Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.

Recently, dental cone-beam computed tomography (CBCT) methods have been improved to significantly reduce radiation dose while maintaining image resolution with minimal equipment cost. In low-dose CBCT environments, metallic inserts such as implants, crowns, and dental fillings cause severe artifacts, which result in a significant loss of morphological structures of teeth in reconstructed images. Such metal artifacts prevent accurate 3D bone-teeth-jaw modeling for diagnosis and treatment planning.

Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.

Purpose: This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT).

Methods: A total of 19,052 coronal reformatted chest CT images of 110 CT image sets (55 pairs of concordant 16- and 320-row CT image sets) were included and used to train a deep learning algorithm for artifact and noise correction. For internal validation, 4093 coronal reformatted CT images of 25 patients from 16-row CT images underwent correction processing.

A fidelity-embedded learning for metal artifact reduction in dental CBCT.

Purpose: Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metal artifacts.

Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

Objectives: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.

Materials And Methods: LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets.

Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET) and short-time scanning PET (PET) images. We generated a standard-time scanning PET-like image (sPET) from a PET image using a deep-learning network.

Diagnostic Performance of a New Convolutional Neural Network Algorithm for Detecting Developmental Dysplasia of the Hip on Anteroposterior Radiographs.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs.

Materials And Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH.

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed.

CT sinogram-consistency learning for metal-induced beam hardening correction.

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.

Technical Note: A model-based sinogram correction for beam hardening artifact reduction in CT.

Purpose: This study aims to propose a physics-based method of reducing beam-hardening artifacts induced by high-attenuation materials such as metal stents or other metallic implants.

Methods: The proposed approach consists of deriving a sinogram inconsistency formula representing the energy dependence of the attenuation coefficient of high-attenuation materials. This inconsistency formula more accurately represents the inconsistencies of the sinogram than that of a previously reported formula (called the MAC-BC method).

Metal Artifact Reduction for Polychromatic X-ray CT Based on a Beam-Hardening Corrector.

This paper proposes a new method to correct beam hardening artifacts caused by the presence of metal in polychromatic X-ray computed tomography (CT) without degrading the intact anatomical images. Metal artifacts due to beam-hardening, which are a consequence of X-ray beam polychromaticity, are becoming an increasingly important issue affecting CT scanning as medical implants become more common in a generally aging population. The associated higher-order beam-hardening factors can be corrected via analysis of the mismatch between measured sinogram data and the ideal forward projectors in CT reconstruction by considering the known geometry of high-attenuation objects.

Computed tomographic beam-hardening artefacts: mathematical characterization and analysis.

This paper presents a mathematical characterization and analysis of beam-hardening artefacts in X-ray computed tomography (CT). In the field of dental and medical radiography, metal artefact reduction in CT is becoming increasingly important as artificial prostheses and metallic implants become more widespread in ageing populations. Metal artefacts are mainly caused by the beam-hardening of polychromatic X-ray photon beams, which causes mismatch between the actual sinogram data and the data model being the Radon transform of the unknown attenuation distribution in the CT reconstruction algorithm.

Metal artifact reduction in CT by identifying missing data hidden in metals.

There is increasing demand in the field of dental and medical radiography for effective metal artifact reduction (MAR) in computed tomography (CT) because artifact caused by metallic objects causes serious image degradation that obscures information regarding the teeth and/or other biological structures. This paper presents a new MAR method that uses the Laplacian operator to reveal background projection data hidden in regions containing data from metal. In the proposed method, we attempted to decompose the projection data into two parts: data from metal only (metal data), and background data in the absence of metal.