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Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model for aneurysm detection that overcomes the issue with ''weak'' labels: oversized annotations which are considerably faster to create. Our weak labels resulted to be four times faster to generate than their voxel-wise counterparts. In addition, our model leverages prior anatomical knowledge by focusing only on plausible locations for aneurysm occurrence. We frst train and evaluate our model through cross-validation on an in-house TOF-MRA dataset comprising 284 subjects (170 females / 127 healthy controls / 157 patients with 198 aneurysms). On this dataset, our best model achieved a sensitivity of 83%, with False Positive (FP) rate of 0.8 per patient. To assess model generalizability, we then participated in a challenge for aneurysm detection with TOF-MRA data (93 patients, 20 controls, 125 aneurysms). On the public challenge, sensitivity was 68% (FP rate=2.5), ranking 4th/18 on the open leaderboard. We found no signifcant diference in sensitivity between aneurysm risk-of-rupture groups (p=0.75), locations (p=0.72), or sizes (p=0.15). Data, code and model weights are released under permissive licenses. We demonstrate that weak labels and anatomical knowledge can alleviate the necessity for prohibitively expensive voxel-wise annotations., Paper accepted as Original Article in the journal Neuroinformatics (https://link.springer.com/article/10.1007/s12021-022-09597-0)

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

Objective Cortical lesions are common in multiple sclerosis (MS), but their visualization is challenging on conventional magnetic resonance imaging. The uniform image derived from magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE(uni)) detects cortical lesions with a similar rate as the criterion standard sequence, double inversion recovery. Fluid and white matter suppression (FLAWS) provides multiple reconstructed contrasts acquired during a single acquisition. These contrasts include FLAWS minimum image (FLAWS(min)), which provides an exquisite sensitivity to the gray matter signal and therefore may facilitate cortical lesion identification, as well as high contrast FLAWS (FLAWS(hco)), which gives a contrast that is similar to one of MP2RAGE(uni). In this study, we compared the manual detection rate of cortical lesions on MP2RAGE(uni), FLAWS(min), and FLAWS(hco) in MS patients. Furthermore, we assessed whether the combined detection rate on FLAWS(min) and FLAWS(hco) was superior to MP2RAGE(uni) for cortical lesions identification. Last, we compared quantitative T1 maps (qT1) provided by both MP2RAGE and FLAWS in MS lesions. Materials and Methods We included 30 relapsing-remitting MS patients who underwent MP2RAGE and FLAWS magnetic resonance imaging with isotropic spatial resolution of 1 mm at 3 T. Cortical lesions were manually segmented by consensus of 3 trained raters and classified as intracortical or leukocortical lesions on (1) MP2RAGE uniform/flat images, (2) FLAWS(min), and (3) FLAWS(hco). In addition, segmented lesions on FLAWS(min) and FLAWS(hco) were merged to produce a union lesion map (FLAWS(min + hco)). Number and volume of all cortical, intracortical, and leukocortical lesions were compared among MP2RAGE(uni), FLAWS(min), and FLAWS(hco) using Friedman test and between MP2RAGE(uni) and FLAWS(min + hco) using Wilcoxon signed rank test. The FLAWS T1 maps were then compared with the reference MP2RAGE T1 maps using relative differences in percentage. In an exploratory analysis, individual cortical lesion counts of the 3 raters were compared, and interrater variability was quantified using Fleiss K. Results In total, 633 segmentations were made on the 3 contrasts, corresponding to 355 cortical lesions. The median number and volume of single cortical, intracortical, and leukocortical lesions were comparable among MP2RAGE(uni), FLAWS(min), and FLAWS(hco). In patients with cortical lesions (22/30), median cumulative lesion volume was larger on FLAWS(min) (587 mu L; IQR, 1405 mu L) than on MP2RAGE(uni) (490 mu L; IQR, 990 mu L; P = 0.04), whereas there was no difference between FLAWS(min) and FLAWS(hco), or FLAWS(hco) and MP2RAGE(uni). FLAWS(min + hco) showed significantly greater numbers of cortical (median, 4.5; IQR, 15) and leukocortical (median, 3.5; IQR, 12) lesions than MP2RAGE(uni) (median, 3; IQR, 10; median, 2.5; IQR, 7; both P < 0.001). Interrater agreement was moderate on MP2RAGE(uni) (K = 0.582) and FLAWS(hco) (K = 0.584), but substantial on FLAWS(min) (K = 0.614). qT1 in lesions was similar between MP2RAGE and FLAWS. Conclusions Cortical lesions identification in FLAWS(min) and FLAWS(hco) was comparable to MP2RAGE(uni)., The combination of FLAWS(min) and FLAWS(hco) allowed to identify a higher number of cortical lesions than MP2RAGE(uni), whereas qT1 maps did not differ between the 2 acquisition schemes.

