaging acquisition in patients with brain tumors.8,9 Data were preprocessed using SPM and in-house written MATLAB scripts. The induced BOLD signal changes were used to calculate %BOLD signal change during hypoxic stimulus with respect to the baseline, contrast to noise (CNR) ratio and goodness-of-fit (Rsquared) for each voxel in the whole-brain and automatically segmented region of interest (contrast-enhancement, necrosis, edema) and overlayed using color-maps onto anatomical high resolution T1. RESULTS Hypoxic targeting during gas modulation could be performed with high reproducibility, caused no discomfort in patients with brain tumors and was able to induce strong signal change during hypoxic stimulus with high contrast-noise ratio and Rsquared. In particular, when compared with other tumors, BOLD hypoxia glioblastoma tissue invasion maps show very distinct hypoxic signal responses in some cases extending beyond the focal -contrast-enhancing- glioblastoma lesion. CONCLUSION The ability to map at voxel level BOLD hypoxia signal patterns may determine a novel therapeutic imaging marker based on structural vascular alterations in the tumour tissue infiltration zone before these become appreciable with standard neuroimaging. This may support a refinement and tailoring of tumor resection and improve follow-up after resection and during treatment i.e. chemoradiotherapy.

Distinct brain and neurocognitive transformations after bariatric surgery: a pilot study.

Obese patients have worse outcomes after surgery and are at increased risk for perioperative neurocognitive disorders (PND). Our aim was to detail the cognitive trajectories of patients undergoing bariatric surgery (BS) and map distinct structural brain changes using magnetic resonance imaging (MRI) to better understand the association between the vulnerable brain, surgery, and the arc of PND. Prospective pilot study with longitudinal comprehensive cognitive assessments and MRI were performed on obese patients scheduled for BS. We analyzed baseline cognitive function and high-resolution T1-/T2-weighted brain images on 19 obese patients [age, 54 (9) years, BMI, 40 (36, 42) kg m-2] and compared with 50 healthy control subjects [age, 52 (6) years; BMI, 25 (24, 27) kg m-2]. Patients were evaluated within five days of BS (baseline), immediately after (within 48h), and follow up at six months. At baseline, obese patients had significant brain tissue changes seen in MRI and decreased cognitive scores compared to controls (MoCA 26 vs 28, P = 0.017). Surgery induced further gray matter volume and brain tissue changes along with reduced cognitive scores within the immediate postoperative period (MoCA 26 vs 24, P < 0.001). At six months, we observed reversal of brain alterations for most patients and a concomitant rebound of cognitive scores to patient's baseline status. Bariatric surgery resulted in worsening of preexisting brain structural integrity and lower cognitive function for obese patients compared to baseline. These distinct brain lesions are consistent with specific domains of cognition. Most of these changes reverted to patient's baseline condition within six months after surgery.

Magnetic Resonance Imaging Diagnosis of Spinal Longitudinal Epidural Collections in a Spontaneous Spinal Dural Breach and Successful Surgical Treatment

Spontaneous intracranial hypotension is generally secondary to cerebrospinal fluid leaks. Spinal magnetic resonance imaging is an important part of the diagnostic workup, especially to display spinal longitudinal epidural collections. Other causes of leaks include nerve root sleeve tear and cerebrospinal fluid-venous fistula. We propose a standard magnetic resonance imaging protocol including sagittal T1, T2-weighted, and T2 fat-saturated sequences, with addition of high-resolution three-dimensional T2 images to improve detection of spinal longitudinal epidural collections and help locate the dural breach level. Spine computed tomography may prove useful in detecting a bony spur that may cause the dural breach. Treatment of identified dural breaches includes targeted epidural blood patch or neurosurgery in case of blood patch inefficacy. Few cases of successful surgical treatments of dural breaches have been reported with complete regression of spinal longitudinal epidural collections; thus more studies will be valuable to help establish guidelines in this pathology.

High-resolution 7T fMRI reveals the visual zone of the human claustrum

Abstract The claustrum is a thin grey matter structure located between the insular cortex and the putamen. The function of the claustrum is largely unknown with diverse hypotheses ranging from multisensory integration and consciousness to attention and cognitive control. Much research on the function of the claustrum relies on invasive techniques in animal models, as the claustrum’s uniquely thin shape makes it difficult to image non-invasively in human subjects. In the current proof-of-concept study, we used high-resolution ultra-high field (7 Tesla) functional magnetic resonance imaging (fMRI) to measure activity in the human claustrum during the processing of naturalistic stimuli. We presented short video clips as visual only, auditory only, or audiovisual conditions while participants performed a central fixation task. We found distinct visual responses in both the left and the right claustrum at a consistent spatial location across participants, hemispheres, and sessions. We also found deactivations in response to auditory stimulation. These deactivations were confined to the right claustrum and did not overlap with visual activity. The deactivation in response to auditory stimulation demonstrates the complexity of the claustrum’s functional organization and suggests functional differentiation within the claustrum. This is the first study to demonstrate sensory-specific effects within the human claustrum. It opens the possibility for studying the claustrum’s role in higher-level aspects of sensory processing in humans.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.