Literature DB >> 23511193

Assessment of a model-based, iterative reconstruction algorithm (MBIR) regarding image quality and dose reduction in liver computed tomography.

Won Chang1, Jeong Min Lee, Kyunghee Lee, Jeong Hee Yoon, Mi Hye Yu, Joon Koo Han, Byung Ihn Choi.   

Abstract

OBJECTIVES: The purpose of this study was to assess the image quality of half-dose (HD) liver computed tomography (CT) using a model-based iterative reconstruction algorithm (MBIR) compared with reference dose (RD) using filtered back projection (FBP) and the HD CT images using FBP and adaptive statistical iterative reconstruction (ASIR).
MATERIALS AND METHODS: A total of 103 patients suspected of having liver metastases underwent liver CT including HD portal venous phase imaging. Among these patients, 73 had undergone RD liver CT reconstructed using FBP, and the HD portal phase CT scans were separately reconstructed using FBP and MBIR. For the other 30 patients, the HD CT images were reconstructed using FBP, ASIR, and MBIR. The CT attenuation coefficients and the mean image noise of various sites, including the liver, the aorta, the main portal vein (MPV), and the subcutaneous fat, were measured, and the contrast-to-noise ratios (CNRs) of the metastatic lesion to the liver and the MPV to the liver were calculated. Two radiologists reviewed each image set with regard to image noise, image quality, lesion conspicuity, and diagnostic acceptability.
RESULTS: Compared with RD CT, there was a 46.1% decrease in CT dose index volume with HD CT. Image noise was significantly lower in the HD images reconstructed with MBIR than in both the HD FBP images and the RD FBP images (P < 0.001). Compared with the RD FBP and HD FBP images, the CNRs of the metastatic lesion to the liver and the MPV to the liver were higher in the HD MBIR images (P < 0.001). Despite the presence of the unique whirling artifacts of the MBIR images, the HD MBIR images were of good to excellent quality and were not inferior to RD FBP images regarding the lesion conspicuity, the image quality, and the diagnostic acceptability (P > 0.05). Half-dose MBIR also showed less image noise, higher CNRs, and superior image quality compared with HD ASIR (P < 0.001).
CONCLUSIONS: The HD MBIR images showed less noise, higher CNR, and better image quality than the HD ASIR and HD FBP images did; they also provided less image noise, higher CNR, and similar image quality compared with those of RD FBP images.

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Year:  2013        PMID: 23511193     DOI: 10.1097/RLI.0b013e3182899104

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  44 in total

1.  Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom.

Authors:  Young Jin Ryu; Young Hun Choi; Jung-Eun Cheon; Seongmin Ha; Woo Sun Kim; In-One Kim
Journal:  Pediatr Radiol       Date:  2015-11-06

2.  The feasibility of sub-millisievert coronary CT angiography with low tube voltage, prospective ECG gating, and a knowledge-based iterative model reconstruction algorithm.

Authors:  Chul Hwan Park; Joohee Lee; Chisuk Oh; Kyung Hwa Han; Tae Hoon Kim
Journal:  Int J Cardiovasc Imaging       Date:  2015-10-31       Impact factor: 2.357

3.  Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging.

Authors:  Takeshi Nakaura; Yuji Iyama; Masafumi Kidoh; Koichi Yokoyama; Seitaro Oda; Shinichi Tokuyasu; Kazunori Harada; Yasuyuki Yamashita
Journal:  Neuroradiology       Date:  2015-12-29       Impact factor: 2.804

4.  Combining automated attenuation-based tube voltage selection and iterative reconstruction: a liver phantom study.

Authors:  Daniela B Husarik; Sebastian T Schindera; Fabian Morsbach; Natalie Chuck; Burkhardt Seifert; Zsolt Szucs-Farkas; Hatem Alkadhi
Journal:  Eur Radiol       Date:  2013-10-24       Impact factor: 5.315

5.  Matching and Homogenizing Convolution Kernels for Quantitative Studies in Computed Tomography.

Authors:  Dennis Mackin; Rachel Ger; Skylar Gay; Cristina Dodge; Lifei Zhang; Jinzhong Yang; Aaron Kyle Jones; Laurence Court
Journal:  Invest Radiol       Date:  2019-05       Impact factor: 6.016

6.  Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance.

Authors:  Taihei Inoue; Takeshi Nakaura; Morikatsu Yoshida; Koichi Yokoyama; Kenichiro Hirata; Masafumi Kidoh; Seitaro Oda; Daisuke Utsunomiya; Kazunori Harada; Yasuyuki Yamashita
Journal:  Eur Radiol       Date:  2017-03-08       Impact factor: 5.315

7.  Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages.

Authors:  André Euler; Bram Stieltjes; Zsolt Szucs-Farkas; Reto Eichenberger; Clemens Reisinger; Anna Hirschmann; Caroline Zaehringer; Achim Kircher; Matthias Streif; Sabine Bucher; David Buergler; Luigia D'Errico; Sebastién Kopp; Markus Wilhelm; Sebastian T Schindera
Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

8.  Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps.

Authors:  Hyo-Jin Kang; Se Hyung Kim; Cheong-Il Shin; Ijin Joo; Hwaseong Ryu; Sang Gyun Kim; Jong Pil Im; Joon Koo Han
Journal:  Eur Radiol       Date:  2018-06-08       Impact factor: 5.315

9.  Full model-based iterative reconstruction (MBIR) in abdominal CT increases objective image quality, but decreases subjective acceptance.

Authors:  Gautier Laurent; Nicolas Villani; Gabriela Hossu; Aymeric Rauch; Alain Noël; Alain Blum; Pedro Augusto Gondim Teixeira
Journal:  Eur Radiol       Date:  2019-01-30       Impact factor: 5.315

10.  Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction.

Authors:  Corey T Jensen; Morgan E Telesmanich; Nicolaus A Wagner-Bartak; Xinming Liu; John Rong; Janio Szklaruk; Aliya Qayyum; Wei Wei; Adam G Chandler; Eric P Tamm
Journal:  J Comput Assist Tomogr       Date:  2017-01       Impact factor: 1.826

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