Damage to the myelin sheath and the neuroaxonal unit is a cardinal feature of multiple sclerosis; however, a detailed characterization of the interaction between myelin and axon damage in vivo remains challenging., We applied myelin water and multi-shell diffusion imaging to quantify the relative damage to myelin and axons (i) among different lesion types; (ii) in normal-appearing tissue; and (iii) across multiple sclerosis clinical subtypes and healthy controls. We also assessed the relation of focal myelin/axon damage with disability and serum neurofilament light chain as a global biological measure of neuroaxonal damage. Ninety-one multiple sclerosis patients (62 relapsing-remitting, 29 progressive) and 72 healthy controls were enrolled in the study., Differences in myelin water fraction and neurite density index were substantial when lesions were compared to healthy control subjects and normal-appearing multiple sclerosis tissue: both white matter and cortical lesions exhibited a decreased myelin water fraction and neurite density index compared with healthy (P < 0.0001) and peri-plaque white matter (P < 0.0001). Periventricular lesions showed decreased myelin water fraction and neurite density index compared with lesions in the juxtacortical region ( P < 0.0001 and P < 0.05). Similarly, lesions with paramagnetic rims showed decreased myelin water fraction and neurite density index relative to lesions without a rim (P < 0.0001). Also, in 75% of white matter lesions, the reduction in neurite density index was higher than the reduction in the myelin water fraction. Besides, normal-appearing white and grey matter revealed diffuse reduction of myelin water fraction and neurite density index in multiple sclerosis compared to healthy controls (P < 0.01). Further, a more extensive reduction in myelin water fraction and neurite density index in normalappearing cortex was observed in progressive versus relapsing-remitting participants. Neurite density index in white matter lesions correlated with disability in patients with clinical deficits (P < 0.01, beta = -10.00); and neurite density index and myelin water fraction in white matter lesions were associated to serum neurofilament light chain in the entire patient cohort (P < 0.01, beta = -3.60 and P < 0.01, beta = 0.13, respectively)., These findings suggest that (i) myelin and axon pathology in multiple sclerosis is extensive in both lesions and normal-appearing tissue; (ii) particular types of lesions exhibit more damage to myelin and axons than others; (iii) progressive patients differ from relapsing-remitting patients because of more extensive axon/myelin damage in the cortex; and (iv) myelin and axon pathology in lesions is related to disability in patients with clinical deficits and global measures of neuroaxonal damage.

Magnetic resonance imaging of the eye and orbit (MReye) is a cross-domain research field, combining (bio)physics, (bio)engineering, physiology, data sciences and ophthalmology. A growing number of reports document technical innovations of MReye and promote their application in preclinical research and clinical science. Realizing the progress and promises, this review outlines current trends in MReye. Examples of MReye strategies and their clinical relevance are demonstrated. Frontier applications in ocular oncology, refractive surgery, ocular muscle disorders and orbital inflammation are presented and their implications for explorations into ophthalmic diseases are provided. Substantial progress in anatomically detailed, high-spatial resolution MReye of the eye, orbit and optic nerve is demonstrated. Recent developments in MReye of ocular tumors are explored, and its value for personalized eye models derived from machine learning in the treatment planning of uveal melanoma and evaluation of retinoblastoma is highlighted. The potential of MReye for monitoring drug distribution and for improving treatment management and the assessment of individual responses is discussed. To open a window into the eye and into (patho)physiological processes that in the past have been largely inaccessible, advances in MReye at ultrahigh magnetic field strengths are discussed. A concluding section ventures a glance beyond the horizon and explores future directions of MReye across multiple scales, including in vivo electrolyte mapping of sodium and other nuclei. This review underscores the need for the (bio)medical imaging and ophthalmic communities to expand efforts to find solutions to the remaining unsolved problems and technical obstacles of MReye, with the objective to transfer methodological advancements driven by MR physics into genuine clinical value.

Manually segmenting multiple sclerosis (MS) cortical lesions (CL) is extremely time-consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep learning-based framework (CLAIMS: Cortical Lesion Artificial Intelligence-based assessment in Multiple Sclerosis) for the automated detection and classification of MS CL with 7T MRI.Two 7T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5mm isotropic MP2RAGE acquired 4 times (MP2RAGEx4), 0.7mm MP2RAGE, 0.5mm T2*-weighted GRE, and 0.5mm T2*-weighted EPI. The second dataset consisted of 20 scans including only 0.75×0.75×0.9 mm MP2RAGE. CLAIMS was first evaluated using 6-fold cross-validation with single and multi-contrast 0.5mm MRI input. Second, performance of the model was tested on 0.7mm MP2RAGE images after training with either 0.5mm MP2RAGEx4, 0.7mm MP2RAGE, or alternating the two. Third, its generalizability was evaluated on the second external dataset and compared with a state-of-the-art technique based on partial volume estimation and topological constraints (MSLAST). CLAIMS trained only with MP2RAGEx4 achieved comparable results to the multi-contrast model, reaching a CL true positive rate of 74% with a false positive rate of 30%. Detection rate was excellent for leukocortical and subpial lesions (83%, and 70%, respectively), whereas it reached 53% for intracortical lesions. The correlation between disability measures and CL count was similar for manual and CLAIMS lesion counts. Applying a domain-scanner adaptation approach and testing CLAIMS on the second dataset, the performance was superior to MSLAST when considering a minimum lesion volume of 6μL (lesion-wise detection rate of 71% vs 48%).The proposed framework outperforms previous state-of-the-art methods for automated CL detection across scanners and protocols. In the future, CLAIMS may be useful to support clinical decisions at 7T MRI, especially in the field of diagnosis and differential diagnosis of multiple sclerosis patients.

Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS.We performed 3 studies: (1) a cross-sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso-intense, hypo-intense, hyperintense, lesions with hypo-intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology-QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions.At baseline, hypo- and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p 0.0001). Further, at 2-year follow-up, hypo-/iso-intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo-/iso-intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%).These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486-502.

