Magnetic Resonance Imaging Acquisition Research Articles

Brain Tumour Temporal Monitoring of Interval Change Using Digital Image Subtraction Technique.

A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.

Spend time outdoors for your brain – an in-depth longitudinal MRI study

Objectives The effects of nature on physical and mental health are an emerging topic in empirical research with increasing influence on practical health recommendations. Here we set out to investigate the association between spending time outdoors and brain structural plasticity in conjunctions with self-reported affect. Methods We established the Day2day study, which includes an unprecedented in-depth assessment of variability of brain structure in a serial sequence of 40–50 structural magnetic resonance imaging (MRI) acquisitions of each of six young healthy participants for 6–8 months (n = 281 MRI scans in total). Results A whole-brain analysis revealed that time spent outdoors was positively associated with grey matter volume in the right dorsolateral prefrontal cortex and positive affect, also after controlling for physical activity, fluid intake, free time, and hours of sunshine. Conclusions Results indicate remarkable and potentially behaviorally relevant plasticity of cerebral structure within a short time frame driven by the daily time spent outdoors. This is compatible with anecdotal evidence of the health and mood-promoting effects of going for a walk. The study may provide the first evidence for underlying cerebral mechanisms of so-called green prescriptions with possible consequences for future interventions in mental disorders.

123I]MIBG is a better early marker of anthracycline cardiotoxicity than [18F]FDG: a preclinical SPECT/CT and simultaneous PET/MR study

BackgroundDuring anthracycline treatment of cancer, there is a lack for biomarkers of cardiotoxicity besides the cardiac dysfunction. The objective of the present study was to compare [18F]FDG and [123I]MIBG (metaiodobenzylguanidine) in a longitudinal study in a doxorubicin-induced cardiotoxicity rat model.MethodsMale Wistar Han rats were intravenously administered 3 times at 10 days’ interval with saline or doxorubicin (5 mg/kg). [123I]MIBG SPECT/CT (single photon emission computed tomography-computed tomography) and simultaneous [18F]FDG PET (positron emission tomography)/7 Tesla cardiac MR (magnetic resonance) imaging acquisitions were performed at 24 h interval before first doxorubicin / saline injection and every 2 weeks during 6 weeks. At 6 weeks, the heart tissue was collected for histomorphometry measurements.ResultsAt week 4, left ventricle (LV) end-diastolic volume was significantly reduced in the doxorubicin group. At week 6, the decreased LV end-diastolic volume was maintained, and LV end-systolic volume was increased resulting in a significant reduction of LV ejection fraction (47 ± 6% vs. 70 ± 3%). At weeks 4 and 6, but not at week 2, myocardial [18F]FDG uptake was decreased compared with the control group (respectively, 4.2 ± 0.5%ID/g and 9.2 ± 0.8%ID/g at week 6). Moreover, [18F]FDG cardiac uptake correlated with cardiac function impairment. In contrast, from week 2, a significant decrease of myocardial [123I]MIBG heart to mediastinum ratio was detected in the doxorubicin group and was maintained at weeks 4 and 6 with a 45.6% decrease at week 6.ConclusionThis longitudinal study precises that after doxorubicin treatment, cardiac [123I]MIBG uptake is significantly reduced as early as 2 weeks followed by the decrease of the LV end-diastolic volume and [18F]FDG uptake at 4 weeks and finally by the increase of LV end-systolic volume and decrease of LV ejection fraction at 6 weeks. Cardiac innervation imaging should thus be considered as an early key feature of anthracycline cardiac toxicity.

Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy.

The acquisition of multiparametric quantitative magnetic resonance imaging (qMRI) is becoming increasingly important for functional characterization of cancer prior to- and throughout the course of radiation therapy. The feasibility of a qMRI method known as magnetic resonance fingerprinting (MRF) for rapid T1 and T2 mapping was assessed on a low-field MR-linac system. A three-dimensional MRF sequence was implemented on a 0.35T MR-guided radiotherapy system. MRF-derived measurements of T1 and T2 were compared to those obtained with gold standard single spin echo methods, and the impacts of the radiofrequency field homogeneity and scan times ranging between 6 and 48 min were analyzed by acquiring between 1 and 8 spokes per time point in a standard quantitative system phantom. The short-term repeatability of MRF was assessed over three measurements taken over a 10-h period. To evaluate transferability, MRF measurements were acquired on two additional MR-guided radiotherapy systems. Preliminary human volunteer studies were performed. The phantom benchmarking studies showed that MRF is capable of mapping T1 and T2 values within 8% and 10% of gold standard measures, respectively, at 0.35T. The coefficient of variation of T1 and T2 estimates over three repeated scans was<5% over a broad range of relaxation times. The T1 and T2 times derived using a single-spoke MRF acquisition across three scanners were near unity and mean percent errors in T1 and T2 estimates using the same phantom were<3%. The mean percent differences in T1 and T2 as a result of truncating the scan time to 6 min over the large range of relaxation times in the system phantom were 0.65% and 4.05%, respectively. The technical feasibility and accuracy of MRF on a low-field MR-guided radiation therapy device has been demonstrated. MRF can be used to measure accurate T1 and T2 maps in three dimensions from a brief 6-min scan, offering strong potential for efficient and reproducible qMRI for future clinical trials in functional plan adaptation and tumor/normal tissue response assessment.

