Literature DB >> 31359122

CT iterative reconstruction algorithms: a task-based image quality assessment.

J Greffier1, J Frandon2, A Larbi2, J P Beregi2, F Pereira2.   

Abstract

PURPOSE: To assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors.
MATERIALS AND METHODS: We used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d') was computed to model the detection task of a large mass in the liver (large feature; 120 HU and 25-mm diameter) and a small calcification (small feature; 500 HU and 1.5-mm diameter).
RESULTS: With H/SIR, the highest values of d' for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were - 35% for GE, - 62% for Philips, and - 13% for Siemens; for the small feature, corresponding reductions were - 45%, - 78%, and - 14%, respectively. With the Canon system, a potential dose reduction of - 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%.
CONCLUSION: This multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d' is highly adapted and robust for an optimization process. KEY POINTS: • The performance of four CT systems was evaluated by using imQuest software to assess noise characteristic, spatial resolution, and lesion detection. • Two task functions were defined to model the detection task of a large mass in the liver and a small calcification. • The advantage of task-based image quality assessment for radiologists is that it does not include only complicated metrics, but also clinically meaningful image quality.

Entities:  

Keywords:  Image enhancement; Image reconstruction; Multidetector computed tomography

Mesh:

Year:  2019        PMID: 31359122     DOI: 10.1007/s00330-019-06359-6

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  25 in total

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Authors:  Cynthia H McCollough; Guang Hong Chen; Willi Kalender; Shuai Leng; Ehsan Samei; Katsuyuki Taguchi; Ge Wang; Lifeng Yu; Roderic I Pettigrew
Journal:  Radiology       Date:  2012-06-12       Impact factor: 11.105

2.  A three-dimensional statistical approach to improved image quality for multislice helical CT.

Authors:  Jean-Baptiste Thibault; Ken D Sauer; Charles A Bouman; Jiang Hsieh
Journal:  Med Phys       Date:  2007-11       Impact factor: 4.071

3.  An Improved Index of Image Quality for Task-based Performance of CT Iterative Reconstruction across Three Commercial Implementations.

Authors:  Olav Christianson; Joseph J S Chen; Zhitong Yang; Ganesh Saiprasad; Alden Dima; James J Filliben; Adele Peskin; Christopher Trimble; Eliot L Siegel; Ehsan Samei
Journal:  Radiology       Date:  2015-02-13       Impact factor: 11.105

4.  Diagnostic Performance of an Advanced Modeled Iterative Reconstruction Algorithm for Low-Contrast Detectability with a Third-Generation Dual-Source Multidetector CT Scanner: Potential for Radiation Dose Reduction in a Multireader Study.

Authors:  Justin Solomon; Achille Mileto; Juan Carlos Ramirez-Giraldo; Ehsan Samei
Journal:  Radiology       Date:  2015-03-04       Impact factor: 11.105

5.  Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

Authors:  Motonori Akagi; Yuko Nakamura; Toru Higaki; Keigo Narita; Yukiko Honda; Jian Zhou; Zhou Yu; Naruomi Akino; Kazuo Awai
Journal:  Eur Radiol       Date:  2019-04-11       Impact factor: 5.315

6.  Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms.

Authors:  Samuel Richard; Daniela B Husarik; Girijesh Yadava; Simon N Murphy; Ehsan Samei
Journal:  Med Phys       Date:  2012-07       Impact factor: 4.071

7.  Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

Authors:  Julien G Ott; Fabio Becce; Pascal Monnin; Sabine Schmidt; François O Bochud; Francis R Verdun
Journal:  Phys Med Biol       Date:  2014-07-03       Impact factor: 3.609

8.  Digital image processing: effect on detectability of simulated low-contrast radiographic patterns.

Authors:  M Ishida; K Doi; L N Loo; C E Metz; J L Lehr
Journal:  Radiology       Date:  1984-02       Impact factor: 11.105

9.  Value of ultra-low-dose chest CT with iterative reconstruction for selected emergency room patients with acute dyspnea.