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect., Three partial‐volume‐based and three probabilistic‐based tissue segmentation approaches were employed to assess thalamic gray matter integrity in patients at different stages of psychosis (early and chronic). This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Purpose: We present a 3-dimensional patient-specific eye model from magnetic resonance imaging (MRI) for proton therapy treatment planning of uveal melanoma (UM). During MRI acquisition of UM patients, the point fixation can be difficult and, together with physiological blinking, can introduce motion artifacts in the images, thus challenging the model creation. Furthermore, the unclear boundary of the small objects (eg, lens, optic nerve) near the muscle or of the tumors with hemorrhage and tantalum clips can limit model accuracy., Methods and Materials: A dataset of 37 subjects, including 30 healthy eyes of volunteers and 7 eyes of UM patients, was investigated. In our previous work, active shape model was successfully applied to retinoblastoma eye segmentation in T1-weighted 3T MRI. Here, we evaluate this method in a more challenging setting, based on 1.5T MRI acquisition and different datasets of awake adult eyes with UM. The lens and cornea together with the sclera, vitreous humor, and optic nerve were automatically segmented and validated against manual delineations of a senior ocular radiation oncologist, in terms of the Dice similarity coefficient and Hausdorff distance., Results: Leave-one-out cross validation (mixing both volunteers and UM patients) yielded median Dice similarity coefficient values (respective of Hausdorff distance) of 94.5% (1.64 mm) for the sclera, 92.2% (1.73 mm) for the vitreous humor, 88.3% (1.09 mm) for the lens, and 81.9% (1.86 mm) for the optic nerve. The average computation time for an eye was 10 seconds., Conclusions: To our knowledge, our work is the first attempt to automatically segment adult eyes, including patients with UM. Our results show that automated active shape model segmentation can succeed in the presence of motion, tumors, and tantalum clips. These results are promising for inclusion in clinical practice. (C) 2018 Elsevier Inc. All rights reserved.

It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. Here we introduce a publicly available dataset of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, deep grey matter, brainstem/spinal cord). In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. Four research groups participated, submitting a total of 10 algorithms, demonstrating the benefits the dataset for the development of automatic algorithms., Scientific Data, 8 (1), ISSN:2052-4463

Highlights • The attention mechanism can rank MR measures by relative importance. • Proposed guideline for use of the attention mechanism with MR measures. • Attention weights and quantitative MR measures can potentially form new patterns., Introduction During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. Methods GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. Results In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p

Purpose The thalamus is an important brain structure and neurosurgical target, but its constituting nuclei are challenging to image non-invasively. Recently, susceptibility-weighted imaging (SWI) at ultra-high field has shown promising capabilities for thalamic nuclei mapping. In this work, several methodological improvements were explored to enhance SWI quality and contrast, and specifically its ability for thalamic imaging. Methods High-resolution SWI was performed at 7T in healthy participants, and the following techniques were applied: (a) monitoring and retrospective correction of head motion and B-0 perturbations using integrated MR navigators, (b) segmentation and removal of venous vessels on the SWI data using vessel enhancement filtering, and (c) contrast enhancement by tuning the parameters of the SWI phase-magnitude combination. The resulting improvements were evaluated with quantitative metrics of image quality, and by comparison to anatomo-histological thalamic atlases. Results Even with sub-millimeter motion and natural breathing, motion and field correction produced clear improvements in both magnitude and phase data quality (76% and 41%, respectively). The improvements were stronger in cases of larger motion/field deviations, mitigating the dependence of image quality on subject performance. Optimizing the SWI phase-magnitude combination yielded substantial improvements in image contrast, particularly in the thalamus, well beyond previously reported SWI results. The atlas comparisons provided compelling evidence of anatomical correspondence between SWI features and several thalamic nuclei, for example, the ventral intermediate nucleus. Vein detection performed favorably inside the thalamus, and vein removal further improved visualization. Conclusion Altogether, the proposed developments substantially improve high-resolution SWI, particularly for thalamic nuclei imaging.

Recovering the T

Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

Graphical abstract, Highlights • RimNet, an automated method to detect paramagnetic rim in Multiple Sclerosis lesions. • Different rim detection ability of 3D FLAIR and 3D EPI (T2* & Phase) MRI. • RimNet performance is close to experts’ at lesion and patient-wise levels. • Automated rim analysis is feasible with one single 3D EPI MR acquisition. • Excellent RimNet performance is maintained in inter-hospital evaluation., Objectives In multiple sclerosis (MS), the presence of a paramagnetic rim at the edge of non-gadolinium-enhancing lesions indicates perilesional chronic inflammation. Patients featuring a higher paramagnetic rim lesion burden tend to have more aggressive disease. The objective of this study was to develop and evaluate a convolutional neural network (CNN) architecture (RimNet) for automated detection of paramagnetic rim lesions in MS employing multiple magnetic resonance (MR) imaging contrasts. Materials and methods Imaging data were acquired at 3 Tesla on three different scanners from two different centers, totaling 124 MS patients, and studied retrospectively. Paramagnetic rim lesion detection was independently assessed by two expert raters on T2*-phase images, yielding 462 rim-positive (rim+) and 4857 rim-negative (rim-) lesions. RimNet was designed using 3D patches centered on candidate lesions in 3D-EPI phase and 3D FLAIR as input to two network branches. The interconnection of branches at both the first network blocks and the last fully connected layers favors the extraction of low and high-level multimodal features, respectively. RimNet’s performance was quantitatively evaluated against experts’ evaluation from both lesion-wise and patient-wise perspectives. For the latter, patients were categorized based on a clinically relevant threshold of 4 rim+ lesions per patient. The individual prediction capabilities of the images were also explored and compared (DeLong test) by testing a CNN trained with one image as input (unimodal). Results The unimodal exploration showed the superior performance of 3D-EPI phase and 3D-EPI magnitude images in the rim+/- classification task (AUC = 0.913 and 0.901), compared to the 3D FLAIR (AUC = 0.855, Ps

