scholarly journals An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging

2019 ◽  
Author(s):  
Horea-Ioan Ioanas ◽  
Markus Marks ◽  
Valerio Zerbi ◽  
Mehmet Fatih Yanik ◽  
Markus Rudin
Keyword(s):  
Small Animal ◽  
Work Flow ◽  
Imaging Data ◽  
Reference Space ◽  
Geometric Space ◽  
Scientific Results ◽  
Magnetic Resonance Imaging Mri ◽  
Small Animal Mri ◽  
High Level ◽  
Automated Quality Control

AbstractThe reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic work-flow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We present four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 2-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring work-flow robustness, data comparability, and region assignment validity, all of which are indispensable prerequisites for the comparability of scientific results across experiments and centers.

scholarly journals MRI Powered and Triggered Current Stimulator for Concurrent Stimulation and MRI

2019 ◽  
Author(s):  
Ranajay Mandal ◽  
Nishant Babaria ◽  
Jiayue Cao ◽  
Kun-Han Lu ◽  
Zhongming Liu
Keyword(s):  
Scale Up ◽  
Small Animal ◽  
7 Tesla ◽  
Planar Imaging ◽  
Novel Device ◽  
Peripheral Muscle ◽  
Gradient Fields ◽  
Magnetic Resonance Imaging Mri ◽  
Small Animal Mri ◽  
Echo Planar

AbstractBioelectric stimulation during concurrent magnetic resonance imaging (MRI) is of interest to basic and translational studies. However, existing stimulation systems often interfere with MRI, are difficult to use or scale up, have limited efficacy, or cause safety concerns. To address these issues, we present a novel device capable of supplying current stimulation synchronized with MRI while being wirelessly powered by the MRI gradient fields. Results from testing it with phantoms and live animals in a 7 Tesla small-animal MRI system suggest that the device is able to harvest up to 72 (or 18) mW power during typical echo-planar imaging (or fast low angle shot imaging) and usable for stimulating peripheral muscle or nerve to modulate the brain or the gut, with minimal effects on MRI image quality. As a compact and standalone system, the plug-and-play device is suitable for animal research and merits further development for human applications.

scholarly journals Designing a compact MRI motion phantom

2016 ◽  
Vol 2 (1) ◽  
pp. 471-474
Author(s):  
Max Schmiedel ◽  
Anita Moeller ◽  
Martin A. Koch ◽  
Alfred Mertins
Keyword(s):  
Motion Compensation ◽  
Rigid Motion ◽  
Small Animal ◽  
Ground Truth ◽  
Motion Artifacts ◽  
Body Motion ◽  
Retrospective Gating ◽  
Motion Phantom ◽  
Magnetic Resonance Imaging Mri ◽  
Small Animal Mri

AbstractEven today, dealing with motion artifacts in magnetic resonance imaging (MRI) is a challenging task. Image corruption due to spontaneous body motion complicates diagnosis. In this work, an MRI phantom for rigid motion is presented. It is used to generate motion-corrupted data, which can serve for evaluation of blind motion compensation algorithms. In contrast to commercially available MRI motion phantoms, the presented setup works on small animal MRI systems. Furthermore, retrospective gating is performed on the data, which can be used as a reference for novel motion compensation approaches. The motion of the signal source can be reconstructed using motor trigger signals and be utilized as the ground truth for motion estimation. The proposed setup results in motion corrected images. Moreover, the importance of preprocessing the MRI raw data, e.g. phase-drift correction, is demonstrated. The gained knowledge can be used to design an MRI phantom for elastic motion.

Top-Ten Tips for Imaging the Brachial Plexus with Ultrasound and MRI

2019 ◽  
Vol 23 (04) ◽  
pp. 405-418 ◽  
Author(s):  
James F. Griffith ◽  
Radhesh Krishna Lalam
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brachial Plexus ◽  
Muscle Denervation ◽  
Resonance Imaging ◽  
Neural Injury ◽  
Clinical Indications ◽  
Magnetic Resonance Imaging Mri ◽  
High Level ◽  
Extensive Involvement

AbstractWhen it comes to examining the brachial plexus, ultrasound (US) and magnetic resonance imaging (MRI) are complementary investigations. US is well placed for screening most extraforaminal pathologies, whereas MRI is more sensitive and accurate for specific clinical indications. For example, MRI is probably the preferred technique for assessment of trauma because it enables a thorough evaluation of both the intraspinal and extraspinal elements, although US can depict extraforaminal neural injury with a high level of accuracy. Conversely, US is probably the preferred technique for examination of neurologic amyotrophy because a more extensive involvement beyond the brachial plexus is the norm, although MRI is more sensitive than US for evaluating muscle denervation associated with this entity. With this synergy in mind, this review highlights the tips for examining the brachial plexus with US and MRI.

scholarly journals Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo-yong Park ◽  
Seok-Jun Hong ◽  
Sofie L. Valk ◽  
Casey Paquola ◽  
Oualid Benkarim ◽  
...  
Keyword(s):  
Data Exchange ◽  
Computational Models ◽  
Simulation Models ◽  
Autism Diagnostic Observation Schedule ◽  
Supervised Machine Learning ◽  
Global Network ◽  
Imaging Data ◽  
Phenotypic Data ◽  
Severity Scores ◽  
High Level

AbstractThe pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

scholarly journals An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging

NeuroImage ◽  
2021 ◽  
pp. 118386
Author(s):  
Horea-Ioan Ioanas ◽  
Markus Marks ◽  
Valerio Zerbi ◽  
Mehmet Fatih Yanik ◽  
Markus Rudin
Keyword(s):  
Brain Imaging ◽  
Small Animal ◽  
Animal Brain ◽  
Geometric Space

scholarly journals Complete heart block in cardiac sarcoidosis reversed by corticosteroid therapy: time course of resolution