Authors:  Francesco Macri; Joel Greffier; Fabricio Pereira; Alina Chica Rosa; Elina Khasanova; Pierre-Geraut Claret; Ahmed Larbi; Gianfranco Gualdi; Jean Paul Beregi
Journal:  Eur J Radiol       Date:  2016-07-01       Impact factor: 3.528

10.  Radiation Dose Reduction by Using CT with Iterative Model Reconstruction in Patients with Pulmonary Invasive Fungal Infection.

Authors:  Chenggong Yan; Jun Xu; Chunyi Liang; Qi Wei; Yuankui Wu; Wei Xiong; Huan Zheng; Yikai Xu
Journal:  Radiology       Date:  2018-04-10       Impact factor: 11.105

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  10 in total

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2.  Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study.

Authors:  Joël Greffier; Djamel Dabli; Aymeric Hamard; Asmaa Belaouni; Philippe Akessoul; Julien Frandon; Jean-Paul Beregi
Journal:  Quant Imaging Med Surg       Date:  2022-01

3.  Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose.

Authors:  Kun Zhang; Xiang Shi; Shuang-Shuang Xie; Ji-Hang Sun; Zhuo-Heng Liu; Shuai Zhang; Jia-Yang Song; Wen Shen
Journal:  Quant Imaging Med Surg       Date:  2022-06

4.  Ultra-low-dose chest CT performance for the detection of viral pneumonia patterns during the COVID-19 outbreak period: a monocentric experience.

Authors:  Joël Greffier; Adel Hoballah; Alexandre Sadate; Fabien de Oliveira; Pierre-Geraud Claret; Hélène de Forges; Paul Loubet; Jean-Marc Mauboussin; Aymeric Hamard; Jean-Paul Beregi; Julien Frandon
Journal:  Quant Imaging Med Surg       Date:  2021-07

5.  Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.

Authors:  Andrea Steuwe; Marie Weber; Oliver Thomas Bethge; Christin Rademacher; Matthias Boschheidgen; Lino Morris Sawicki; Gerald Antoch; Joel Aissa
Journal:  Br J Radiol       Date:  2020-10-23       Impact factor: 3.039

6.  Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction.

Authors:  Joël Greffier; Salim Si-Mohamed; Boris Guiu; Julien Frandon; Maeliss Loisy; Fabien de Oliveira; Philippe Douek; Jean-Paul Beregi; Djamel Dabli
Journal:  Quant Imaging Med Surg       Date:  2022-02

7.  Periappendiceal fat-stranding models for discriminating between complicated and uncomplicated acute appendicitis: a diagnostic and validation study.

Authors:  Hui-An Lin; Hung-Wei Tsai; Chun-Chieh Chao; Sheng-Feng Lin
Journal:  World J Emerg Surg       Date:  2021-10-13       Impact factor: 5.469

8.  Evaluation of Abdominal CT Obtained Using a Deep Learning-Based Image Reconstruction Engine Compared with CT Using Adaptive Statistical Iterative Reconstruction.

Authors:  Yeo Jin Yoo; In Young Choi; Suk Keu Yeom; Sang Hoon Cha; Yunsub Jung; Hyun Jong Han; Euddeum Shim
Journal:  J Belg Soc Radiol       Date:  2022-04-08       Impact factor: 1.894

9.  Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network.

Authors:  Zheng Fang; Renbin Wang; Mengyi Wang; Shuo Zhong; Liquan Ding; Siyuan Chen
Journal:  Materials (Basel)       Date:  2020-04-22       Impact factor: 3.623

Review 10.  Low Dose Chest CT and Lung Ultrasound for the Diagnosis and Management of COVID-19.

Authors:  Julie Finance; Laurent Zieleskewicz; Paul Habert; Alexis Jacquier; Philippe Parola; Alain Boussuges; Fabienne Bregeon; Carole Eldin
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