We investigated the impact of a CNN-based deep-learning (DL) image de-blurring algorithm on coronary artery calcium (CAC) detection performance in conventional CT imaging. Our approach comprises first de-noising the image with a state-of-the-art CNN-based image de-noising algorithm. With improved SNR, it is then possible to sharpen the image with a CNN-based image de-blurring algorithm. We train such networks using natural images, i.e., a large set of diverse photographs. The de-noising strength in the final image can be adjusted by blending back the estimated noise from the first step to the desired degree. To assess the impact of the de-blurring algorithm, we scanned an anthropomorphic phantom containing 100 small calcifications on a CT system using a CAC scoring protocol. Data were acquired at clinical and high dose, and subsequently reconstructed with and without the DL de-blurring algorithm, using 25% of the maximum de-noising strength. For each small CAC, detectability was defined as the ability to calculate an Agatston score (at least 3 adjacent voxels exceeding 130 HU). For the high dose scans, CAC detectability increased from 39% for the standard reconstruction to 49% with de-blurring. The same 39% CAC detectability at high dose without de-blurring was obtained with routine dose with de-blurring. In this work, we also show some visual impressions of applying our DL de-blurring method to clinical cardiac data.
For the X-ray image acquisition one of the most important factors for diagnostic quality is the patient position with respect to the X-ray tube and the detector. In case of orthopedic lateral ankle examinations, inaccurate positioning might lead to a covered joint space. This could make a reliable reading of the images impossible, which necessitates a retake. The presented approach estimates the joint space visibility of lateral ankle X-ray images. An annotation method for the joint space visibility is proposed which depends on the condyle alignment of the talus. A Convolutional Neural Network (CNN) was trained to estimate the joint space visibility. Additionally, the plausibility of the approach was confirmed by an experimental phantom setup. The estimations on a clinical dataset show that using the quality measure in regression space results in a sensitivity of 0.85 and a specificity of 0.91 for a clinically reasonable definition of image quality.
Deviations from the MR acquisition guidelines could lead to images with serious quality concerns such as incompletely imaged anatomies, which might require re-examinations and could result in missed pathologies. In this paper, we propose a deep learning method to automatically estimate the coverage of the target anatomy and to predict the extent of an anatomy outside the present field-of-view (FOV). For this purpose, we employed a 3D fully-convolutional neural network operating at multiple resolution levels. The proposed solution could be employed to propose a correct FOV setting in case of organ-coverage issues while patient is on the table and could be incorporated as a retrospective tool for quality monitoring and staff training. Our method was evaluated for four abdominal organs - liver, spleen, and left and right kidneys - in 40 magnetic resonance (MR) images from the publicly available Combined Healthy Abdominal Organ Segmentation (CHAOS) dataset. We obtained median extent-detection errors of 5.5-7.3mm or 3-4 voxels in the superior or inferior position in a dataset with average anatomical clippings of 24.8-43.6mm for four partially missing organs in the given FOV.
Combined PET/MR imaging allows to incorporate the high-resolution anatomical information delivered by MRI into the PET reconstruction algorithm for improvement of PET accuracy beyond standard corrections. We used the working hypothesis that glucose uptake in adipose tissue is low. Thus, our aim was to shift 18F-FDG PET signal into image regions with a low fat content. Dixon MR imaging can be used to generate fat-only images via the water/fat chemical shift difference. On the other hand, the Origin Ensemble (OE) algorithm, a novel Markov chain Monte Carlo method, allows to reconstruct PET data without the use of forward- and back projection operations. By adequate modifications to the Markov chain transition kernel, it is possible to include anatomical a priori knowledge into the OE algorithm. In this work, we used the OE algorithm to reconstruct PET data of a modified IEC/NEMA Body Phantom simulating body water/fat composition. Reconstruction was performed 1) natively, 2) informed with the Dixon MR fat image to down-weight 18F-FDG signal in fatty tissue compartments in favor of adjacent regions, and 3) informed with the fat image to up-weight 18F-FDG signal in fatty tissue compartments, for control purposes. Image intensity profiles confirmed the visibly improved contrast and reduced partial volume effect at water/fat interfaces. We observed a 17±2% increased SNR of hot lesions surrounded by fat, while image quality was almost completely retained in fat-free image regions. An additional in vivo experiment proved the applicability of the presented technique in practice, and again verified the beneficial impact of fat-constrained OE reconstruction on PET image quality.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.