2021 ◽  
Vol 14 (3) ◽  
pp. e240834
Author(s):  
Anna Tomdio ◽  
Huzaefah Syed ◽  
Kenneth Ellenbogen ◽  
Jordana Kron
Keyword(s):  
Heart Block ◽  
Time Course ◽  
Cardiac Sarcoidosis ◽  
Complete Heart Block ◽  
Positron Emission ◽  
Av Conduction ◽  
Magnetic Resonance Imaging Mri ◽  
High Level ◽  
Active Inflammation ◽  
Lateral Walls

A 53-year-old man was admitted for recurrent syncope and found to have complete heart block (CHB). Cardiac magnetic resonance imaging MRI) showed extensive patchy late gadolinium enhancement in the apical and lateral walls, consistent with cardiac sarcoidosis (CS) but no scar in the septum. A fluorodeoxyglucose (FDG)–positron emission tomography showed FDG uptake in the septum and basal lateral walls. Imaging suggested active inflammation in the septum affecting atrioventricular (AV) conduction but no irreversible fibrosis. Diagnosis of isolated CS requires a high level of suspicion and multidisciplinary teamwork involving heart failure specialists, electrophysiologists and rheumatologists. After specialist and patient discussion, treatment of the disease was initiated with prednisone 40 mg daily, 11 months after presenting with CHB. Three weeks later, ECG with pacing inhibited showed second-degree AV block Mobitz type II and 4 weeks later, AV conduction recovery. This highlights the importance of immediate therapy in reversing AV conduction abnormalities in CS.

scholarly journals Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 742
Author(s):  
Rima Hajjo ◽  
Dima A. Sabbah ◽  
Sanaa K. Bardaweel ◽  
Alexander Tropsha
Keyword(s):  
Machine Learning ◽  
High Specificity ◽  
Cancer Biomarkers ◽  
Cancer Prognosis ◽  
Current Status ◽  
Imaging Data ◽  
Non Invasive ◽  
Machine Learning Methods ◽  
Cancer Types ◽  
Magnetic Resonance Imaging Mri

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Seronegative Spondyloarthritis: Epidemiology, Pathogenesis, And Pathology

2018 ◽  
Author(s):  
Walter P Maksymowych
Keyword(s):  
Association Studies ◽  
Human Leukocyte ◽  
Hla Class I ◽  
Population Based ◽  
Cell Phenotype ◽  
Imaging Data ◽  
Leukocyte Antigen ◽  
Seronegative Spondyloarthritis ◽  
Magnetic Resonance Imaging Mri

Classification of spondyloarthritis (SpA) is aimed at including patients with radiographic evidence of sacroiliitis and those with early disease who do not yet meet radiographic criteria but have positive features on magnetic resonance imaging (MRI). Most studies report a prevalence of SpA of 0.1 to 0.6%. Human leukocyte antigen (HLA)-B27 contributes approximately 20% of the heritability of SpA, and non–major histocompatibility complex loci identified to date (n = 113) contribute another approximately 10%. To date, 160 subtypes of HLA-B*27 have been reported, although population-based disease association studies are limited to only a few subtypes. Subtypes HLA-B*27:05 and HLA-B*27:04 are examples of subtypes associated with disease, whereas HLA-B*27:06 and HLA-B*27:09 are nonassociated. Properties of the B27 molecule relevant to pathogenesis include antigen presentation, propensity to misfold, and formation of homodimers. Key pathways identified by genetic studies include the interleukin (IL)-23 and M1-aminopeptidase pathways. The latter pathway is involved in peptide trimming in the endoplasmic reticulum, changing both the length and amino acid composition of peptides available for HLA class I presentation. IL-23 is a key cytokine regulating expression of IL-17 in a specific T helper cell phenotype, Th17, and also a variety of cells of the innate immune system. The IL-23–IL-17 pathway has been directly implicated in inflammation at sites that are inflamed in SpA. Increasing evidence based on prospective clinical and imaging data supports a link between inflammation and ankylosis, especially if the resolution of inflammation is followed by the appearance of a particular type of reparative tissue, namely, fat metaplasia, on T1-weighted MRI. This review contains 8 figures, 5 tables and 33 references Key words: association, classification, genetics, heritability, innate immunity, prevalence, spondyloarthritis

Biomedical Image Processing Overview

2016 ◽  
pp. 1-12 ◽  
Author(s):  
Monia Mannai Mannai ◽  
Wahiba Ben Abdessalem Karâa
Keyword(s):  
Image Processing ◽  
Medical Imaging ◽  
Medical Image Processing ◽  
Main Process ◽  
Nuclear Medicine Imaging ◽  
Biomedical Image Processing ◽  
Biomedical Image ◽  
X Ray Computed ◽  
Magnetic Resonance Imaging Mri ◽  
High Level

Over the years, there are different sorts of medical imaging have been developed. Where the most known are: X-ray, computed tomography (CT), nuclear medicine imaging (PET, SPECT), ultrasound and magnetic resonance imaging (MRI), each one has its different utilities. Various studies in biomedical informatics present a process to analyze images for extracting the hidden information which can be used after that. Image analysis combines several fields that are classified into two categories; the process of low-level, that requires very little information about the content image and the process of high-level, which may need information about the image content. The topic of this chapter is to introduce the different techniques for medical image processing and to present many research studies in this domain. It includes four stages, firstly, we introduce the most important medical imaging modalities and secondly, we outline the main process of biomedical image.

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