Multimodal Imaging Analysis Research Articles

Multimodal Image Fusion in Clinical Research

With the advancement in medical imaging, far reaching changes are perceptible in clinical analysis. Most of the diagnosed medical evaluation is a resultant of imaging or it is in close synchronization with imaging techniques. This promotes the need to closely read, evaluate and collaborate images. Medical image fusion is a technique to collaborate the clinical images acquired from one or many modalities. Multimodal image analysis during fusion well capitalizes the strength of each medical modality. Incorporating the features from multimodal input images thus holds added potential to abet better-quality diagnosis. Medical image fusion is an intricate task specially when high quality fused image, possessing all relevant information and reasonable operating speed is aimed. Many efforts have been undertaken in this field resulting in diverse research approaches. Image fusion can be performed using medical images obtained from single modality or from multiple modalities. This paper has been designed collaborating the work based on multimodal medical images in multiscale image fusion domain. When the same region, organ or a tissue is captured from various different perceptions, complimentary information is maximized and diagnostic value is reinforced. The fusion framework using Mexican Hat wavelet is proposed using adaptive median filtering detailing each executable fusion block. The Fusion techniques, pre and post processing aspects and evaluation mechanisms are illustrated from literature. The drifts of researchers from single processing to multiple processing hybrid techniques are discussed. The medical modality aspects are detailed. These may provide as a valuable reference to understand the image fusion trade-offs comprehensively with future viabilities.

Comparison of feature point detectors for multimodal image registration in plant phenotyping.

With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in “difficult” image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.

Gene-Related Cerebellar Neurodegeneration in SCA3/MJD: A Case-Controlled Imaging-Genetic Study.

Background: Spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) is one of the nine polyglutamine (polyQ) diseases and is caused by a CAG repeat expansion within the coding sequence of the ATXN3 gene. Few multimodal imaging analyses of the macro- and micro-structural changes have been performed.Methods: In the present study, we recruited 31 genetically-confirmed symptomatic SCA3/MJD patients and 31 healthy subjects as controls for a multimodal neuroimaging study using structural magnetic resonance imaging (sMRI), proton magnetic resonance spectroscopy (1H-MRS) and diffusion tensor imaging (DTI).Results: The SCA3/MJD patients displayed a significantly reduced of gray matter volume in the cerebellum, pons, midbrain and medulla, as well as inferior frontal gyrus and insula, and left superior frontal gyrus. The total International Cooperative Ataxia Rating Scale (ICARS) score was inversely correlated with the gray matter volume in the cerebellar culmen, pons and midbrain. The numbers of CAG repeats in the expanded alleles were inversely correlated with the gray matter in the cerebellar culmen. NAA/Cr and NAA/Cho ratio in the middle cerebellar peduncles, dentate nucleus, cerebellar vermis, and thalamus in the SCA3/MJD patients were significantly reduced when compared to that in the normal controls, suggesting neurochemical alterations in cerebellum in the SCA3/MJD patients. Tract-Based Spatial Statistics (TBSS) analysis revealed significant lower volume and mean FA values of the cerebellar peduncles, which inversely correlated with the total scores of ICARS in our patients.Conclusions: In this study, we demonstrated cerebellar degeneration in SCA3/MJD based on tissue volume, neurochemistry, and tissue microstructure. Moreover, the associations between the clinical measures, cerebellar degeneration and genetic variation support a distinct genotype-phenotype relationship in SCA3/MJD.

Machine learning for radiomics-based multimodality and multiparametric modeling.

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in clinical practice and research studies. Previously, the application of multimodality imaging in oncology has been mainly related to combining anatomical and functional imaging to improve diagnostic specificity and/or target definition, such as positron emission tomography/computed tomography (PET/CT) and single-photon emission CT (SPECT)/CT. More recently, the fusion of various images, such as multiparametric magnetic resonance imaging (MRI) sequences, different PET tracer images, PET/MRI, has become more prevalent, which has enabled more comprehensive characterization of the tumor phenotype. In order to take advantage of these valuable multimodal data for clinical decision making using radiomics, we present two ways to implement the multimodal image analysis, namely radiomic (handcrafted feature) based and deep learning (machine learned feature) based methods. Applying advanced machine (deep) learning algorithms across multimodality images have shown better results compared with single modality modeling for prognostic and/or prediction of clinical outcomes. This holds great potentials for providing more personalized treatment for patients and achieve better outcomes.

Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer.

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications (e.g., detection and segmentation) requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality's features across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions (lungs, mediastinum, tumors) with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion (fused inputs (FS), multi-branch (MB) techniques, and multichannel (MC) techniques) and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, p < 0:05) than the fusion baselines (FS: 99.00%, MB: 99.08%, TC: 98.92%) and a significantly higher Dice score (63.85%) than recent PET-CT tumor segmentation methods.

326-OR: A Novel Machine Learning Analysis of Brain Multimodal Magnetic Resonance Imaging Classifies Painful Diabetic Neuropathic Pain Severity with High Accuracy

Using advanced MR neuroimaging we have demonstrated altered brain structure and functional connectivity that could serve as a potential Central Pain Signature (CPS) for painful diabetic neuropathy (DN). However, the key challenge is how to apply this potential biomarker for routine diagnostic purposes. The aim of this study was to examine if the CPS can accurately classify painful DN patients based on a patient’s pain severity. Methods: 53 painful DN patients underwent detailed clinical and neurophysiological assessments. The NTSS-6, a validated questionnaire that measures both the frequency and the intensity of neuropathic pain was used to assess pain severity. Patients were divided into high pain (NTSS-6 score &amp;gt;7) and low pain (&amp;lt;7). All subjects underwent brain volume and resting state functional MRI at 3T. We used novel and validated machine learning algorithms [support vector machines] to differentiate pain severity based on functional connectivity and structural/volume data. A nested cross validation method was used to determine the accuracy (sensitivity and specificity). Results: There was no age or gender difference (p &amp;gt; 0.05) between the high and low pain groups. The CPS classified painful DN patients based on their pain intensity with 94% accuracy (AUC 0.98). The positive and negative predictive values were 0.80 and 1.00 respectively. The F1 scores for predicting high pain and low pain were 0.89 and 0.96 respectively. Brain regions identified as the best classifier were the left and right postcentral gyri, thalami, and anterior and posterior cingulate cortices. Conclusions: This novel study demonstrates that a simple, 15-minute MR brain scan can accurately classify painful DN patients according to pain intensities with high accuracy. This assessment tool has a great potential as a biomarker of diabetic neuropathic pain and may serve as a target for future trials of analgesic compounds for painful DN. Disclosure D. Selvarajah: None. K. Teh: None. G.P. Sloan: None. P. Shillo: None. I.D. Wilkinson: None. S. Tesfaye: Advisory Panel; Self; Mitsubishi Tanabe Pharma Corporation, WÖRWAG Pharma. Speaker's Bureau; Self; AstraZeneca, Merck &amp; Co., Inc., NAPP Pharmaceuticals Limited, Neurometrix, Novo Nordisk Inc., Pfizer Inc. Other Relationship; Self; Impeto Medical. Funding European Foundation for the Study of Diabetes

Multimodal Imaging Analysis of Acute Syphilitic Posterior Placoid Chorioretinitis: A Case Report.

The authors report a multimodal imaging analysis of a case of acute syphilitic posterior placoid chorioretinitis (ASPPC) occurring in a 51-year-old man. Best-corrected visual acuity (BCVA) was 0.5 and 0.8 in the right and left eyes, respectively. The authors performed spectral-domain optical coherence tomography, fundus autofluorescence, fluorescein angiography, and indocyanine green angiography. All of the examinations were suggestive of the diagnosis of ASPPC, a rare manifestation of syphilis that has distinctive anatomical characteristics that are detectable early on with multimodal imaging. Moreover, serological tests were positive for syphilis infection, so the patient received intravenous penicillin G for 14 days. Final BCVA was 1.0 in the right eye and 0.9 in the left eye. [Ophthalmic Surg Lasers Imaging Retina. 2019;50:e179-e184.].

