In this study, we demonstrated the feasibility of a deep learning–based approach for MR imaging–based attenuation correction (MRAC) that performs better than current clinical approaches, with deep MRAC producing a PET reconstruction error that is lower than 1% in most brain regions. PurposeTo develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging–based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging.Materials and MethodsA PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existi...

Purpose: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three‐dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint.Methods: A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel‐wise multi‐class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state‐of‐the‐art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts.Results: The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state‐of‐the‐art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions.Conclusion: The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379–2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A new three-dimensional short repetition time adiabatic inversion recovery–prepared ultrashort echo time sequence provided selective imaging of brain myelin with water signals efficiently suppresse... Background Water signal contamination is a major challenge for direct ultrashort echo time (UTE) imaging of myelin in vivo because water contributes most of the signals detected in white matter. Pu...

Three-dimensional double-echo sliding inversion recovery ultrashort echo time MRI enables robust direct imaging of myelin confirmed via phantom, ex vivo, and in vivo studies; obvious myelin signal ... Background Signal contamination from long T2 water is a major challenge in direct imaging of myelin with MRI. Nulling of the unwanted long T2 signals can be achieved with an inversion recovery (IR)...