Background Essential tremor (ET) is a common movement disorder. Resting-state functional magnetic resonance imaging is a noninvasive neuroimaging method acquired in absence of task. Objective Our study aimed to correlate pretherapeutic ventrolateral thalamus functional connectivity (FC) with clinical results 1 year after stereotactic radiosurgical thalamotomy (SRS-T) for drug-resistant ET. Data from 12 healthy control individuals were additionally included. Methods Resting state was acquired for 17 consecutive (right-handed) patients, before and 1 year after left unilateral SRS-T. Standard tremor scores were evaluated pretherapeutically and 1 year after SRS-T. Tremor network was investigated using region of interest, left ventrolateral ventral (VLV) cluster, obtained from pretherapeutic diffusion magnetic resonance imaging. Seed-based FC was obtained as correlations between the time courses of the VLV and that of every other voxel. The seed-connectivity maps were obtained pretherapeutically and correlated across all patients with clinical outcome 1 year after SRS-T. One-year magnetic resonance signature volume was always located inside VLV and did not correlate with reported seed-FC measures (P > 0.05). Results We report statistically significant correlations between pretherapeutic VLV FC with clinical outcome for 1) right visual association area (Brodmann area, BA19) predicting 1 year activities of daily living decrease (Punc = 0.02); 2) left fusiform gyrus (BA37) predicting 1 year head tremor score improvement (Punc = 0.04); and 3) posterior cingulate (left BA23, Puncor = 0.009), lateral temporal cortex (right BA21, Punc = 0.02) predicting time to tremor arrest. Conclusions Our results suggest that pretherapeutic resting-state seed-FC of left VLV predicts tremor arrest after SRS-T for ET. Visual areas are identified as the main regions in this correlation.

OBJECTIVE: To correlate pretherapeutic resting-state functional magnetic resonance imaging (rs-fMRI) measures with pretherapeutic head tremor presence and/or further improvement 1 year after stereotactic radiosurgical thalamotomy (SRS-T) for essential tremor (ET). METHODS: We prospectively collected head tremor scores (range, 0-3) and rs-fMRI data for a cohort of 17 consecutive ET patients in pretherapeutic and 1 year after SRS-T states. We additionally acquired rs-fMRI data for a healthy control (HC) group (n~= 12). Group-level independent component analysis (n~= 17 for pretherapeutic rs-fMRI) was applied to decompose neuroimaging data into 20 large-scale brain networks using a standard approach. Through spatial regression, we projected 1 year after SRS-T and HC rs-fMRI time points, on the same 20 brain networks. RESULTS: Pretherapeutic interconnectivity (IC) strength between the network including bilateral thalamus and limbic system with left supplementary motor area predicted head tremor improvement at 1 year after SRS-T (family-wise corrected P < 0.001, cluster size Kc~= 146). For the statistically significant cluster, IC strength was strongest in HCs (mean, 4.6; median, 3.8) compared with pre- (mean, 0.1; median, 0.2) or posttherapeutic (mean,~-0.2; median, 0.09) states. CONCLUSIONS: Baseline measures of IC between bilateral thalamus and limbic system with left supplementary motor area may predict head tremor arrest after thalamotomy. However, procedures such as SRS-T, for this particular clinical feature, do not align patients to HCs in terms of functional brain connectivity. We postulate that supplementary motor area is modulating head tremor appearance, by abnormal connectivity with the thalamolimbic system.

White-matter lesion count and volume estimation are key to the diagnosis and monitoring of multiple sclerosis (MS). Automated MS lesion segmentation methods that have been proposed in the past 20 years reach their limits when applied to patients in early disease stages characterized by low lesion load and small lesions. We propose an algorithm to automatically assess MS lesion load (number and volume) while taking into account the mixing of healthy and lesional tissue in the image voxels due to partial volume effects. The proposed method works on 3D MPRAGE and 3D FLAIR images as obtained from current routine MS clinical protocols. The method was evaluated and compared with manual segmentation on a cohort of 39 early-stage MS patients with low disability, and showed higher Dice similarity coefficients (median DSC = 0.55) and higher detection rate (median DR = 61%) than two widely used methods (median DSC = 0.50, median DR, Highlights • Modeling the partial volume improves lesion volumetric measurements. • Higher detection of small lesions inherently prone to partial volume effects. • Partial volume effects should be taken into account in early stages of MS.

International audience; INTRODUCTION: Drug-resistant essential tremor (ET) can benefit from open standard stereotactic procedures, such as deep-brain stimulation or radiofrequency thalamotomy. Non-surgical candidates can be offered either high-focused ultrasound (HIFU) or radiosurgery (RS). All procedures aim to target the same thalamic site, the ventro-intermediate nucleus (e.g., Vim). The mechanisms by which tremor stops after Vim RS or HIFU remain unknown. We used voxel-based morphometry (VBM) on pretherapeutic neuroimaging data and assessed which anatomical site would best correlate with tremor arrest 1 year after Vim RS. METHODS: Fifty-two patients (30 male, 22 female; mean age 71.6 years, range 49-82) with right-sided ET benefited from left unilateral Vim RS in Marseille, France. Targeting was performed in a uniform manner, using 130 Gy and a single 4-mm collimator. Neurological (pretherapeutic and 1 year after) and neuroimaging (baseline) assessments were completed. Tremor score on the treated hand (TSTH) at 1 year after Vim RS was included in a statistical parametric mapping analysis of variance (ANOVA) model as a continuous variable with pretherapeutic neuroimaging data. Pretherapeutic gray matter density (GMD) was further correlated with TSTH improvement. No a priori hypothesis was used in the statistical model. RESULTS: The only statistically significant region was right Brodmann area (BA) 18 (visual association area V2, p = 0.05, cluster size Kc = 71). Higher baseline GMD correlated with better TSTH improvement at 1 year after Vim RS (Spearman's rank correlation coefficient = 0.002). CONCLUSIONS: Routine baseline structural neuroimaging predicts TSTH improvement 1 year after Vim RS. The relevant anatomical area is the right visual association cortex (BA 18, V2). The question whether visual areas should be included in the targeting remains open.

