MRI Modalities Research Articles

Using imaging modalities to predict nanoparticle distribution and treatment efficacy in solid tumors: The growing role of ultrasound.

Nanomedicine in oncology has not had the success in clinical impact that was anticipated in the early stages of the field's development. Ideally, nanomedicines selectively accumulate in tumor tissue and reduce systemic side effects compared to traditional chemotherapeutics. However, this has been more successful in preclinical animal models than in humans. The causes of this failure to translate may be related to the intra- and inter-patient heterogeneity of the tumor microenvironment. Predicting whether a patient will respond positively to treatment prior to its initiation, through evaluation of characteristics like nanoparticle extravasation and retention potential in the tumor, may be a way to improve nanomedicine success rate. While there are many potential strategies to accomplish this, prediction and patient stratification via noninvasive medical imaging may be the most efficient and specific strategy. There have been some preclinical and clinical advances in this area using MRI, CT, PET, and other modalities. An alternative approach that has not been studied as extensively is biomedical ultrasound, including techniques such as multiparametric contrast-enhanced ultrasound (mpCEUS), doppler, elastography, and super-resolution processing. Ultrasound is safe, inexpensive, noninvasive, and capable of imaging the entire tumor with high temporal and spatial resolution. In this work, we summarize the in vivo imaging tools that have been used to predict nanoparticle distribution and treatment efficacy in oncology. We emphasize ultrasound imaging and the recent developments in the field concerning CEUS. The successful implementation of an imaging strategy for prediction of nanoparticle accumulation in tumors could lead to increased clinical translation of nanomedicines, and subsequently, improved patient outcomes. This article is categorized under: Diagnostic Tools In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery Emerging Technologies.

Neurotopographical Transformations: Dissecting Cortical Reconfigurations in Auditory Deprivation.

Within the intricate matrices of cognitive neuroscience, auditory deprivation acts as a catalyst, propelling a cascade of neuroanatomical adjustments that have, until now, been suboptimally articulated in extant literature. Addressing this gap, our study harnesses high-resolution 3 T MRI modalities to unveil the multifaceted cortical transformations that emerge in tandem with congenital auditory deficits. We conducted a rigorous cortical surface analysis on a cohort of 90 congenitally deaf individuals, systematically compared with 90 normoacoustic controls. Our sample encompassed both male and female participants, ensuring a gender-inclusive perspective in our analysis. Expected alterations within prototypical auditory domains were evident, but our findings transcended these regions, spotlighting modifications dispersed across a gamut of cortical and subcortical structures, thereby epitomizing the cerebral adaptive dynamics to sensory voids. Crucially, the study's innovative methodology integrated two pivotal variables: the duration of auditory deprivation and the extent of sign language immersion. By intersecting these metrics with structural changes, our analysis unveiled nuanced layers of cortical reconfigurations, elucidating a more granulated understanding of neural plasticity. This intersectional approach bestows a unique advantage, allowing for a discerning exploration into how varying durations of sensory experience and alternative communication modalities modulate the brain's morphological terrain. In encapsulating the synergy of neuroimaging finesse and incisive scientific rigor, this research not only broadens the current understanding of adaptive neural mechanisms but also paves the way for tailored therapeutic strategies, finely attuned to individual auditory histories and communicative repertoires.

Relaxed fibronectin: a potential novel target for imaging endometriotic lesions

BackgroundEndometriosis is characterized by the ectopic occurrence of endometrial tissue. Though considered benign, endometriotic lesions possess tumor-like properties such as tissue invasion and remodeling of the extracellular matrix. One major clinical hurdle concerning endometriosis is its diagnosis. The diagnostic modalities ultrasound and MRI are often unable to detect all lesions, and a clear correlation between imaging and clinical symptoms is still controversial. Therefore, it was our aim to identify a potential target to image active endometriotic lesions.ResultsFor our studies, we employed the preclinical radiotracer [111In]In-FnBPA5, which specifically binds to relaxed fibronectin–an extracellular matrix protein with key functions in homeostasis that has been implicated in the pathogenesis of diseases such as cancer and fibrosis. We employed this tracer in biodistribution as well as SPECT/CT studies in mice and conducted immunohistochemical stainings on mouse uterine tissue as well as on patient-derived endometriosis tissue. In biodistribution and SPECT/CT studies using the radiotracer [111In]In-FnBPA5, we found that radiotracer uptake in the myometrium varies with the estrous cycle of the mouse, leading to higher uptake of [111In]In-FnBPA5 during estrogen-dependent phases, which indicates an increased abundance of relaxed fibronectin when estrogen levels are high. Finally, immunohistochemical analysis of patient samples demonstrated that there is preferential relaxation of fibronectin in the proximity of the endometriotic stroma.ConclusionEstrous cycle stages characterized by high estrogen levels result in a higher abundance of relaxed fibronectin in the murine myometrium. This finding together with a first proof-of-concept study employing human endometriosis tissues suggests that relaxed fibronectin could be a potential target for the development of a diagnostic radiotracer targeting endometriotic lesions. With [111In]In-FnBPA5, the matching targeting molecule is in preclinical development.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI

