Literature DB >> 23784877

System for verifiable CT radiation dose optimization based on image quality. part II. process control system.

David B Larson1, Remo J Malarik, Seth M Hall, Daniel J Podberesky.   

Abstract

PURPOSE: To evaluate the effect of an automated computed tomography (CT) radiation dose optimization and process control system on the consistency of estimated image noise and size-specific dose estimates (SSDEs) of radiation in CT examinations of the chest, abdomen, and pelvis.
MATERIALS AND METHODS: This quality improvement project was determined not to constitute human subject research. An automated system was developed to analyze each examination immediately after completion, and to report individual axial-image-level and study-level summary data for patient size, image noise, and SSDE. The system acquired data for 4 months beginning October 1, 2011. Protocol changes were made by using parameters recommended by the prediction application, and 3 months of additional data were acquired. Preimplementation and postimplementation mean image noise and SSDE were compared by using unpaired t tests and F tests. Common-cause variation was differentiated from special-cause variation by using a statistical process control individual chart.
RESULTS: A total of 817 CT examinations, 490 acquired before and 327 acquired after the initial protocol changes, were included in the study. Mean patient age and water-equivalent diameter were 12.0 years and 23.0 cm, respectively. The difference between actual and target noise increased from -1.4 to 0.3 HU (P < .01) and the standard deviation decreased from 3.9 to 1.6 HU (P < .01). Mean SSDE decreased from 11.9 to 7.5 mGy, a 37% reduction (P < .01). The process control chart identified several special causes of variation.
CONCLUSION: Implementation of an automated CT radiation dose optimization system led to verifiable simultaneous decrease in image noise variation and SSDE. The automated nature of the system provides the opportunity for consistent CT radiation dose optimization on a broad scale. © RSNA, 2013.

Entities:  

Mesh:

Year:  2013        PMID: 23784877     DOI: 10.1148/radiol.13122321

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  13 in total

1.  A comparison study of size-specific dose estimate calculation methods.

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Journal:  Pediatr Radiol       Date:  2017-09-27

2.  Dynamic Volume Computed Tomography Imaging of the Upper Airway in Obstructive Sleep Apnea.

Authors:  Robert J Fleck; Stacey L Ishman; Sally R Shott; Ephraim J Gutmark; Keith B McConnell; Mohamed Mahmoud; Goutham Mylavarapu; Dhananjay R Subramaniam; Rhonda Szczesniak; Raouf S Amin
Journal:  J Clin Sleep Med       Date:  2017-02-15       Impact factor: 4.062

3.  Optimizing CT radiation dose based on patient size and image quality: the size-specific dose estimate method.

Authors:  David B Larson
Journal:  Pediatr Radiol       Date:  2014-10-11

4.  Size-based quality-informed framework for quantitative optimization of pediatric CT.

Authors:  Ehsan Samei; Xiang Li; Donald P Frush
Journal:  J Med Imaging (Bellingham)       Date:  2017-08-21

5.  Evaluating the appropriateness of dosimetric indices in body CT.

Authors:  Gianluca Valeri; Silvia Cegna; Alberto Mari; Luigi La Riccia; Giovanni Mazzoni; Stefania Maggi; Andrea Giovagnoni
Journal:  Radiol Med       Date:  2014-11-21       Impact factor: 3.469

6.  Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.

Authors:  Sihwan Kim; Woo Kyoung Jeong; Jin Hwa Choi; Jong Hyo Kim; Minsoo Chun
Journal:  PLoS One       Date:  2022-09-29       Impact factor: 3.752

7.  Pediatric CT quality management and improvement program.

Authors:  David B Larson; Lior Z Molvin; Jia Wang; Frandics P Chan; Beverley Newman; Dominik Fleischmann
Journal:  Pediatr Radiol       Date:  2014-10-11

Review 8.  Design for Additive Bio-Manufacturing: From Patient-Specific Medical Devices to Rationally Designed Meta-Biomaterials.

Authors:  Amir A Zadpoor
Journal:  Int J Mol Sci       Date:  2017-07-25       Impact factor: 5.923

9.  Improving Low-Dose Pediatric Abdominal CT by Using Convolutional Neural Networks.

Authors:  Robert D MacDougall; Yanbo Zhang; Michael J Callahan; Jeannette Perez-Rossello; Micheál A Breen; Patrick R Johnston; Hengyong Yu
Journal:  Radiol Artif Intell       Date:  2019-11-27

10.  Is It Better to Enter a Volume CT Dose Index Value before or after Scan Range Adjustment for Radiation Dose Optimization of Pediatric Cardiothoracic CT with Tube Current Modulation?

Authors:  Hyun Woo Goo
Journal:  Korean J Radiol       Date:  2018-06-14       Impact factor: 3.500

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