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Med Phys. 2019 Nov;46(11):e735-e756. doi: 10.1002/mp.13763. Epub 2019 Sep 11.

Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233.

Author information

1
Duke University, 2424 Erwin Rd, Durham, NC, 27710, USA.
2
Henry Ford Health System, 2799 W Grand Blvd, Detroit, MI, 48202, USA.
3
Canon Medical Systems, 1440 Warnall Ave, Los Angeles, CA, 90024, USA.
4
Cincinnati Children's Hospital, 3333 Burnet Ave, Cincinnati, OH, 45229, USA.
5
GE Healthcare, 3000 N. Grandview Blvd, Waukesha, WI, 53188, USA.
6
Mayo Clinic, 200 1st. St, Rochester, MN, 55901, USA.
7
Office of Science and Engineering Laboratories, FDA, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
8
Neusoft Medical Systems USA, 130 Rollins Ave, Rockville, MD, 20852, USA.
9
Siemens Healthineers, 122 Naperville Dr, Cary, NC, 27519, USA.
10
University of Wisconsin, 1111 Highland Ave, Madison, WI, 53705, USA.
11
Duke University Medical Center, 2424 Erwin Rd, Durham, NC, 27710, USA.
12
Canon Medical Systems, 2441 Michelle Dr, Tustin, CA, 92780, USA.
13
Stanford University, 480 Oak Road, Stanford, CA, 94305, USA.

Abstract

BACKGROUND:

The rapid development and complexity of new x-ray computed tomography (CT) technologies and the need for evidence-based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation.

AIMS:

This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques.

MATERIALS AND METHODS:

The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task-based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons.

RESULTS:

The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis.

DISCUSSION:

In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification-based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice).

CONCLUSION:

We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance-based criteria for CT imaging.

KEYWORDS:

acceptance testing; computed tomography; detectability; noise; quality control; resolution

PMID:
31408540
DOI:
10.1002/mp.13763

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