Diagnosis of AD Based on sMRI Whole Brain Segmentation

With the advent of convolutional neural networks (CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging (MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as T1-weighted and T2-weighted contrasts with only T1-weighted training data. The segmentations generated are highly accurate with state-of-the-art results (overall Dice overlap=0.94), with a fast run time (≈ 45 s), and consistent across a wide range of acquisition protocols.

Load-Sharing Classification Score as Supplemental Grading System in the Decision-Making Process for Patients With Thoracolumbar Injury Classification and Severity 4.

Patients with Thoracolumbar Injury Classification and Severity (TLICS) score of 4 fall into a gray zone between surgical and conservative management. The integrity of posterior ligamentous complex (PLC) evaluated by magnetic resonance imaging (MRI) contributes to surgical decision-making. Load-sharing classification (LSC) may provide a modifier to further guide decision-making in these patients. To evaluate associations between LSC score and MRI acquisition, compromise of PLC on MRI, and surgical intervention in TLICS 4 patients. A cohort of 111 neurologically intact patients with isolated thoracolumbar burst fracture with TLICS 4 was evaluated. LSC score was determined based on degree of comminution (1-3), apposition (1-3), and kyphosis (1-3), total composite score of 3 to 9. Overall, 44 patients underwent MRI, 15 had PLC injury, and 32 (28.8%) underwent surgery. LSC score was higher in patients who had an MRI (median 6vs 3, P <.001) and patients who had surgery (median 7vs 4, P <.001). In univariate logistic regression, LSC score was associated with MRI acquisition (odds ratio [OR] 1.7; 1.32-2.12; P <.001), presence of PLC injury on MRI (OR 1.5; 1.2-2.0; P=.002) and, in multivariate logistic regression, undergoing surgical intervention (OR 3.7; 2.3-5.9; P <.001), independent of MRI or PLC injury. LSC score in neurologically intact patients with isolated thoracolumbar burst fracture with TLICS 4 was independently associated with operative intervention. The application of LSC may further guide decision-making in this patient group.

Deep Adaptive Blending Network for 3D Magnetic Resonance Image Denoising.

The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.

A systematic review of brain MRI findings in monogenic disorders strongly associated with autism spectrum disorder

Research on monogenic forms of autism spectrum disorder (autism) can inform our understanding of genetic contributions to the autism phenotype; yet, there is much to be learned about the pathways from gene to brain structure to behavior. This systematic review summarizes and evaluates research on brain magnetic resonance imaging (MRI) findings in monogenic conditions that have strong association with autism. This will improve understanding of the impact of genetic variability on brain structure and related behavioral traits in autism. The search strategy for this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias (ROB) assessment was completed on included studies using the Newcastle-Ottawa Scales. Of 4,287 studies screened, 69 were included pertaining to 13 of the top 20 genes with the strongest association with autism. The greatest number of studies related to individuals with PTEN variants and autism. Brain MRI abnormalities were reported for 12 of the 13 genes studied, and in 51.7% of participants across all 13 genes, including 100% of participants with ARID1B variants. Specific MRI findings were highly variable, with no clear patterns emerging within or between the 13 genes, although white matter abnormalities were the most common. Few studies reported specific details about methods for acquisition and processing of brain MRI, and descriptors for brain abnormalities were variable. ROB assessment indicated high ROB for all studies, largely due to small sample sizes and lack of comparison groups. Brain abnormalities are common in this population of individuals, in particular, children; however, a range of different brain abnormalities were reported within and between genes. Directions for future neuroimaging research in monogenic autism are suggested.

Generic acquisition protocol for quantitative MRI of the spinal cord.

