Physics in medicine and biology
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The purpose of this work was to develop and evaluate a deep learning approach for automatic rat brain image segmentation of magnetic resonance imaging (MRI) images in a clinical PET/MR, providing a useful tool for analyzing studies of the pathology and progression of neurological disease and to validate new radiotracers and therapeutic agents. Rat brain PET/MR images (N = 56) were collected from a clinical PET/MR system using a dedicated small-animal imaging phased array coil. A segmentation method based on a triple cascaded convolutional neural network (CNN) was developed, where, for a rectangular region of interest covering the whole brain, the entire brain volume was outlined using a CNN, then the outlined brain was fed into the cascaded network to segment both the cerebellum and cerebrum, and finally the sub-cortical structures within the cerebrum including hippocampus, thalamus, striatum, lateral ventricles and prefrontal cortex were segmented out using the last cascaded CNN. ⋯ The proposed method achieved a mean DSC of 0.965, 0.927, 0.858, 0.594, 0.847, 0.674 and 0.838 for whole brain, cerebellum, hippocampus, lateral ventricles, striatum, prefrontal cortex and thalamus, respectively. Compared with the segmentation results reported in previous publications using atlas-based methods, the proposed method demonstrated improved performance in the whole brain and cerebellum segmentation. In conclusion, the proposed method achieved high accuracy for rat brain segmentation in MRI images from a clinical PET/MR and enabled the possibility of automatic rat brain image processing for small animal neurological research.
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To investigate the feasibility of the log-demons deformable image registration (DIR) method to correct eddy current and field inhomogeneity distortions while preserving diffusion tensor information. Diffusion-weighted images (DWIs) are susceptible to distortions caused by eddy current and echo-planar imaging (EPI) gradients. We propose a post-acquisition correction algorithm using the log-demons DIR technique for eddy current and field inhomogeneity distortions of DWI. ⋯ In the MASSIVE study, the average MI of all slices increased for both eddy current and field inhomogeneity distortion correction. The average absolute differences of all slices between corrected images with opposing gradients were also on average decreased. This work indicates that the log-demons DIR algorithm is feasible to reduce eddy current and field inhomogeneity distortions while preserving quantitative diffusion information.
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We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T 1-mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remove the streaking artefacts from under-sampled T 1 mapping images. A total of 2090 T 1-weighted images for 33 patients (55 ± 15 years, 19 males) and five healthy subjects (30 ± 14 years, 2 males) were used for training and testing the network. ⋯ There was no statistically significant difference between the measured T 1 maps for both per-subject (reference: 1085 ± 37 ms, CNN: 1088 ± 37 ms, p = 0.4) and per-segment (reference: 1084 ± 48 ms, CNN: 1083 ± 58 ms, p = 0.9) analyses. In summary, deep CNN allows fast and reliable removal of streaking artefact from under-sampled radial T 1 mapping images. Our results show that the highly non-linear operations of deep CNN processing of T 1 mapping images do not impact accurate reconstruction of myocardial T 1 maps.
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The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body truncation and metal artefacts translate to incomplete MRI-derived attenuation correction (AC) maps, consequently resulting in large quantification errors in PET imaging. ⋯ The percentage of the torso volume affected by body truncation in the 3-class AC maps reduced from 9.8 ± 1.9% to 0.6 ± 0.3% after truncation compensation. PET quantification errors in the affected regions were also reduced from -45.5 ± 10% to -9.5 ± 3% after truncation compensation. The quantitative results demonstrated promising performance of the proposed approach towards the completion of MR images corrupted by metal artefacts and/or body truncation in the context of WB PET/MR imaging.
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Surgical reduction of pelvic dislocation is a challenging procedure with poor long-term prognosis if reduction does not accurately restore natural morphology. The procedure often requires long fluoroscopic exposure times and trial-and-error to achieve accurate reduction. We report a method to automatically compute the target pose of dislocated bones in preoperative CT and provide 3D guidance of reduction using routine 2D fluoroscopy. ⋯ The method demonstrated accurate estimation of the target reduction pose in simulation, phantom, and a clinical feasibility study for a broad range of dislocation patterns, initialization error, dose levels, and FOV size. The system provides a novel means of guidance and assessment of pelvic reduction from routinely acquired preoperative CT and intraoperative fluoroscopy. The method has the potential to reduce radiation dose by minimizing trial-and-error and to improve outcomes by guiding more accurate reduction of joint dislocations.