OBJECTIVE Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard stereotactic deep brain stimulation or radiofrequency thalamotomy or, alternatively, minimally invasive techniques, including stereotactic radiosurgery (SRS) and high-intensity focused ultrasound, at the level of the ventral intermediate nucleus (Vim). The aim of the present study was to evaluate potential correlations between pretherapeutic interconnectivity (IC), as depicted on resting-state functional MRI (rs-fMRI), and MR signature volume at 1 year after Vim SRS for tremor, to be able to potentially identify hypo-and hyperresponders based only on pretherapeutic neuroimaging data., METHODS Seventeen consecutive patients with ET were included, who benefitted from left unilateral SRS thalamotomy (SRS-T) between September 2014 and August 2015. Standard tremor assessment and rs-fMRI were acquired pretherapeutically and 1 year after SRS-T. A healthy control group was also included (n = 12). Group-level independent component analysis (ICA; only n = 17 for pretherapeutic rs-fMRI) was applied. The mean MR signature volume was 0.125 ml (median 0.063 ml, range 0.002-0.600 ml). The authors correlated baseline IC with 1-year MR signatures within all networks. A 2-sample t-test at the level of each component was first performed in two groups: group 1 (n = 8, volume < 0.063 ml) and group 2 (n = 9, volume >= 0.063 ml). These groups did not statistically differ by age, duration of symptoms, baseline ADL score, ADL point decrease at 1 year, time to tremor arrest, or baseline tremor score on the treated hand (TSTH; p > 0.05). An ANOVA was then performed on each component, using individual subject-level maps and continuous values of 1-year MR signatures, correlated with pretherapeutic IC., RESULTS Using 2-sample t-tests, two networks were found to be statistically significant: network 3, including the brain-stem, motor cerebellum, bilateral thalamus, and left supplementary motor area (SMA) (p(FWE) = 0.004, cluster size = 94), interconnected with the red nucleus (MNI -2, -22, -32); and network 9, including the brainstem, posterior insula, bilateral thalamus, and left SMA (p(FWE) = 0.002, cluster size = 106), interconnected with the left SMA (MNI 24, -28, 44). Higher pretherapeutic IC was associated with higher MR volumes, in a network including the anterior default-mode network and bilateral thalamus (ANOVA, p(FWE) = 0.004, cluster size = 73), interconnected with cerebellar lobule V (MNI -12, -70, -22). Moreover, in the same network, radiological hyporesponders presented with negative IC values., CONCLUSIONS These findings have clinical implications for predicting MR signature volumes after SRS-T. Here, using pretherapeutic MRI and data processing without prior hypothesis, the authors showed that pretherapeutic network inter-connectivity strength predicts 1-year MR signature volumes following SRS-T.

INTRODUCTION: Essential tremor (ET) is the most common movement disorder. Drug-resistant ET can benefit from standard surgical stereotactic~procedures (deep brain stimulation, thalamotomy) or minimally invasive high-intensity focused ultrasound (HIFU) or stereotactic radiosurgical thalamotomy (SRS-T). Resting-state fMRI (rs-fMRI) is a non-invasive imaging method acquired in absence of a task. We examined whether rs-fMRI correlates with tremor score on the treated hand (TSTH) improvement 1~year after SRS-T. METHODS: We included 17 consecutive patients treated with left unilateral SRS-T in Marseille, France. Tremor score evaluation and rs-fMRI were acquired at baseline and 1~year after SRS-T. Resting-state data (34 scans) were analyzed without a priori hypothesis, in Lausanne, Switzerland. Based on degree of improvement in TSTH, to consider SRS-T at least as effective as medication, we separated two groups: 1, ≤q\,50% (n\,=\,6, 35.3%); 2, >\,50% (n\,=\,11, 64.7%). They did not differ statistically by age (p\,=\,0.86), duration of symptoms (p\,=\,0.41), or lesion volume at 1~year (p\,=\,0.06). RESULTS: We report TSTH improvement correlated with interconnectivity strength between salience network with the left claustrum and putamen, as well as between bilateral motor cortices, frontal~eye fields and left~cerebellum lobule VI with right visual association area (the former also with lesion volume). Longitudinal changes showed additional associations in interconnectivity strength between right dorsal attention network with ventro-lateral prefrontal cortex and a reminiscent~salience network with fusiform gyrus. CONCLUSIONS: Brain connectivity measured by resting-state fMRI relates to clinical response after SRS-T. Relevant networks are visual, motor, and attention. Interconnectivity between visual and motor areas is a novel finding, revealing implication in movement sensory guidance.

Objective To evaluate functional connectivity (FC) of the ventrolateral thalamus, a common target for drug-resistant essential tremor (ET), resting-state data were analyzed before and 1 year after stereotactic radiosurgical thalamotomy and compared against healthy controls (HCs). Methods In total, 17 consecutive patients with ET and 10 HCs were enrolled. Tremor network was investigated using the ventrolateral ventral (VLV) thalamic nucleus as the region of interest, extracted with automated segmentation from pretherapeutic diffusion magnetic resonance imaging. Temporal correlations of VLV at whole brain level were evaluated by comparing drug-naive patients with ET with HCs, and longitudinally, 1 year after stereotactic radiosurgical thalamotomy. 1 year thalamotomy MR signature was always located inside VLV and did not correlate with any of FC measures (P > 0.05). This suggested presence of longitudinal changes in VLV FC independently of the MR signature volume. Results Pretherapeutic ET displayed altered VLV FC with left primary sensory-motor cortex, pedunculopontine nucleus, dorsal anterior cingulate, left visual association, and left superior parietal areas. Pretherapeutic negative FC with primary somatosensory cortex and pedunculopontine nucleus correlated with poorer baseline tremor scores (Spearman = 0.04 and 0.01). Longitudinal study displayed changes within right dorsal attention (frontal eye-fields and posterior parietal) and salience (anterior insula) networks, as well as areas involved in hand movement planning or language production. Conclusions Our results demonstrated that patients with ET and HCs differ in their left VLV FC to primary somatosensory and supplementary motor, visual association, or brainstem areas (pedunculopontine nucleus). Longitudinal changes display reorganization of dorsal attention and salience networks after thalamotomy. Beside attentional gateway, they are also known for their major role in facilitating a rapid access to the motor system.

