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CT automated exposure control using a generalized detectability index
Author(s) -
Khobragade P.,
Rupcich Franco,
Fan Jiahua,
Crotty Dominic J.,
Kulkarni Naveen M.,
O'Connor Stacy D.,
Foley W. Dennis,
Schmidt Taly Gilat
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13286
Subject(s) - imaging phantom , iterative reconstruction , lookup table , computer science , image quality , noise (video) , artificial intelligence , image resolution , optical transfer function , algorithm , mathematics , nuclear medicine , image (mathematics) , medicine , programming language , mathematical analysis
Purpose Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( d gen ′ ) to determine the noise parameter to input to existing automated exposure control ( AEC ) systems to provide consistent image quality (IQ) across different reconstruction approaches. Methods This study proposes a task‐based automated exposure control ( AEC ) method using a generalized detectability index ( d gen ′ ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d gen ′ metric is calculated using lookup tables of task‐based modulation transfer function ( MTF ) and noise power spectrum ( NPS ). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT , GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mA s. Images were reconstructed using a reference reconstruction algorithm and four levels of an in‐house iterative reconstruction algorithm with different regularization strengths ( IR 1– IR 4). The task‐based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d gen ′ and noise standard deviation. The performance of the proposed d gen ′ ‐ AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images. Results The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d gen ′ predicted by the lookup table andd ′ measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d gen ′ ‐ AEC method were 3.3 (reference image), 3.5 ( IR 1), 3.6 ( IR 2), 3.5 ( IR 3), and 2.2 ( IR 4). When using the d gen ′ ‐ AEC method, the observers’ IQ scores for the reference reconstruction were statistical equivalent to the scores for IR 1, IR 2, and IR 3 iterative reconstructions ( P > 0.35). The d gen ′ ‐ AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans. Conclusions A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d gen ′ ‐ AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.