Pediatric orbital lesions: ocular pathologies.

Orbital pathologies can be broadly classified as ocular, extra-ocular soft-tissue (non-neoplastic and neoplastic), osseous, and traumatic. In part 1 of this orbital series, the authors will discuss the differential diagnosis and key imaging features of pediatric ocular pathologies. These include congenital and developmental lesions (microphthalmos, anophthalmos, persistent fetal vasculature, coloboma, morning glory disc anomaly, retinopathy of prematurity, Coats disease), optic disc drusen, infective and inflammatory lesions (uveitis, toxocariasis, toxoplasmosis), and ocular neoplasms (retinoblastoma, retinal hamartoma, choroidal melanoma, choroidal nevus). This pictorial review provides a practical approach to the imaging work-up of these anomalies with a focus on ocular US as the first imaging modality and additional use of CT and/or MRI for the evaluation of intracranial abnormalities. The characteristic imaging features of the non-neoplastic mimics of retinoblastoma, such as persistent fetal vasculature and Coats disease, are also highlighted.

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities

Background:Deep learning methods have shown great potential in processing multi-modal Magnetic Resonance Imaging (MRI) data, enabling improved accuracy in brain tumor segmentation. However, the performance of these methods can suffer when dealing with incomplete modalities, which is a common issue in clinical practice. Existing solutions, such as missing modality synthesis, knowledge distillation, and architecture-based methods, suffer from drawbacks such as long training times, high model complexity, and poor scalability. Method:This paper proposes IMS2Trans, a novel lightweight scalable Swin Transformer network by utilizing a single encoder to extract latent feature maps from all available modalities. This unified feature extraction process enables efficient information sharing and fusion among the modalities, resulting in efficiency without compromising segmentation performance even in the presence of missing modalities. Results:Two datasets, BraTS 2018 and BraTS 2020, containing incomplete modalities for brain tumor segmentation are evaluated against popular benchmarks. On the BraTS 2018 dataset, our model achieved higher average Dice similarity coefficient (DSC) scores for the whole tumor, tumor core, and enhancing tumor regions (86.57, 75.67, and 58.28, respectively), in comparison with a state-of-the-art model, i.e. mmFormer (86.45, 75.51, and 57.79, respectively). Similarly, on the BraTS 2020 dataset, our model scored higher DSC scores in these three brain tumor regions (87.33, 79.09, and 62.11, respectively) compared to mmFormer (86.17, 78.34, and 60.36, respectively). We also conducted a Wilcoxon test on the experimental results, and the generated p-value confirmed that our model’s performance was statistically significant. Moreover, our model exhibits significantly reduced complexity with only 4.47 M parameters, 121.89G FLOPs, and a model size of 77.13 MB, whereas mmFormer comprises 34.96 M parameters, 265.79 G FLOPs, and a model size of 559.74 MB. These indicate our model, being light-weighted with significantly reduced parameters, is still able to achieve better performance than a state-of-the-art model. Conclusion:By leveraging a single encoder for processing the available modalities, IMS2Trans offers notable scalability advantages over methods that rely on multiple encoders. This streamlined approach eliminates the need for maintaining separate encoders for each modality, resulting in a lightweight and scalable network architecture. The source code of IMS2Trans and the associated weights are both publicly available at https://github.com/hudscomdz/IMS2Trans.