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

International audience; INTRODUCTION: Radiosurgery (RS) is an alternative to open standard stereotactic procedures (deep-brain stimulation or radiofrequency thalamotomy) for drug-resistant essential tremor (ET), aiming at the same target (ventro-intermediate nucleus, Vim). We investigated the Vim RS outcome using voxel-based morphometry by evaluating the interaction between clinical response and time. METHODS: Thirty-eight patients with right-sided ET benefited from left unilateral Vim RS. Targeting was performed using 130 Gy and a single 4-mm collimator. Neurological and neuroimaging assessment was completed at baseline and 1 year. Clinical responders were considered those with at least 50% improvement in tremor score on the treated hand (TSTH). RESULTS: Interaction between clinical response and time showed the left temporal pole and occipital cortex (Brodmann area 19, including V4, V5 and the parahippocampal place area) as statistically significant. A decrease in gray matter density (GMD) 1 year after Vim RS correlated with higher TSTH improvement (Spearman = 0.01) for both anatomical areas. Higher baseline GMD within the left temporal pole correlated with better TSTH improvement (Spearman = 0.004). CONCLUSIONS: Statistically significant structural changes in the relationship to clinical response after Vim RS are present in remote areas, advocating a distant neurobiological effect. The former regions are mainly involved in locomotor monitoring toward the local and distant environment, suggesting the recruiting requirement in targeting of the specific visuomotor networks.

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.

Reference EPFL-ARTICLE-229525doi:10.1007/s00701-017-3227-8View record in Web of Science Record created on 2017-07-10, modified on 2017-07-10

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Objectives: The aim of this study was to study focal cerebellar pathology in early stages of multiple sclerosis (MS) using ultra-high-field magnetization-prepared 2 inversion-contrast rapid gradient-echo (7T MP2RAGE). Materials and Methods: Twenty early-stage relapsing-remitting MS patients underwent an MP2RAGE acquisition at 7 T magnetic resonance imaging (MRI) (images acquired at 2 different resolutions: 0.58 x 0.58 x 0.58 mm(3), 7T_0.58, and 0.75 x 0.75 x 0.90 mm(3), 7T_0.75) and 3 T MRI (1.0 x 1.0 x 1.2 mm(3), 3T_1.0). Total cerebellar lesion load and volume andmean cerebellar lesion volume were compared across images using a Wilcoxon signed-rank test. Mean T1 relaxation times in lesions and normal-appearing tissue as well as contrast-to-noise ratio (CNR) measurements were also compared using a Wilcoxon signed-rank test. A multivariate analysis was applied to assess the contribution of MRI metrics to clinical performance in MS patients. Results: Both 7T_0.58 and 7T_0.75 MP2RAGE showed significantly higher lesion load compared with 3T_1.0 MP2RAGE (P < 0.001). Plaques that were judged as leukocortical in 7T_0.75 and 3T_1.0 MP2RAGEs were instead identified as WM lesions in 7T_0.58 MP2RAGE. Cortical lesion CNR was significantly higher in MP2RAGEs at 7 T than at 3 T. Total lesion load as well as total and mean lesion volume obtained at both 7 T and 3 T MP2RAGE significantly predicted attention (P < 0.05, adjusted R-2 = 0.5), verbal fluency (P < 0.01, adjusted R-2 = 0.6), and motor performance (P = 0.01, adjusted R-2 = 0.7). Conclusions: This study demonstrates the value of 7 T MP2RAGE to study the cerebellum in early MS patients. 7T_0.58 MP2RAGE provides a more accurate anatomical description of white and gray matter pathology compared with 7T_0.75 and 3T_1.0 MP2RAGE, likely due to the improved spatial resolution, lower partial volume effects, and higher CNR.

The thalamus is an essential relay station in the cortical–subcortical connections. It is characterized by a complex anatomical architecture composed of numerous small nuclei, which mediate the involvement of the thalamus in a wide range of neurological functions. We present a novel framework for segmenting the thalamic nuclei, which explores the orientation distribution functions (ODFs) from diffusion magnetic resonance images at 3 T. The differentiation of the complex intra-thalamic microstructure is improved by using the spherical harmonic (SH) representation of the ODFs, which provides full angular characterization of the diffusion process in each voxel. The clustering was performed using the k-means algorithm initialized in a data-driven manner. The method was tested on 35 healthy volunteers and our results show a robust, reproducible and accurate segmentation of the thalamus in seven nuclei groups. Six of them closely matched the anatomy and were labeled as anterior, ventral anterior, medio-dorsal, ventral latero-ventral, ventral latero-dorsal and pulvinar, while the seventh cluster included the centro-lateral and the latero-posterior nuclei. Results were evaluated both qualitatively, by comparing the segmented nuclei to the histological atlas of Morel, and quantitatively, by measuring the clusters’ extent and the clusters’ spatial distribution across subjects and hemispheres. We also showed the robustness of our approach across different sequences and scanners, as well as intra-subject reproducibility of the segmented clusters using additional two scan–rescan datasets. We also observed an overlap between the path of the main long-connection tracts passing through the thalamus and the spatial distribution of the nuclei identified with our clustering algorithm. Our approach, based on SH representations of the ODFs, outperforms the one based on angular differences between the principle diffusion directions, which is considered so far as state-of-the-art method. Our findings show an anatomically reliable segmentation of the main groups of thalamic nuclei that could be of potential use in many clinical applications. Electronic supplementary material The online version of this article (doi:10.1007/s00429-016-1336-4) contains supplementary material, which is available to authorized users.

