A consensus protocol for functional connectivity analysis in the rat brain - Grandjean, Joanes; Desrosiers-Gregoire, Gabriel; Anckaerts, Cynthia; Angeles-Valdez, Diego; Ayad, Fadi; Barrière, David A.; Blockx, Ines; Bortel, Aleksandra; Broadwater, Margaret; Cardoso, Beatriz M.; Célestine, Marina; Chavez-Negrete, Jorge E.; et al. (Nature Neuroscience, 2023)
Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.
CRIS-Team Scopus Importer:2023-04-07

Characterization and quantification of angiogenesis in rheumatoid arthritis in a mouse model using ?CT - Gayetskyy, Svitlana; Museyko, Oleg; Käßer, Johannes; Heß, Andreas; Schett, Georg; Engelke, Klaus (BMC Musculoskeletal Disorders, 2014)
Angiogenesis is an important pathophysiological process of chronic inflammation, especially in inflammatory arthritis. Quantitative measurement of changes in vascularization may improve the diagnosis and monitoring of arthritis. The aim of this work is the development of a 3D imaging and analysis framework for quantification of vascularization in experimental arthritis.High-resolution micro-computed tomography (?CT) was used to scan knee joints of arthritic human tumor necrosis factor transgenic (hTNFtg) mice and non-arthritic wild-type controls previously perfused with lead-containing contrast agent Microfil MV-122. Vessel segmentation was performed by combination of intensity-based (local adaptive thresholding) and form-based (multi-scale method) segmentation techniques. Four anatomically defined concentric spherical shells centered in the knee joint were used as analysis volumes of interest. Vessel density, density distribution as well as vessel thickness, surface, spacing and number were measured. Simulated digital vessel tree models were used for validation of the algorithms.High-resolution ?CT allows the quantitative assessment of the vascular tree in the knee joint during arthritis. Segmentation and analysis were highly automated but occasionally required manual corrections of the vessel segmentation close to the bone surfaces. Vascularization was significantly increased in arthritic hTNFtg mice compared to wild type controls. Precision errors for the morphologic parameters were smaller than 3% and 6% for intra- and interoperator analysis, respectively. Accuracy errors for vessel thickness were around 20% for vessels larger than twice the resolution of the scanner.Arthritis-induced changes of the vascular tree, including detailed and quantitative description of the number of vessel branches, length of vessel segments and the bifurcation angle, can be detected by contrast-enhanced high-resolution ?CT.
EVALuna2:8512

Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests - Sindel, Aline; Breininger, Katharina; Käßer, Johannes; Heß, Andreas; Maier, Andreas; Köhler, Thomas (25th IEEE International Conference on Image Processing, ICIP 2018, 2018)
Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this end, we propose volumetric super-resolution forests (VSRF) to enhance MRI resolution retrospectively. Our method learns a locally linear mapping between low-resolution and high-resolution volumetric image patches by employing random forest regression. We customize features suitable for volumetric MRI to train the random forest and propose a median tree ensemble for robust regression. VSRF outperforms state-of-the-art example-based super-resolution in terms of image quality and efficiency for model training and inference on different MRI datasets. It is also superior to unsupervised methods with just a handful or even a single volume to assemble training data.<br
Keywords: MRI; Super-resolution; Random forests