Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Objective: To compare the quality of various polychromatic and monochromatic images with or without using an iterative metal artifact reduction algorithm (iMAR) obtained from a dual-energy computed tomography (CT) to evaluate total knee arthroplasty.
Materials And Methods: We included 58 patients (28 male and 30 female; mean age [range], 71.4 [61-83] years) who underwent 74 knee examinations after total knee arthroplasty using dual-energy CT. CT image sets consisted of polychromatic image sets that linearly blended 80 kVp and tin-filtered 140 kVp using weighting factors of 0.4, 0, and -0.3, and monochromatic images at 130, 150, 170, and 190 keV. These image sets were obtained with and without applying iMAR, creating a total of 14 image sets. Two readers qualitatively ranked the image quality (1 [lowest quality] through 14 [highest quality]). Volumes of high- and low-density artifacts and contrast-to-noise ratios (CNRs) between the bone and fat tissue were quantitatively measured in a subset of 25 knees unaffected by metal artifacts.
Results: iMAR-applied, polychromatic images using weighting factors of -0.3 and 0.0 (P and P, respectively) showed the highest image-quality rank scores (median of 14 for both by one reader and 13 and 14, respectively, by the other reader; < 0.001). All iMAR-applied image series showed higher rank scores than the iMAR-unapplied ones. The smallest volumes of low-density artifacts were found in P, P, and iMAR-applied monochromatic images at 130 keV. The smallest volumes of high-density artifacts were noted in P. The CNRs were best in polychromatic images using a weighting factor of 0.4 with or without iMAR application, followed by polychromatic images using a weighting factor of 0.0 with or without iMAR application.
Conclusion: Polychromatic images combined with iMAR application, P and P, provided better image qualities and substantial metal artifact reduction compared with other image sets.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316773 | PMC |
| http://dx.doi.org/10.3348/kjr.2020.0548 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Reducing motion artifacts in craniocervical background subtraction angiography with deformable registration and unsupervised deep learning.
Radiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
Machine Learning-Aided Screening and Design Rule Discovery for LWIR-Transparent Optical Materials.
J Chem Inf Model
September 2025
Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721-0041, United States.
The development of low-cost, high-performance materials with enhanced transparency in the long-wavelength infrared (LWIR) region (800-1250 cm/8-12.5 μm) is essential for advancing thermal imaging and sensing technologies. Traditional LWIR optics rely on costly inorganic materials, limiting their broader deployment.
View Article and Find Full Text PDFTwo-Step Semi-Automated Classification of Choroidal Metastases on MRI: Orbit Localization via Bounding Boxes Followed by Binary Classification via Evolutionary Strategies.
AJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Prediction of double expression status of primary CNS lymphoma using multiparametric MRI radiomics combined with habitat radiomics: a double-center study.
J Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
Radiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.