22q11.2 deletion syndrome (22q11.2DS) is a common genetic condition associated with cognitive and learning impairments. In this study, we applied a three-dimensional method for quantifying gyrification at thousands of points over the cortical surface to imaging data from 44 children, adolescents, and young adults with 22q11.2DS (17 males, 27 females; mean age 17y 2mo [SD 9y 1mo], range 6-37y), and 53 healthy participants (21 males, 32 females; mean age 15y 4mo [SD 8y 6mo]; range 6-40y). Several clusters of reduced gyrification were observed, further substantiating the pattern of cerebral alterations presented by children with the syndrome. Comparisons within 22q11.2DS demonstrated an effect of congenital heart disease (CHD) on cortical gyrification, with reduced gyrification at the parieto-temporo-occipital junction in patients with CHD, as compared with patients without CHD. Reductions in gyrification can resemble mild polymicrogyria, suggesting early abnormal neuronal proliferation or migration and providing support for an effect of hemodynamic factors on brain development in 22q11.2DS. The results also shed light on the pathophysiology of acquired brain injury in other populations with CHD.

Current methods for processing diffusion MRI (dMRI) to map the connectivity of the human brain require precise delineations of anatomical structures. This requirement has been approached by either segmenting the data in native dMRI space or mapping the structural information from T1-weighted (T1w) images. The characteristic features of diffusion data in terms of signal-to-noise ratio, resolution, as well as the geometrical distortions caused by the inhomogeneity of magnetic susceptibility across tissues hinder both solutions. Unifying the two approaches, we proposeregseg, a surface-to-volume nonlinear registration method that segments homogeneous regions within multivariate images by mapping a set of nested reference-surfaces. Accurate surfaces are extracted from a T1w image of the subject, using as target image the bivariate volume comprehending the fractional anisotropy (FA) and the apparent diffusion coefficient (ADC) maps derived from the dMRI dataset. We first verify the accuracy ofregsegon a general context using digital phantoms distorted with synthetic and random deformations. Then we establish an evaluation framework using undistorted dMRI data from the Human Connectome Project (HCP) and realistic deformations derived from the inhomogeneity fieldmap corresponding to each subject. We analyze the performance ofregsegcomputing the misregistration error of the surfaces estimated after being mapped withregsegonto 16 datasets from the HCP. The distribution of errors shows a 95% CI of 0.56–0.66 mm, that is below the dMRI resolution (1.25 mm, isotropic). Finally, we cross-compare the proposed tool against a nonlinearb0-to-T2w registration method, thereby obtaining a significantly lower misregistration error withregseg. The accurate mapping of structural information in dMRI space is fundamental to increase the reliability of network building in connectivity analyses, and to improve the performance of the emerging structure-informed techniques for dMRI data processing.

Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 +/- 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross- validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 +/- 2.12% for the sclera and the cornea, 94.72 +/- 1.89% for the vitreous humor, and 85.16 +/- 4.91% for the lens. The mean distance error was 0.26 +/- 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. (C) 2015 Elsevier Inc. All rights reserved.

© 2015 Elsevier Inc.. Although fetal anatomy can be adequately viewed in new multi slice MR images many critical limitations remain for quantitative data analysis. To this end several research groups have recently developed advanced image processing methods often denoted by super resolution (SR) techniques to reconstruct from a set of clinical low resolution (LR) images a high resolution (HR) motion free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super resolution problem. In this work two major contributions are presented. Firstly we will briefly review the Bayesian and Variational dual formulations of current state of the art methods dedicated to fetal MRI reconstruction. Secondly we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade off between speed and accuracy for fetal MRI recovery as in comparison with state of the art methods.

Fetal MRI reconstruction aims at finding a high resolution image given a small set of low resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy [1] Total Variation (TV) based energies [23] and more recently non local means [4]. Although TV energies are quite attractive because of their ability in edge preservation standard explicit steepest gradient techniques have been applied to optimize fetal based TV energies. The main contribution of this work lies in the introduction of a well posed TV algorithm from the point of view of convex optimization. Specifically our proposed TV optimization algorithm for fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n 2) and O(1/ve) while existing techniques are in O(1/n) and O(1/e). We apply our algorithm to (1) clinical newborn data considered as ground truth and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy. © 2014 Springer International Publishing.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