Multi-modal Imaging Analysis in the Patients with Traumatic Maculopathy

Multi-modal Imaging Analysis in the Patients with Traumatic Maculopathy

Comparison and extension of three methods for automated registration of multimodal plant images

With the introduction of high-throughput multisensory imaging platforms, the automatization of multimodal image analysis has become the focus of quantitative plant research. Due to a number of natural and technical reasons (e.g., inhomogeneous scene illumination, shadows, and reflections), unsupervised identification of relevant plant structures (i.e., image segmentation) represents a nontrivial task that often requires extensive human-machine interaction. Registration of multimodal plant images enables the automatized segmentation of ’difficult’ image modalities such as visible light or near-infrared images using the segmentation results of image modalities that exhibit higher contrast between plant and background regions (such as fluorescent images). Furthermore, registration of different image modalities is essential for assessment of a consistent multiparametric plant phenotype, where, for example, chlorophyll and water content as well as disease- and/or stress-related pigmentation can simultaneously be studied at a local scale. To automatically register thousands of images, efficient algorithmic solutions for the unsupervised alignment of two structurally similar but, in general, nonidentical images are required. For establishment of image correspondences, different algorithmic approaches based on different image features have been proposed. The particularity of plant image analysis consists, however, of a large variability of shapes and colors of different plants measured at different developmental stages from different views. While adult plant shoots typically have a unique structure, young shoots may have a nonspecific shape that can often be hardly distinguished from the background structures. Consequently, it is not clear a priori what image features and registration techniques are suitable for the alignment of various multimodal plant images. Furthermore, dynamically measured plants may exhibit nonuniform movements that require application of nonrigid registration techniques. Here, we investigate three common techniques for registration of visible light and fluorescence images that rely on finding correspondences between (i) feature-points, (ii) frequency domain features, and (iii) image intensity information. The performance of registration methods is validated in terms of robustness and accuracy measured by a direct comparison with manually segmented images of different plants. Our experimental results show that all three techniques are sensitive to structural image distortions and require additional preprocessing steps including structural enhancement and characteristic scale selection. To overcome the limitations of conventional approaches, we develop an iterative algorithmic scheme, which allows it to perform both rigid and slightly nonrigid registration of high-throughput plant images in a fully automated manner.

In vivo localization of cortical areas using a 3D computerized atlas of the marmoset brain.

We created a volumetric template of the marmoset (Callithrix jacchus) brain, which enables localization of the cortical areas defined in the Paxinos et al. (The marmoset brain in stereotaxic coordinates. Elsevier Academic Press, Cambridge, 2012) marmoset brain atlas, as well as seven broader cortical regions (occipital, temporal, parietal, prefrontal, motor, limbic, insular), different brain compartments (white matter, gray matter, cerebro-spinal fluid including ventricular spaces), and various other structures (brain stem, cerebellum, olfactory bulb, hippocampus). The template was designed from T1-weighted MR images acquired using a 3 T MRI scanner. It was based on a single fully segmented marmoset brain image, which was transported onto the mean of 13 adult marmoset brain images using a diffeomorphic strategy that fully preserves the brain topology. In addition, we offer an automatic segmentation pipeline which fully exploits the proposed template. The segmentation pipeline was quantitatively assessed by comparing the results of manual and automated segmentations. An associated program, written in Python, can be used from a command-line interface, or used interactively as a module of the 3DSlicer software. This program can be applied to the analysis of multimodal images, to map specific cortical areas in lesions or to define the seeds for further tractography analyses.

Data Curation for Preclinical and Clinical Multimodal Imaging Studies

PurposeIn biomedical research, imaging modalities help discover pathological mechanisms to develop and evaluate novel diagnostic and theranostic approaches. However, while standards for data storage in the clinical medical imaging field exist, data curation standards for biomedical research are yet to be established. This work aimed at developing a free secure file format for multimodal imaging studies, supporting common in vivo imaging modalities up to five dimensions as a step towards establishing data curation standards for biomedical research.ProceduresImages are compressed using lossless compression algorithm. Cryptographic hashes are computed on the compressed image slices. The hashes and compressions are computed in parallel, speeding up computations depending on the number of available cores. Then, the hashed images with digitally signed timestamps are cryptographically written to file. Fields in the structure, compressed slices, hashes, and timestamps are serialized for writing and reading from files. The C++ implementation is tested on multimodal data from six imaging sites, well-documented, and integrated into a preclinical image analysis software.ResultsThe format has been tested with several imaging modalities including fluorescence molecular tomography/x-ray computed tomography (CT), positron emission tomography (PET)/CT, single-photon emission computed tomography/CT, and PET/magnetic resonance imaging. To assess performance, we measured the compression rate, ratio, and time spent in compression. Additionally, the time and rate of writing and reading on a network drive were measured. Our findings demonstrate that we achieve close to 50 % reduction in storage space for μCT data. The parallelization speeds up the hash computations by a factor of 4. We achieve a compression rate of 137 MB/s for file of size 354 MB.ConclusionsThe development of this file format is a step to abstract and curate common processes involved in preclinical and clinical multimodal imaging studies in a standardized way. This work also defines better interface between multimodal imaging modalities and analysis software.