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

Robust semi-automatic segmentation method: an expert assistant tool for muscles in CT and MR data

ABSTRACT Image muscle segmentation is useful to quantitatively assess musculoskeletal diseases by extracting biomarkers such as shape, texture and water diffusivity metrics. Although volumetric manual segmentation is time consuming and a bottleneck in practice, fully automatic approaches are still in progress to reach an acceptable accuracy. In this paper, we provide a robust semi-automated tool to segment two musculoskeletal systems, i.e. thigh and shoulder in MRI and CT modalities, respectively. The tool only needs a few manually labelled cross-sections to build a directed graph-structure of corresponding points between the successive spaced slices. The boundaries of each muscle are obtained by performing a spline interpolation based on the directed graph-structure. Each muscle label and its corresponding 3D mesh are deduced using post-processing techniques. We evaluated the tool on 26 MRI thighs and 16 CT shoulders. Three metrics along with inter-muscle overlapping were employed to evaluate the tool by comparison to an expert manual segmentation and a publicly available tools (ITK-SNAP, 3D Slicer). The results showed a mean Dice 0.988 ± 0.003 , and Hausdorff Distance 4.86 ± 1.67 mm in comparison to the manual reference for thigh muscle segmentation, and a mean Dice 0.961 ± 0.005 and Hausdorff Distance 2.42 ± 0.79 mm for shoulder muscle segmentation, outperformed the other methods. The tool is proposed as slicer module available at https://github.com/latimagine/SlicerSpline.

Novel image-guided marker aimed at organ-preserving therapies for prostate cancer.

We developed fiducial imaging-guidance markers for the prostate with less imaging artifacts than currently commercially available markers. The aim of this study was to evaluate the imaging artifacts and potential usefulness and safety of these novel fiducial imaging markers in preclinical experiments. We selected specific metal materials and a shape that can minimize artifacts in line with a license we obtained for a metal with a gold-platinum (Au-Pt) alloy composition that maximized artifact-free MRI images. Both phantom and canine prostate tests were conducted in order to evaluate the imaging artifacts for three imaging modalities, MRI, CT and ultrasound, and the risk of migration of the markers from the site of insertion to elsewhere, as well as crushing. The newly developed Au-Pt material had less imaging artifacts in the MRI, CT and ultrasound imaging modalities in comparison with current commercially available fiducial markers made from gold materials only. The Au-Pt markers had sufficient strength and durability and were considered to be potentially clinically useful and safe markers. The developed Au-Pt markers could be potential tools for accurate lesion-targeted, organ-preserving therapies such as lesion-targeted focal therapy and active surveillance in addition to conventional radiation therapies.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders

Background:The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).Material and methodsWe analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections. We considered 1383 male subjects with age between 5 and 40 years, including 680 subjects with ASD and 703 TD from 35 different acquisition sites. We extracted morphometric and functional brain features from MRI scans with the Freesurfer and the CPAC analysis packages, respectively. Then, due to the multisite nature of the dataset, we implemented a data harmonization protocol. The ASD vs. TD classification was carried out with a multiple-input DL model, consisting in a neural network which generates a fixed-length feature representation of the data of each modality (FR-NN), and a Dense Neural Network for classification (C-NN). Specifically, we implemented a joint fusion approach to multiple source data integration. The main advantage of the latter is that the loss is propagated back to the FR-NN during the training, thus creating informative feature representations for each data modality. Then, a C-NN, with a number of layers and neurons per layer to be optimized during the model training, performs the ASD-TD discrimination. The performance was evaluated by computing the Area under the Receiver Operating Characteristic curve within a nested 10-fold cross-validation. The brain features that drive the DL classification were identified by the SHAP explainability framework.ResultsThe AUC values of 0.66±0.05 and of 0.76±0.04 were obtained in the ASD vs. TD discrimination when only structural or functional features are considered, respectively. The joint fusion approach led to an AUC of 0.78±0.04. The set of structural and functional connectivity features identified as the most important for the two-class discrimination supports the idea that brain changes tend to occur in individuals with ASD in regions belonging to the Default Mode Network and to the Social Brain.ConclusionsOur results demonstrate that the multimodal joint fusion approach outperforms the classification results obtained with data acquired by a single MRI modality as it efficiently exploits the complementarity of structural and functional brain information.

Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario.

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.

