Incremental Image Noise Reduction in Coronary CT Angiography Using a Deep Learning-Based Technique with Iterative Reconstruction

Abstract

Objective: To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction. Materials and Methods: We retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction).

Radiation Dose Reduction in CT Torsion Measurement of the Lower Limb: Introduction of a New Ultra-Low Dose Protocol

“…Further dose reduction and improvement of CNR may even be achieved by using modern CT scanners with a fully integrated circuit detector system, inherently generating less image noise, or by advanced post-processing algorithms (e.g., the advanced modeled iterative reconstruction technique (ADMIRE), Siemens Healthineers, Forchheim, Germany). The use of artificial intelligence and neural networks may also have potential to further improve CNR [ 28 , 29 , 30 ]. We believe this may be subject to future research on ULD CT protocols for torsion measurements of the limbs and other indications.…”

Advances in deep learning for computed tomography denoising

“…In any case, the diminishment of radiation dosage increases noise and presents artifacts in reconstructed images[ 7 , 8 ]. In other words, reductions in radiation dose lead to decreases in image quality, which may adversely affect diagnosis using LDCT images[ 1 , 8 ]. Therefore, much exertion has been made to plan better image processing techniques that can further diminish image noise after image capture[ 1 ].…”

“…ClariCT (ClariPi, Seoul, South Korea) is based on the CNN algorithm noise reduction approach and features digital imaging and communications in medicine (DICOM)-based sinogram blend and crossover IR. It offers the advantage in terms of denoising from both projection and image space[ 1 , 19 , 20 ].…”

“…In radiography, decreases in radiation dosage result in the debasement of image quality, basically due to an increment in image noise[ 1 ]. Increased image noise can compromise the diagnostic performance of computed tomography (CT) images.…”

“…Recently, deep learning (DL) techniques have been increasingly applied to many aspects of medical imaging and are showing promise as an effective solution for the problem of noise[ 3 - 5 ]. This is particularly true in their application to denoising CT images, where DL techniques have appeared noteworthy performance in moving forward imaging quality by noise suppression, structural preservation, and lesion detection[ 1 , 6 ].…”