Literature DB >> 26197735

Knowledge-based iterative model reconstruction (IMR) algorithm in ultralow-dose CT for evaluation of urolithiasis: evaluation of radiation dose reduction, image quality, and diagnostic performance.

Sung Bin Park1, Yang Soo Kim2, Jong Beum Lee2, Hyun Jeong Park2.   

Abstract

PURPOSE: To evaluate the efficacy of a knowledge-based iterative model reconstruction (IMR) algorithm for reducing image noise in ultralow-dose (ULD) CT for urolithiasis.
MATERIALS AND METHODS: A total of 103 patients diagnosed with urinary stones (n = 276) were enrolled. Regular dose (RD) scans (120 kV and 150 mAs, maximal tube current in dose modulation) were reconstructed using filtered back-projection (FBP, RD-FBP), and ULD scans (100 kV and 20 mAs, fixed tube current) were reconstructed with FBP (ULD-FBP), statistical iterative reconstruction (IR; ULD-iDose), and a knowledge-based IMR algorithm (ULD-IMR). Prospective interpretations of the two scans were performed with respect to radiation dose, objective image noise, and subjective assessment. The subjective assessment was also evaluated with regard to each patient's body mass index (BMI, < 25 or ≥ 25 kg/m(2)). Using RD CT (RD-FBP) as the reference standard, two reviewers assessed the diagnostic performance and inter-observer agreement for ULD-IMR. RESULT: The average effective doses with RD CT and ULD CT were 8.31 and 0.68 mSv, respectively, and the average radiation dose reduction rate was 91.82% (p < 0.01). The lowest objective image noise was observed with ULD-IMR (p < 0.01). Subjective assessment in ULD-IMR was comparable to that of RD-FBP, although RD-FBP remained statistically superior. For BMI, there was a statistically significant difference in subjective image quality between the normal (4.7 ± 0.54) and overweight or obese groups (4.2 ± 0.5) (p < 0.05). The ULD-IMR showed a greater than 75% concordant rate in overall stones and 100% in ureter stones larger than 3 mm. However, for stones <3 mm, neither reviewer had a good detection rate (45.5% and 56.9% for the general and genitourinary radiologist, respectively). Inter-observer agreement was almost perfect (κ = 0.82).
CONCLUSION: Despite a significant radiation dose reduction, ULD-IMR images were comparable in image quality and noise to RD-FBP images. Furthermore, the diagnostic performance of the ULD non-enhanced CT protocol was comparable to that of the RD scan for diagnosing urinary stones larger than 3 mm.

Entities:  

Keywords:  Knowledge-based iterative model reconstruction; Low-dose computed tomography; Model-based iterative reconstruction; Statistical iterative reconstruction; Urolithiasis

Mesh:

Year:  2015        PMID: 26197735     DOI: 10.1007/s00261-015-0504-y

Source DB:  PubMed          Journal:  Abdom Imaging        ISSN: 0942-8925


  9 in total

1.  Effect of iterative reconstruction techniques on image quality in low radiation dose chest CT: a phantom study.

Authors:  Yan Xu; Ting-Ting Zhang; Zhi-Hai Hu; Juan Li; Hong-Jun Hou; Zu-Shan Xu; Wen He
Journal:  Diagn Interv Radiol       Date:  2019-11       Impact factor: 2.630

2.  Iterative model reconstruction (IMR) algorithm for reduced radiation dose renal artery CT angiography with different tube voltage protocols.

Authors:  Le Qin; ZePeng Ma; FuHua Yan; WenJie Yang
Journal:  Radiol Med       Date:  2017-10-20       Impact factor: 3.469

Review 3.  Imaging in the diagnosis of pediatric urolithiasis.

Authors:  Gabrielle C Colleran; Michael J Callahan; Harriet J Paltiel; Caleb P Nelson; Bartley G Cilento; Michelle A Baum; Jeanne S Chow
Journal:  Pediatr Radiol       Date:  2016-11-04

4.  Determination of optimal imaging settings for urolithiasis CT using filtered back projection (FBP), statistical iterative reconstruction (IR) and knowledge-based iterative model reconstruction (IMR): a physical human phantom study.

Authors:  Se Y Choi; Seung H Ahn; Jae D Choi; Jung H Kim; Byoung-Il Lee; Jeong-In Kim; Sung B Park
Journal:  Br J Radiol       Date:  2015-11-18       Impact factor: 3.039

5.  Comparison of image quality from filtered back projection, statistical iterative reconstruction, and model-based iterative reconstruction algorithms in abdominal computed tomography.

Authors:  Yu Kuo; Yi-Yang Lin; Rheun-Chuan Lee; Chung-Jung Lin; Yi-You Chiou; Wan-Yuo Guo
Journal:  Medicine (Baltimore)       Date:  2016-08       Impact factor: 1.889

6.  Contrast-Enhanced CT with Knowledge-Based Iterative Model Reconstruction for the Evaluation of Parotid Gland Tumors: A Feasibility Study.

Authors:  Chae Jung Park; Ki Wook Kim; Ho-Joon Lee; Myeong-Jin Kim; Jinna Kim
Journal:  Korean J Radiol       Date:  2018-08-06       Impact factor: 3.500

7.  Evaluation of Ultra-Low-Dose Chest Computed Tomography Images in Detecting Lung Lesions Related to COVID-19: A Prospective Study.

Authors:  Fariba Zarei; Reza Jalli; Sabyasachi Chatterjee; Rezvan Ravanfar Haghighi; Pooya Iranpour; Vani Vardhan Chatterjee; Sedigheh Emadi
Journal:  Iran J Med Sci       Date:  2022-07

8.  Low-Dose Unenhanced Computed Tomography with Iterative Reconstruction for Diagnosis of Ureter Stones.

Authors:  Byung Hoon Chi; In Ho Chang; Dong Hoon Lee; Sung Bin Park; Kyung Do Kim; Young Tae Moon; Taekyu Hur
Journal:  Yonsei Med J       Date:  2018-05       Impact factor: 2.759

9.  Coronary computed tomography angiography using model-based iterative reconstruction algorithms in the detection of significant coronary stenosis: how the plaque type influences the diagnostic performance.

Authors:  Antonio Vizzuso; Riccardo Righi; Aldo Carnevale; Michela Zerbini; Giorgio Benea; Melchiore Giganti
Journal:  Pol J Radiol       Date:  2019-12-09
  9 in total

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