Statistical Methods for Comparing Predictive Values in Medical Diagnosis.

Evaluating the performance of a binary diagnostic test, including artificial intelligence classification algorithms, involves measuring sensitivity, specificity, positive predictive value, and negative predictive value. Particularly when comparing the performance of two diagnostic tests applied on the same set of patients, these metrics are crucial for identifying the more accurate test. However, comparing predictive values presents statistical challenges because their denominators depend on the test outcomes, unlike the comparison of sensitivities and specificities. This paper reviews existing methods for comparing predictive values and proposes using the permutation test. The permutation test is an intuitive, non-parametric method suitable for datasets with small sample sizes. We demonstrate each method using a dataset from MRI and combined modality of mammography and ultrasound in diagnosing breast cancer.

Fetal-BET: Brain Extraction Tool for Fetal MRI.

Goal: In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development in-utero. Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it poses significant challenges due to 1) non-standard fetal head positioning, 2) fetal movements during examination, and 3) vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across gestation, and with various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. Currently, there is no method for accurate fetal brain extraction on various fetal MRI sequences. Methods: In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. These data include images of normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, feature learning across multiple MRI modalities, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. Results: Evaluations on independent test data, including data available from other centers, show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. Conclusions:By leveraging rich information from diverse multi-modality fetal MRI data, our proposed deep learning solution enables precise delineation of the fetal brain on various fetal MRI sequences. The robustness of our deep learning model underscores its potential utility for fetal brain imaging.

Improved Global U-Net applied for multi-modal brain tumor fuzzy segmentation

In this paper, we extended our work from Global U-Net combined with fuzzy amalgamation of Inception Model and Improved Kernel Variation for MRI Brain Image Segmentation [1] which was meant for single modality MRI images only to a brain tumor fuzzy segmentation. Many CNNs gives state of art results for a particular type of images. However, they cannot achieve the same result for the images captured from different imaging techniques. We experimented the Global U-Net model for MRI images earlier and this time we intended to make it applicable for other type of images too using the concept of fuzzy segmentation. The major concern was to overcome the limitations of single modality system that is not all the kernels of U-Net are capable of generating clear feature vectors for different image modalities. The result generated was satisfactory and we would further extend it for colored images.

Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security

Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images. The proposed algorithm outperforms referenced algorithms in both image fidelity and steganographic capacity across diverse medical imaging modalities. It consistently achieves higher Peak Signal-to-Noise Ratio (PSNR) values, indicating superior image fidelity, reduced noise, and preserved signal quality in CT, MRI, ultrasound, and X-ray modalities. The experimental results demonstrate a considerable improvement in both the Peak Signal-to-Noise Ratio (PSNR) and maximum embedding capacity. Specifically, the average PSNR value for the X-ray modality reached a notable 73 dB, signifying superior image quality. Moreover, the CT modality exhibited the highest maximum embedding capacity, measured at 0.52, showcasing its efficiency in accommodating data within the images. Moreover, the algorithm consistently offers increased steganographic data hiding capacity in these images without perceptibly degrading their quality or integrity.

Magnetic Resonance Imaging in Studies of Perifocal Zone of Brain Gliomas (a Literature Review)

This review provides insight into application of modern MRI modalities including diffusion kurtosis imaging in assessment of perifocal glioma zone. Differentiation of “pure” perifocal vasogenic edema from edema infiltrated by glioma cells, as well as identification of peritumoral intact (on conventional MRI) brain matter infiltration make it possible to determine glial tumor borders more accurately. Analysis of diffusion and perfusion quantitative MR data allow determining glioma borders in areas with unaltered blood-brain barrier. There is a growing possibility to develop a personalized navigation algorithm for surgical removal of the tumor, followed by the determination of an individual plan for radiation and chemotherapy, as well as prediction of disease outcomes.

