Automated skull stripping in mouse fMRI analysis using 3D U-Net

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.

Symmetry-aware recursive image similarity exploration for materials microscopy

In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of this interactive tool for researchers to use with their data.

VMAT Planning With Xe-CT Functional Images Enables Radiotherapy Planning With Consideration of Lung Function

Background/Aim: The most severe adverse event of radiotherapy in lung cancer is radiation pneumonitis (RP). Some indices commonly used to prevent RP are evaluated based on the anatomical lung volume. The irradiation dose may be more accurately assessed by using functional lung volume. We evaluated the usefulness of computed tomography (CT) incorporating functional ventilation images acquired by the inhalation of xenon (Xe) gas (Xe-CT functional images). Patients and Methods: Two plans were created for twelve patients: volumetric modulated arc therapy (VMAT) planning using conventional chest CT images (anatomical plans) and VMAT planning using Xe-CT functional images (functional plans), and the dosimetric parameters were compared. Results: Compared to the anatomical plans, the functional plans had significantly reduced V20Gy in the high-functional lungs (p=0.005), but significant differences were not seen in the moderate-functional and low-functional lungs. Conclusion: The incorporation of Xe-CT functional images into VMAT plans enables radiotherapy planning with consideration of lung function.

Implementation of a TMS-fMRI system: A primer

Transcranial magnetic stimulation (TMS) is a non-invasive and non-pharmacological intervention, approved for the treatment of individuals diagnosed with treatment-resistant depression. This well-tolerated approach uses magnetic pulses to stimulate specific brain regions and induce changes in brain networks at multiple levels of human functioning. Combining TMS with other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), offers new insights into brain functioning, and allows to map out the causal alterations brought on by TMS interventions on neural network connectivity and behaviour. However, the implemention of concurrent TMS-fMRI brings on a number of technical challenges that must be overcome to ensure good quality of functional images. The goal of this study was thus to investigate the impact of TMS pulses in an MR-environment on the quality of BRAINO phantom images, in terms of the signal of the images, the temporal fluctuation noise, the spatial noise and the signal to fluctuation noise ratio, at the University of British Columbia (UBC) Neuroimaging facility. The results of our analyses replicated those of previous sites, and showed that the present set-up for concurrent TMS-fMRI ensures minimal noise artefact on functional images obtained through this multimodal approach. This step was a key stepping stone for future clinical trials at UBC.

A Gadolinium(III) Complex Based on the Thymine Nucleobase with Properties Suitable for Magnetic Resonance Imaging

The paramagnetic gadolinium(III) ion is used as contrast agent in magnetic resonance (MR) imaging to improve the lesion detection and characterization. It generates a signal by changing the relaxivity of protons from associated water molecules and creates a clearer physical distinction between the molecule and the surrounding tissues. New gadolinium-based contrast agents displaying larger relaxivity values and specifically targeted might provide higher resolution and better functional images. We have synthesized the gadolinium(III) complex of formula [Gd(thy)2(H2O)6](ClO4)3·2H2O (1) [thy = 5-methyl-1H-pyrimidine-2,4-dione or thymine], which is the first reported compound based on gadolinium and thymine nucleobase. 1 has been characterized through UV-vis, IR, SEM-EDAX, and single-crystal X-ray diffraction techniques, and its magnetic and relaxometric properties have been investigated by means of SQUID magnetometer and MR imaging phantom studies, respectively. On the basis of its high relaxivity values, this gadolinium(III) complex can be considered a suitable candidate for contrast-enhanced magnetic resonance imaging.

A Gadolinium(III) Complex Based on the Thymine Nucleobase With Properties Suitable for Magnetic Resonance Imaging Applications

The paramagnetic gadolinium(III) ion is used as contrast agent in magnetic resonance (MR) imaging to improve the lesion detection and characterization. It generates a signal by changing the relaxivity of protons from associated water molecules and creates a clearer physical distinction between the molecule and the surrounding tissues. New gadolinium-based contrast agents displaying larger relaxivity values and specifically targeted might provide higher resolution and better functional images. We have synthesized the gadolinium(III) complex of formula [Gd(thy)2(H2O)6](ClO4)3&middot;2H2O (1) [thy = 5-methyl-1H-pyrimidine-2,4-dione or thymine], which is the first reported compound based on gadolinium and thymine nucleobase. 1 has been characterized through vis-IR, SEM-EDAX and single-crystal X-ray diffraction techniques, and its magnetic and relaxometric properties have been investigated by means of SQUID magnetometer and MR imaging phantom studies, respectively. On the basis of its high relaxivity values, this gadolinium(III) complex can be considered a suitable candidate for contrast enhanced magnetic resonance imaging.

In Silico Validation of MCID Platform for Monte Carlo-Based Voxel Dosimetry Applied to 90Y-Radioembolization of Liver Malignancies

The aim was the validation of a platform for internal dosimetry, named MCID, based on patient-specific images and direct Monte Carlo (MC) simulations, for radioembolization of liver tumors with 90Y-labeled microspheres. CT of real patients were used to create voxelized phantoms with different density and activity maps. SPECT acquisitions were simulated by the SIMIND MC code. Input macros for the GATE/Geant4 code were generated by MCID, loading coregistered morphological and functional images and performing image segmentation. The dosimetric results obtained from the direct MC simulations and from conventional MIRD approach at both organ and voxel level, in condition of homogeneous tissues, were compared, obtaining differences of about 0.3% and within 3%, respectively, whereas differences increased (up to 14%) introducing tissue heterogeneities in phantoms. Mean absorbed dose for spherical regions of different sizes (10 mm ≤ r ≤ 30 mm) from MC code and from OLINDA/EXM were also compared obtaining differences varying in the range 7–69%, which decreased to 2–9% after correcting for partial volume effects (PVEs) from imaging, confirming that differences were mostly due to PVEs, even though a still high difference for the smallest sphere suggested possible source description mismatching. This study validated the MCID platform, which allows the fast implementation of a patient-specific GATE simulation, avoiding complex and time-consuming manual coding. It also points out the relevance of personalized dosimetry, accounting for inhomogeneities, in order to avoid absorbed dose misestimations.

Dealing With Noise and Partial Volume Effects in Alzheimer Disease Brain Tissue Classification by a Fuzzy-Possibilistic Modeling Based on Fuzzy-Genetic Initialization

Segmentation is a key step in brain imaging where clustering techniques are widely used, particularly the fuzzy approach which offers active and robust methods against noise and partial volume effect (PVE). To address those imperfections, this article suggests an automatic segmentation of brain tissues for magnetic resonance and functional images of Alzheimer's patients, based on an efficient and robust genetic-fuzzy-possibilistic clustering scheme for the assessment of white matter, gray matter and cerebrospinal fluid volumes. The proposed hybrid clustering process based on: 1) A fuzzy possibilistic c-means algorithm that models the degree of relationship between each voxel and a given tissue. 2) A fuzzy c-means algorithm to initialize the clusters centers, with subsequent optimization by a genetic algorithm. Each stage of the proposed clustering process is validated on real brain data and synthetic images of an Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom. A performance comparison is made with the usual fuzzy techniques. The visual and quantitative results obtained with the proposed approach using various signal-to-noise ratios prove its effectiveness to quantify the tissue volume of images of different modalities types in the presence of noise and PVE. The effectiveness in terms of computational rate is also demonstrated.