Cortical folding (gyrification) is determined during the first months of life, so that adverse events occurring during this period leave traces that will be identifiable at any age. As recently reviewed by Mangin and colleagues(2), several methods exist to quantify different characteristics of gyrification. For instance, sulcal morphometry can be used to measure shape descriptors such as the depth, length or indices of inter-hemispheric asymmetry(3). These geometrical properties have the advantage of being easy to interpret. However, sulcal morphometry tightly relies on the accurate identification of a given set of sulci and hence provides a fragmented description of gyrification. A more fine-grained quantification of gyrification can be achieved with curvature-based measurements, where smoothed absolute mean curvature is typically computed at thousands of points over the cortical surface(4). The curvature is however not straightforward to comprehend, as it remains unclear if there is any direct relationship between the curvedness and a biologically meaningful correlate such as cortical volume or surface. To address the diverse issues raised by the measurement of cortical folding, we previously developed an algorithm to quantify local gyrification with an exquisite spatial resolution and of simple interpretation. Our method is inspired of the Gyrification Index(5), a method originally used in comparative neuroanatomy to evaluate the cortical folding differences across species. In our implementation, which we name local Gyrification Index (lGI(1)), we measure the amount of cortex buried within the sulcal folds as compared with the amount of visible cortex in circular regions of interest. Given that the cortex grows primarily through radial expansion(6), our method was specifically designed to identify early defects of cortical development. In this article, we detail the computation of local Gyrification Index, which is now freely distributed as a part of the FreeSurfer Software (http://surfer.nmr.mgh.harvard.edu/, Martinos Center for Biomedical Imaging, Massachusetts General Hospital). FreeSurfer provides a set of automated reconstruction tools of the brain's cortical surface from structural MRI data. The cortical surface extracted in the native space of the images with sub-millimeter accuracy is then further used for the creation of an outer surface, which will serve as a basis for the lGI calculation. A circular region of interest is then delineated on the outer surface, and its corresponding region of interest on the cortical surface is identified using a matching algorithm as described in our validation study(1). This process is repeatedly iterated with largely overlapping regions of interest, resulting in cortical maps of gyrification for subsequent statistical comparisons (Fig. 1). Of note, another measurement of local gyrification with a similar inspiration was proposed by Toro and colleagues(7), where the folding index at each point is computed as the ratio of the cortical area contained in a sphere divided by the area of a disc with the same radius. The two implementations differ in that the one by Toro et al. is based on Euclidian distances and thus considers discontinuous patches of cortical area, whereas ours uses a strict geodesic algorithm and include only the continuous patch of cortical area opening at the brain surface in a circular region of interest.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad-hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

International audience; This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner. The final accepted version of this paper is published in Medical Image Analysis, vol 15(6) ,doi:10.1016/j.media.2011.05.008

Normal and abnormal brains can be segmented by registering the target image with an atlas. Here, an atlas is defined as the combination of an intensity image (template) and its segmented image (the atlas labels). After registering the atlas template and the target image, the atlas labels are propagated to the target image. We define this process as atlas-based segmentation. In recent years, researchers have investigated registration algorithms to match atlases to query subjects and also strategies for atlas construction. In this paper we present a review of the automated approaches for atlas-based segmentation of magnetic resonance brain images. We aim to point out the strengths and weaknesses of atlas-based methods and suggest new research directions. We use two different criteria to present the methods. First, we refer to the algorithms according to their atlas-based strategy: label propagation, multi-atlas methods, and probabilistic techniques. Subsequently, we classify the methods according to their medical target: the brain and its internal structures, tissue segmentation in healthy subjects, tissue segmentation in fetus, neonates and elderly subjects, and segmentation of damaged brains. A quantitative comparison of the results reported in the literature is also presented.

This paper presents a new non parametric atlas registration framework, derived from the optical flow model and the active contour theory, applied to automatic subthalamic nucleus (STN) targeting in deep brain stimulation (DBS) surgery. In a previous work, we demonstrated that the STN position can be predicted based on the position of surrounding visible structures, namely the lateral and third ventricles. A STN targeting process can thus be obtained by registering these structures of interest between a brain atlas and the patient image. Here we aim to improve the results of the state of the art targeting methods and at the same time to reduce the computational time. Our simultaneous segmentation and registration model shows mean STN localization errors statistically similar to the most performing registration algorithms tested so far and to the targeting expert's variability. Moreover, the computational time of our registration method is much lower, which is a worthwhile improvement from a clinical point of view.

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.

To develop a method to automatically detect multiple sclerosis (MS) lesions, located both in white matter (WM) and in the cortex, in patients with low disability and early disease stage.We developed a lesion detection method, based on the k-nearest neighbor (k-NN) technique, to detect lesions as small as 0.0036 mL. This method uses the image intensity information from up to four different 3D magnetic resonance imaging (MRI) sequences (magnetization-prepared rapid gradient-echo, MPRAGE; magnetization-prepared two inversion-contrast rapid gradient-echo, MP2RAGE; 3D fluid-attenuated inversion recovery, FLAIR; and 3D double-inversion recovery, DIR), acquired on a 3T scanner. To these intensity features we added the information obtained by the spatial coordinates and tissue prior probabilities provided by the International Consortium for Brain Mapping (ICBM). Quantitative assessment was done in 39 early-stage MS patients with a "leave-one-out" cross-validation.The best lesion detection rate (DR) performance in WM was obtained using MP2RAGE, FLAIR, and DIR intensities (77% for lesions ≥0.0036 mL; 85% for lesions ≥0.005 mL). Similar results were obtained excluding the DIR intensity as well as when using only MPRAGE and FLAIR (DR = 75%, P = 0.5720). However, the combination of FLAIR with DIR and MP2RAGE appeared to be the best for detecting cortical lesions (DR = 62%), compared to the other combination of sequences (P0.001).For WM lesion detection, similar results were observed when only conventional clinical sequences (FLAIR, MPRAGE) were used compared to a combination of conventional and "advanced" sequences (MP2RAGE, DIR). Cortical lesion detection increased significantly when "advanced" sequences were used. J. Magn. Reson. Imaging 2015. J. Magn. Reson. Imaging 2016;43:1445-1454.

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatent of Parkinson's disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise e.g., Fundus photography, Optical Coherence Tomography (OCT), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The presented article’s goal is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI which was not visible before like vessels and the macula. This article’s contributions include automatic detection of the optic disc, the fovea, the optic axis and an automatic segmentation of the vitreous humor of the eye.