Comparison of Focal Hippocampal T2‐Weighted MRI and Whole‐Brain T1‐Weighted MRI for Detection of Longitudinal Atrophy Using Deep Learning‐Based and Conventional Approaches

AbstractBackgroundLongitudinal measurement of medial temporal lobe (MTL) atrophy is shown to be effective in measuring Alzheimer’s disease (AD) progression and has been extensively studied on T1‐weighted MRI. However, there has been little work analyzing atrophy on high‐resolution T2‐weighted MRI of the hippocampal region, and it is unknown which MRI modality would yield a more effective biomarker of disease progression. We compare atrophy measurements derived from T1 and T2‐weighted MRI using a deep learning framework DeepAtrophy and a deformation‐based morphometry method ALOHA.MethodT2‐weighted MRI of 481 participants from ADNI2/3 are included in the study. DeepAtrophy was trained on 74 participants and evaluated on 407. During training, DeepAtrophy explicitly infers temporal information, such as the scan temporal order for a single scan pair and which image pair has a longer scan time interval for multiple scan pairs. During inference, DeepAtrophy predicts a “total progression score” for each scan pair. This score is expected to be higher (more progression) in individuals with more advanced AD. A similar experiment was conducted using T1‐weighted MRI from 481 ADNI2/GO participants (partially overlapping with T2‐weighted experiment). ALOHA pipelines tailored for T1‐weighted and hippocampal T2‐weighted MRI were evaluated on the same subjects and scan pairs as DeepAtrophy.ResultFor both T1 and T2‐weighted MRI, DeepAtrophy is more accurate, compared with ALOHA, in inferring the temporal order of a pair of longitudinal scans. In hypothetical one‐year and two‐years clinical trials scenarios simulated using ADNI data, DeepAtrophy has an improvement over ALOHA in detecting differences in progression between disease groups in both imaging modalities. The strongest effect for differentiating between Amyloid‐positive and Amyloid‐negative cognitively unimpaired group was obtained using DeepAtrophy progression measures on T2‐weighted MRI.ConclusionThis study supports the potential of high‐resolution focal T2‐weighted MRI of the hippocampus as a longitudinal biomarker of early AD progression suitable for future clinical trials.

Versatile MRI acquisition and processing protocol for population‐based neuroimaging

AbstractBackgroundNeuroimaging plays an essential role in epidemiological studies and a special focus is directed towards the employment of versatile MRI acquisition and processing. The proposed multi‐purpose MRI protocol was designed for large‐scale and long‐term population neuroimaging and includes structural, diffusion‐weighted, and functional MRI modalities. It directly links the acquisition of an extensive set of MRI contrasts with fully automated data processing pipelines and quality assurance of the MRI data and image‐derived phenotypes.MethodThe MRI acquisition protocol is largely based on in‐house developed MR sequences. With a total scan time below one hour per participant, it allows to acquire multiple MR contrasts at 3T with whole‐brain coverage and high isotropic image resolution, while keeping potential subject discomfort and motion artifacts at manageable levels. Designed to be kept constant, but also adaptive, the scan protocol separates into a core imaging protocol with MR contrasts of high relevance acquired for all study participants, and a free protocol to accommodate alternative promising MRI techniques in smaller sub‐populations. The analysis protocol handles large‐scale imaging data by means of fast and fully automated processing pipelines that incorporate state‐of‐the‐art image analysis tools and innovative machine learning methods, particularly using deep learning. Dedicated quality assessment (QA) includes visual rating and inspection for incidental findings on structural MRI and QA workflows tailored for each postprocessing pipeline to automatically identify problematic data based on the distribution of subject‐specific QA metrics with respect to the population average.ResultThe MRI protocol has been successfully applied in the Rhineland Study, a prospective cohort study in Bonn, Germany, with currently over 10,000 participants. Image‐derived phenotypes include: global and regional brain tissue volume, thickness and surface measures from multi‐modal structural MRI, global and regional microstructural measures based on diffusion‐weighted MRI, brain functional connectivity using resting‐state functional MRI, and volumes of subcutaneous and visceral adipose tissue based on a single‐breathhold abdominal MRI (Figure 1).ConclusionWe present a versatile MRI protocol with acquisition and analysis methods that are generally applicable and not geared towards a specific disease or research question. Thus, this protocol may be of specific interest for many neuroimaging applications including population imaging studies.