Multimodal Imaging Analyses of Brain Hippocampal Formation Reveal Reduced Cu and Lipid Content and Increased Lactate Content in Non-Insulin-Dependent Diabetic Mice.

Non-insulin-dependent diabetes mellitus (NIDDM) is reported to increase the risk of cognitive impairment and dementia. However, the underlying mechanisms are not fully understood. While the brain homeostasis of metals and lipids is pivotal to maintaining energy metabolism and redox homeostasis for healthy brain function, no studies have reported hippocampal metal and biochemical changes in NIDDM. Therefore, we here utilized direct spectroscopic imaging to reveal the elemental distribution within the hippocampal subregions of an established murine model of NIDDM, db/db mice. In 26-week-old insulin resistant db/db mice, X-ray fluorescence microscopy revealed that the Cu content within the dentate gyrus and CA3 was significantly greater than that of the age-matched nondiabetic control mice. In addition, Fourier transform infrared (FTIR) spectroscopy analysis indicated a significant increase in the abundance of lactate within the corpus callosum (CC), dentate gyrus, CA1, and CA3 regions of diabetic db/db mice compared to that of the control, indicating altered energy metabolism. FTIR analysis also showed a significant decrease in the level of lipid methylene and ester within the CC of db/db mice. Furthermore, immunomicroscopy analyses demonstrated the increase in the level of glial fibrillary acidic protein expression and peri-vascular extravasation of IgG, indicating astrogliosis and blood-brain barrier dysfunction, respectively. These data suggest that astrogliosis-induced alterations in the supply of Cu, lipids, and energy substrates may be involved in the mechanisms of NIDDM-associated cognitive decline.

Deep Learning-based Image Segmentation on Multimodal Medical Imaging.

Multi-modality medical imaging techniques have been increasingly applied in clinical practice and research studies. Corresponding multi-modal image analysis and ensemble learning schemes have seen rapid growth and bring unique value to medical applications. Motivated by the recent success of applying deep learning methods to medical image processing, we first propose an algorithmic architecture for supervised multi-modal image analysis with cross-modality fusion at the feature learning level, classifier level, and decision-making level. We then design and implement an image segmentation system based on deep Convolutional Neural Networks (CNN) to contour the lesions of soft tissue sarcomas using multi-modal images, including those from Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Positron Emission Tomography (PET). The network trained with multi-modal images shows superior performance compared to networks trained with single-modal images. For the task of tumor segmentation, performing image fusion within the network (i.e. fusing at convolutional or fully connected layers) is generally better than fusing images at the network output (i.e. voting). This study provides empirical guidance for the design and application of multi-modal image analysis.

Multimodal Assessment of Corneal Erosions Using Optical Coherence Tomography and Automated Grading of Fluorescein Staining in a Rabbit Dry Eye Model.

PurposeTo evaluate the potential use of anterior segment spectral domain optical coherence tomography (AS-SD-OCT) combined with an automated grading of fluorescein staining for assessment of corneal erosions in a rabbit short-term dry eye model.MethodsTwenty-one New Zealand white rabbits were anesthetized and eyes were kept open for 140 minutes to induce acute corneal desiccation. Rectangular scans of the cornea were performed using Spectralis AS-SD-OCT. Total corneal thickness, corneal epithelial thickness, and the percentage of epithelial erosion area (PEEA) were evaluated. Corneas were stained with fluorescein and graded automatically using EpiView and semi-automatically using ImageJ. Spearman's rank-order correlations were calculated to compare the AS-SD-OCT PEEA and the two corneal staining scores.ResultsEye desiccation resulted in corneal epithelium erosions that covered 0.67% to 14.2% of the central cornea (mean ± SD: 3.95% ± 3.2%) by AS-SD-OCT. The percentage of corneal area positively stained with fluorescein ranged from 0.24% to 38.01% (mean ± SD: 12.24% ± 9.7%) by using ImageJ, correlating with the AS-SD-OCT PEEA (Spearman's ρ, 0.574; P = 0.007). The EpiView score ranged from 0.5 to 10.17 and was better correlated with the AS-SD-OCT PEEA score (Spearman's ρ, 0.795; P = 0.000017).ConclusionsOur study suggests that multimodal analysis of AS-SD-OCT and grading of fluorescein staining using EpiView software may enable quantitative assessment of corneal epithelial erosions in a rabbit short-term dry eye model.Translational RelevanceThis multimodal imaging analysis may be applied for evaluation of superficial punctate keratitis associated with dry eye.

Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization

PurposeWe have developed a multi-modal imaging approach for SIRT, combining 99mTc-MAA SPECT/CT and/or 90Y PET, 18F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required.MethodIn this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment 18F-FDG PET/CT, 99mTc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image.ResultOur methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry.The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe).ConclusionThis methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients.

Best vitelliform macular dystrophy in a large Brazilian family

BackgroundTo describe the clinical and multimodal imaging findings of a Brazilian family with Best vitelliform macular dystrophy.MethodsA retrospective chart review of a Brazilian family was conducted and complementary fundus images (color photography, autofluorescence, fluorescein angiography and optical coherence tomography) were analyzed.ResultsSeven patients had typical macular lesions at different stages of Best vitelliform macular dystrophy. Electrooculography was performed in two of them and showed abnormal Arden ratio. The pedigree strongly suggests an autosomal dominant inheritance. Low visual acuity was mainly associated with advanced age, retinal pigment epithelium atrophy, and photoreceptors damage. However, yellow subretinal deposits were evidenced in patients with better visual acuity.ConclusionWe present the largest case series of a Brazilian family with Best vitelliform macular dystrophy. Multimodal imaging analysis is important to determine retinal abnormalities. Retinal pigment epithelium atrophy and loss of photoreceptors outer segments seem to be a late but important finding related to severe visual acuity impairment.

Phenotype of macular corneal dystrophy in Labrador Retrievers: A multicenter study.

To describe the phenotype of canine macular corneal dystrophy (MCD) including the clinical presentation, multimodal ocular imaging, histopathology, and ultrastructural analysis in ten Labrador Retrievers. Multicentered data collection. Labrador Retrievers affected by MCD were presented between the age of 4.5 and 6years of age with a history of cloudy eyes and/or visual impairment. Findings on ophthalmic examination included a diffuse haze of the corneal stroma and multiple, well-demarcated, off-white to yellow-brown, punctate corneal opacities heterogeneous in size. Corneal vascularization developed in most dogs as the disease progressed. Disease progression was associated with increased density of the corneal haze as well as increased number and size of the focal opacities and dogs developed significant visual impairment. Spectral domain-optical coherence tomography revealed multifocal hyper-reflective regions within the stroma. In vivo confocal microscopy revealed marked alterations in reflectivity throughout the entire stroma. Normal keratocytes could not be identified in affected areas. Histopathology showed stromal collagen fibers separated by acidophilic granular material on hematoxylin and eosin stain. The material stained with periodic acid-Schiff and colloidal iron stain but not with Masson trichrome stain, confirming the accumulation of glycosaminoglycans. On electron microscopic ultrastructural examination, keratocytes presented with vacuolated rough endoplasmic reticulum and multiple electron dense cytoplasmic inclusions. In areas keratocytes appeared ruptured, with cell organelles and proteinaceous material grouped together between collagen fibers. MCD in Labrador Retrievers has similarities with the human counterpart of the condition and is an important differential diagnosis in dogs with corneal disease.

Multimodal imaging analyses in patients with genetic and sporadic forms of small vessel disease

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is thought to be a pure genetic form of subcortical vascular cognitive impairment (SVCI). The aim of this study was to compare white matter integrity and cortical thickness between typical CADASIL, a genetic form, and two sporadic forms of SVCI (with NOTCH3 and without NOTCH3 variants). We enrolled typical CADASIL patients (N = 11) and SVCI patients [with NOTCH3 variants (N = 15), without NOTCH3 variants (N = 101)]. To adjust the age difference, which reflects the known difference in clinical and radiologic courses between typical CADASIL patients and SVCI patients, we constructed a W-score of measurement for diffusion tensor image and cortical thickness. Typical CADASIL patients showed more frequent white matter hyperintensities in the bilateral posterior temporal region compared to SVCI patients (p < 0.001, uncorrected). We found that SVCI patients, regardless of the presence of NOTCH3 variants, showed significantly greater microstructural alterations (W-score, p < 0.05, FWE-corrected) and cortical thinning (W-score, p < 0.05, FDR-corrected) than typical CADASIL patients. In this study, typical CADASIL and SVCI showed distinct anatomic vulnerabilities in the cortical and subcortical structures. However, there was no difference between SVCI with NOTCH3 variants and SVCI without NOTCH3 variants.