Quantitative spinal cord (SC) magnetic resonance imaging (MRI) presents many challenges, including a lack of standardized imaging protocols. Here we present a prospectively harmonized quantitative MRI protocol, which we refer to as the spine generic protocol, for users of 3T MRI systems from the three main manufacturers: GE, Philips and Siemens. The protocol provides guidance for assessing SC macrostructural and microstructural integrity: T1-weighted and T2-weighted imaging for SC cross-sectional area computation, multi-echo gradient echo for gray matter cross-sectional area, and magnetization transfer and diffusion weighted imaging for assessing white matter microstructure. In a companion paper from the same authors, the spine generic protocol was used to acquire data across 42 centers in 260 healthy subjects. The key details of the spine generic protocol are also available in an open-access document that can be found at https://github.com/spine-generic/protocols . The protocol will serve as a starting point for researchers and clinicians implementing new SC imaging initiatives so that, in the future, inclusion of the SC in neuroimaging protocols will be more common. The protocol could be implemented by any trained MR technician or by a researcher/clinician familiar with MRI acquisition.

Metabolomic Biomarkers Are Associated With Area of the Pons in Fragile X Premutation Carriers at Risk for Developing FXTAS.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late adult-onset neurodegenerative disorder that affects movement and cognition in male and female carriers of a premutation allele (55–200 CGG repeats; PM) in the fragile X mental retardation (FMR1) gene. It is currently unknown how the observed brain changes are associated with metabolic signatures in individuals who develop the disorder over time. The primary objective of this study was to investigate the correlation between longitudinal changes in the brain (area of the pons, midbrain, and MCP width) and the changes in the expression level of metabolic biomarkers of early diagnosis and progression of FXTAS in PM who, as part of an ongoing longitudinal study, emerged into two distinct categories. These included those who developed symptoms of FXTAS (converters, CON) at subsequent visits and those who did not meet the criteria of diagnosis (non-converters, NCON) and were compared to age-matched healthy controls (HC). We assessed CGG repeat allele size by Southern Blot and PCR analysis. Magnetic Resonance Imaging (MRIs) acquisition was obtained on a 3T Siemens Trio scanner and metabolomic profile was obtained by ultra-performance liquid chromatography, accurate mass spectrometer, and an Orbitrap mass analyzer. Our findings indicate that differential metabolite levels are linked with the area of the pons between healthy control and premutation groups. More specifically, we observed a significant association of ceramides and mannonate metabolites with a decreased area of the pons, both at visit 1 (V1) and visit 2 (V2) only in the CON as compared to the NCON group suggesting their potential role in the development of the disorder. In addition, we found a significant correlation of these metabolic signatures with the FXTAS stage at V2 indicating their contribution to the progression and pathogenesis of FXTAS. Interestingly, these metabolites, as part of lipid and sphingolipid lipids pathways, provide evidence of the role that their dysregulation plays in the development of FXTAS and inform us as potential targets for personalized therapeutic development.

Iron Oxide Incorporated Conjugated Polymer Nanoparticles for Simultaneous Use in Magnetic Resonance and Fluorescent Imaging of Brain Tumors.

Conjugated polymer nanoparticles (CPNs) have emerged as advanced polymeric nanoplatforms in biomedical applications by virtue of extraordinary properties including high fluorescence brightness, large absorption coefficients of one and two-photons, and excellent photostability and colloidal stability in water and physiological medium. In addition, low cytotoxicity, easy functionalization, and the ability to modify CPN photochemical properties by the incorporation of dopants, convert them into excellent theranostic agents with multifunctionality for imaging and treatment. In this work, CPNs were designed and synthesized by incorporating a metal oxide magnetic core (Fe3O4 and NiFe2O4 nanoparticles, 5 nm) into their matrix during the nanoprecipitation method. This modification allowed the in vivo monitoring of nanoparticles in animal models using magnetic resonance imaging (MRI) and intravital fluorescence, techniques widely used for intracranial tumors evaluation. The modified CPNs were assessed in vivo in glioblastoma (GBM) bearing mice, both heterotopic and orthotopic developed models. Biodistribution studies were performed with MRI acquisitions and fluorescence images up to 24 h after the i.v. nanoparticles administration. The resulting IONP-doped CPNs were biocompatible in GBM tumor cells in vitro with an excellent cell incorporation depending on nanoparticle concentration exposure. IONP-doped CPNs were detected in tumor and excretory organs of the heterotopic GBM model after i.v. and i.t. injection. However, in the orthotopic GBM model, the size of the nanoparticles is probably hindering a higher effect on intratumorally T2-weighted images (T2WI) signals and T2 values. The photodynamic therapy (PDT)—cytotoxicity of CPNs was not either affected by the IONPs incorporation into the nanoparticles.

Dissecting whole-brain conduction delays through MRI microstructural measures

Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.

Clinical Parameters Reflecting Globe/orbit Volume Imbalances in Japanese Acquired Esotropia Patients with High Myopia but without Abduction Limitations.

