This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the Poisson summation formula. To deal with the localization uncertainty due to image folding, a small number of low-frequency k-space data are added. Training the deep learning net involves input and output images that are pairs of the Fourier transforms of the subsampled and fully sampled k-space data. Our experiments show the remarkable performance of the proposed method; only 29[Formula: see text] of the k-space data can generate images of high quality as effectively as standard MRI reconstruction with the fully sampled data.
Purpose
This paper proposes a sinogram‐consistency learning method to deal with beam hardening‐related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform
Methods
The proposed learning method aims to repair inconsistent sinogram by removing the primary metal‐induced beam hardening factors along the metal trace in the sinogram. Taking account of the fundamental difficulty in obtaining sufficient training data in a medical environment, the learning method is designed to use simulated training data and a patient’s implant type‐specific learning model is used to simplify the learning process.
Results
The feasibility of the proposed method is investigated using a dataset, consisting of real CT scans of pelvises containing simulated hip prostheses. The anatomical areas in training and test data are different, in order to demonstrate that the proposed method extracts the beam hardening features, selectively. The results show that our method successfully corrects sinogram inconsistency by extracting beam hardening sources by means of deep learning.
Conclusion
This paper proposed a deep learning method of sinogram correction for beam hardening reduction in CT for the first time. Conventional methods for beam hardening reduction are based on regularizations, and have the fundamental drawback of being not easily able to use manifold CT images, while a deep learning approach has the potential to do so.
Accurate and robust identification of cephalometric landmarks for three-dimensional (3D) computerized tomography (CT) images is an important task for diagnosis, surgical planning, growth analysis, and treatment evaluation. Recent advances in imaging technology have led to the transition from two-dimensional (2D) cephalometry to a 3D one using CT scan images. 3D cephalometry holds several advantages, including the accurate identification of anatomical structures, avoidance of geometric distortion of the image, and ability to evaluate complicated facial structure. However, 3D cephalometry being a manual operation, it requires timeconsuming and labor-intensive work.Cephalometric analysis is basically conducted through cephalometric annotation, i.e. landmark detection for meaningful anatomical structures. It requires a high level of expertise, experience, and time. As 2D cephalometric analysis gives way to 3D, these difficulties are aggravated by a remarkable increase in data volume and geometric complexity. Several different automatic 3D annotation approaches have been proposed to address these limitations (
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.