Regional brain atrophy in gray and white matter is associated with cognitive impairment in Myotonic Dystrophy type 1.

Myotonic Dystrophy type 1 (DM1) is a slowly progressive myopathy characterized by varying multisystemic involvement. Several cerebral features such as brain atrophy, ventricular enlargement, and white matter lesions (WMLs) have frequently been described. The aim of this study is to investigate the structural organization of the brain that defines the disease through multimodal imaging analysis, and to analyze the relation between structural cerebral changes and DM1 clinical and neuropsychological profiles. 31 DM1 patients and 57 healthy controls underwent an MRI scan protocol, including T1, T2 and DTI. Global gray matter (GM), global white matter (WM), and voxel-level Voxel Based Morphometry (VBM) and voxel-level microstructural WM abnormalities through Diffusion Tensor Imaging (DTI) were assessed through group comparisons and linear regression analysis with age, degree of muscular impairment (MIRS score), CTG expansion size and neuropsychological outcomes from a comprehensive assessment. Compared with healthy controls, DM1 patients showed a reduction in both global GM and WM volume; and further regional GM decrease in specific primary sensory, multi-sensory and association cortical regions. Fractional anisotropy (FA) was reduced in both total brain and regional analysis, being most marked in frontal, paralimbic, temporal cortex, and subcortical regions. Higher ratings on muscular impairment and longer CTG expansion sizes predicted a greater volume decrease in GM and lower FA values. Age predicted global GM reduction, specifically in parietal regions. At the cognitive level, the DM1 group showed significant negative correlations between IQ estimate, visuoconstructive and executive neuropsychological scores and both global and regional volume decrease, mainly distributed in the frontal, parietal and subcortical regions. In this study, we describe the structural brain signatures that delineate the involvement of the CNS in DM1. We show that specific sensory and multi-sensory - as well as frontal cortical areas - display potential vulnerability associated with the hypothesized neurodegenerative nature of DM1 brain abnormalities.

Multimodal quantitative magnetic resonance imaging analysis with individualized postprocessing in patients with drug-resistant focal epilepsy and conventional visual inspection negative for epileptogenic lesions

OBJECTIVES:Approximately one-third of candidates for epilepsy surgery have no visible abnormalities on conventional magnetic resonance imaging. This is extremely discouraging, as these patients have a less favorable prognosis. We aimed to evaluate the utility of quantitative magnetic resonance imaging in patients with drug-resistant neocortical focal epilepsy and negative imaging.METHODS:A prospective study including 46 patients evaluated through individualized postprocessing of five quantitative measures: cortical thickness, white and gray matter junction signal, relaxation rate, magnetization transfer ratio, and mean diffusivity. Scalp video-electroencephalography was used to suggest the epileptogenic zone. A volumetric fluid-attenuated inversion recovery sequence was performed to aid visual inspection. A critical assessment of follow-up was also conducted throughout the study.RESULTS:In the subgroup classified as having an epileptogenic zone, individualized postprocessing detected abnormalities within the region of electroclinical origin in 9.7% to 31.0% of patients. Abnormalities outside the epileptogenic zone were more frequent, up to 51.7%. In five patients initially included with negative imaging, an epileptogenic structural abnormality was identified when a new visual magnetic resonance imaging inspection was guided by information gleaned from postprocessing. In three patients, epileptogenic lesions were detected after visual evaluation with volumetric fluid-attenuated sequence guided by video electroencephalography.CONCLUSION:Although quantitative magnetic resonance imaging analyses may suggest hidden structural lesions, caution is warranted because of the apparent low specificity of these findings for the epileptogenic zone. Conversely, these methods can be used to prevent visible lesions from being ignored, even in referral centers. In parallel, we need to highlight the positive contribution of the volumetric fluid-attenuated sequence.