In high myopia, eye dislocation due to increased globe volume or tight orbital volume causes acquired esotro-pia. GOR (globe/orbit volume ratio), an indicator of the degree of progression of this pathology, was investi-gated the relationships among easily obtained clinical parameters. In this retrospective study, 20 eyes from 10 acquired esotropia patients with high myopia but without abduction limitations were examined. The mean age of the patients was 63.7 ± 8.2 years (mean ± standard deviation). Volumes were measured on the three-dimen-sional fast imaging employing steady-state acquisition magnetic resonance imaging images using the vol-ume-measurement function. Correlations between GOR and the displacement angle of the globe (DA), axial length (AL), and equatorial diameter (ED) were investigated. Mean DA, AL, ED, and GOR values were 107.5 ± 8.5°, 28.86 ± 1.92 mm, 25.00 ± 1.16 mm, and 0.36 ± 0.05, respectively. Only AL was correlated with GOR (p < 0.0001, R2 = 0.6649); DA (p = 0.30, R2 = 0.0633) and ED (p = 0.91, R2 = 0.0008) were not. AL was the only clinically available parameter to indicate globe/orbit volume imbalances in acquired esotropia with high myopia but without abduction limitation. AL may be important for the clinical assessment of the progression of this pathology.

Usefulness of three-dimensional fast imaging employing steady-state acquisition MRI of large vessel occlusion for detecting occluded middle cerebral artery and internal carotid artery before acute mechanical thrombectomy.

Objective Acute mechanical thrombectomy (AMT) in patients with acute ischemic stroke from large vessel occlusion (LVO) is performed without directly identifying the occluded vessels. In this study, we evaluated whether 1.5 T magnetic resonance imaging (MRI) with 3D-fast imaging employing steady-state acquisition (FIESTA) could visualize the occluded intracranial middle cerebral artery (MCA) and internal carotid artery (ICA) before AMT.Methods This retrospective study included 21 consecutive patients who underwent time-of-flight magnetic resonance angiography (TOF MRA) and 3D-FIESTA MRI immediately before AMT. The patients also underwent TOF MRA after AMT and achieved TICI 2b or 3 by AMT at our hospital between February 2018 and April 2019. When LVO in the anterior circulation was detected by TOF MRA, 3D-FIESTA MRI was additionally performed. Then, the occluded intracranial MCA and ICA, including their branches, were constructed on the workstation with volume rendering. The obtained images were fused with the TOF MRA images to create combined 3D images.Results The length and top-to-bottom distance of the affected M1 segment (calculated by the ipsilateral-to-contralateral ratio) were 1.29 and 1.17, respectively, on 3D-FIESTA MRI before AMT and 1.34 and 1.24, respectively, on TOF MRA after AMT. We assessed the number of M2 segments branching from the affected M1/M2 junction and visualized the affected anterior temporal artery. The 3D-FIESTA MRI before AMT and TOF MRA after AMT were consistent in all patients, except for two who moved vigorously during imaging.Conclusions Images acquired by 1.5T 3D-FIESTA MRI can visualize to predict the existing path of the occluded MCA and ICA before AMT in patients with LVO of the anterior circulation.

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the “ground truth” data to which the output of a given processing method must be compared. One way to address this issue is via “digital phantoms”, which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging

Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power.

Deep Learning-Based Localization of EEG Electrodes Within MRI Acquisitions.

The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.

DOES FEAR OF MOVEMENT ALTER BRAIN ACTIVITY? INVESTIGATING THE NEURAL MARKERS OF KINESIOPHOBIA IN PEDIATRIC PATIENTS WITH PATELLOFEMORAL PAIN