Versatile MRI acquisition and processing protocol for population‐based neuroimaging

AbstractBackgroundNeuroimaging plays an essential role in epidemiological studies and a special focus is directed towards the employment of versatile MRI acquisition and processing. The proposed multi‐purpose MRI protocol was designed for large‐scale and long‐term population neuroimaging and includes structural, diffusion‐weighted, and functional MRI modalities. It directly links the acquisition of an extensive set of MRI contrasts with fully automated data processing pipelines and quality assurance of the MRI data and image‐derived phenotypes.MethodThe MRI acquisition protocol is largely based on in‐house developed MR sequences. With a total scan time below one hour per participant, it allows to acquire multiple MR contrasts at 3T with whole‐brain coverage and high isotropic image resolution, while keeping potential subject discomfort and motion artifacts at manageable levels. Designed to be kept constant, but also adaptive, the scan protocol separates into a core imaging protocol with MR contrasts of high relevance acquired for all study participants, and a free protocol to accommodate alternative promising MRI techniques in smaller sub‐populations. The analysis protocol handles large‐scale imaging data by means of fast and fully automated processing pipelines that incorporate state‐of‐the‐art image analysis tools and innovative machine learning methods, particularly using deep learning. Dedicated quality assessment (QA) includes visual rating and inspection for incidental findings on structural MRI and QA workflows tailored for each postprocessing pipeline to automatically identify problematic data based on the distribution of subject‐specific QA metrics with respect to the population average.ResultThe MRI protocol has been successfully applied in the Rhineland Study, a prospective cohort study in Bonn, Germany, with currently over 10,000 participants. Image‐derived phenotypes include: global and regional brain tissue volume, thickness and surface measures from multi‐modal structural MRI, global and regional microstructural measures based on diffusion‐weighted MRI, brain functional connectivity using resting‐state functional MRI, and volumes of subcutaneous and visceral adipose tissue based on a single‐breathhold abdominal MRI (Figure 1).ConclusionWe present a versatile MRI protocol with acquisition and analysis methods that are generally applicable and not geared towards a specific disease or research question. Thus, this protocol may be of specific interest for many neuroimaging applications including population imaging studies.

Interictal magnetoencephalography abnormalities to guide intracranial electrode implantation and predict surgical outcome.

Intracranial EEG is the gold standard technique for epileptogenic zone localization but requires a preconceived hypothesis of the location of the epileptogenic tissue. This placement is guided by qualitative interpretations of seizure semiology, MRI, EEG and other imaging modalities, such as magnetoencephalography. Quantitative abnormality mapping using magnetoencephalography has recently been shown to have potential clinical value. We hypothesized that if quantifiable magnetoencephalography abnormalities were sampled by intracranial EEG, then patients' post-resection seizure outcome may be better. Thirty-two individuals with refractory neocortical epilepsy underwent magnetoencephalography and subsequent intracranial EEG recordings as part of presurgical evaluation. Eyes-closed resting-state interictal magnetoencephalography band power abnormality maps were derived from 70 healthy controls as a normative baseline. Magnetoencephalography abnormality maps were compared to intracranial EEG electrode implantation, with the spatial overlap of intracranial EEG electrode placement and cerebral magnetoencephalography abnormalities recorded. Finally, we assessed if the implantation of electrodes in abnormal tissue and subsequent resection of the strongest abnormalities determined by magnetoencephalography and intracranial EEG corresponded to surgical success. We used the area under the receiver operating characteristic curve as a measure of effect size. Intracranial electrodes were implanted in brain tissue with the most abnormal magnetoencephalography findings-in individuals that were seizure-free postoperatively (T = 3.9, P = 0.001) but not in those who did not become seizure-free. The overlap between magnetoencephalography abnormalities and electrode placement distinguished surgical outcome groups moderately well (area under the receiver operating characteristic curve = 0.68). In isolation, the resection of the strongest abnormalities as defined by magnetoencephalography and intracranial EEG separated surgical outcome groups well, area under the receiver operating characteristic curve = 0.71 and area under the receiver operating characteristic curve = 0.74, respectively. A model incorporating all three features separated surgical outcome groups best (area under the receiver operating characteristic curve = 0.80). Intracranial EEG is a key tool to delineate the epileptogenic zone and help render individuals seizure-free postoperatively. We showed that data-driven abnormality maps derived from resting-state magnetoencephalography recordings demonstrate clinical value and may help guide electrode placement in individuals with neocortical epilepsy. Additionally, our predictive model of postoperative seizure freedom, which leverages both magnetoencephalography and intracranial EEG recordings, could aid patient counselling of expected outcome.