Background:Patellofemoral pain (PFP) is a chronic knee condition that inhibits movement quality and can cascade into kinesiophobia (i.e., fear of pain/movement). Despite its high prevalence in adolescent girls, PFP etiology has remained elusive, potentially due to an incomplete understanding of how pain, motor control, and kinesiophobia interact with central nervous system (CNS) functioning. Discovering linkages between motor control, kinesiophobia, and the CNS for patients with PFP could provide novel opportunities to refine neural therapeutic strategies for enhanced, personalized treatment approaches.Hypothesis/Purpose:To identify neural markers of kinesiophobia in pediatric patients with PFP.Methods:Adolescent girls clinically diagnosed with PFP (n = 14; 14.3 ± 3.2 yrs) were positioned supine in a magnetic resonance imaging (MRI) scanner. A modified Clarke’s test (patellofemoral grind test) was administered to the left knee of patients during brain functional MRI (fMRI) acquisition. The experimenter placed a hand at the superior patellar border applying intermittent distal pressure while the patient contracted her quadriceps. Patients also completed a quadriceps contraction test without application of external pressure (control). Kinesiophobia was measured using the Tampa Scale of Kinesiophobia. fMRI analyses compared brain activation between the two tasks, and correlation analyses were performed to assess relationships between task-induced brain activity and kinesiophobia. Statistical corrections were made to account for multiple, voxel-wise comparisons.Results:Evidenced in Table 1 and Figure 1A, neuroimaging analyses revealed distinct bidirectional activation during the Clarke’s test compared to the control (all p corrected < .05, all z max > 3.1). Table 1 and Figure 1B further highlight that greater kinesiophobia was associated with increased brain activity during the Clarke’s test in two clusters (both p corrected < .05, both z max > 3.1), but no relationships between kinesiophobia and brain activity during the control test were observed (all p corrected > .05, all z max < 3.1).Conclusion:The current results indicate that the Clarke’s test induced differential pain-related brain activity relative to the control (e.g., paracingulate gyrus). The patients’ degree of kinesiophobia was also related to brain activity in regions important for sensorimotor control, attention, and pain. Collectively, these data indicate that PFP may be due to alterations in nociceptive processing throughout the CNS, providing novel complementary pathways for targeted restoration of patellofemoral joint dysfunction. Future research should consider combined mechanistic pain profiling, sensorimotor, psychometric, and CNS assessments to identify patients most susceptible to kinesiophobia to refine treatment approaches that work towards a goal of disease prevention.Tables/Figures:Figure 1: A:.Greater activation during the modified Clarke’s test compared to the control test is colored red and less activation during the Clarke’s test compared to the control test colored blue. B: Green indicates regions for which higher kinesiophobia was positively associated with increased activity during the Clarke’s test. All image slices are shown in neurological convention and identified by the respective Z coordinate in Montreal neurological institute space. Cluster and anatomical details are provided in Table 1.

Determination of Non-Invasive Biomarkers for the Assessment of Fibrosis, Steatosis and Hepatic Iron Overload by MR Image Analysis. A Pilot Study

The reference diagnostic test of fibrosis, steatosis, and hepatic iron overload is liver biopsy, a clear invasive procedure. The main objective of this work was to propose HSA, or human serum albumin, as a biomarker for the assessment of fibrosis and to study non-invasive biomarkers for the assessment of steatosis and hepatic iron overload by means of an MR image acquisition protocol. It was performed on a set of eight subjects to determine fibrosis, steatosis, and hepatic iron overload with four different MRI sequences. We calibrated longitudinal relaxation times (T1 [ms]) with seven human serum albumin (HSA [%]) phantoms, and we studied the relationship between them as this protein is synthesized by the liver, and its concentration decreases in advanced fibrosis. Steatosis was calculated by means of the fat fraction (FF [%]) between fat and water liver signals in “fat-only images” (the subtraction of in-phase [IP] images and out-of-phase [OOP] images) and in “water-only images” (the addition of IP and OOP images). Liver iron concentration (LIC [µmol/g]) was obtained by the transverse relaxation time (T2* [ms]) using Gandon’s method with multiple echo times (TE) in T2-weighted IP and OOP images. The preliminary results showed that there is an inverse relationship (r = −0.9662) between the T1 relaxation times (ms) and HSA concentrations (%). Steatosis was determined with FF > 6.4% and when the liver signal was greater than the paravertebral muscles signal, and thus, the liver appeared hyperintense in fat-only images. Hepatic iron overload was detected with LIC > 36 µmol/g, and in these cases, the liver signal was smaller than the paravertebral muscles signal, and thus, the liver behaved as hypointense in IP images.

Imaging of tumor predisposition syndromes

Tumor predisposition syndromes (TPS) are aheterogeneous group of genetic cancers. About 10% of the approximately 2200 malignancies in the childhood in Germany develop due to an inherited disposition, whereby TPS may be underdiagnosed. The focus of this review is set on imaging of Li-Fraumeni syndrome, neurofibromatoses, tuberous sclerosis, overgrowth, and neuroendocrine syndromes. In order to detect tumors at an early stage, screening at specific time intervals for each TPS are required. Ultrasonography and magnetic resonance imaging (MRI), especially whole-body MRI, are particularly important imaging modalities. Innovative MRI techniques can increase image quality and patient comfort. MRI acquisition time can be significantly reduced through optimized acceleration factors, motion robust radial sequences and joint acquisition and readout of multiple slices during excitation. Thus, shorter MRI examinations can be performed in younger children without anesthesia. Regular screening with ultrasound and MRI can reduce the morbidity and mortality of the patients affected with TPS.