Clinical impact of an adaptive statistical iterative reconstruction algorithm for detection of hypervascular liver tumours using a low tube voltage, high tube current MDCT technique
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To investigate the impact of an adaptive statistical iterative reconstruction (ASiR) algorithm on diagnostic accuracy and confidence for the diagnosis of hypervascular liver tumours, as well as the reader’s perception of image quality, using a low tube voltage (80 kVp), high tube current computed tomography (CT) technique.
Forty patients (29 men, 11 women) with 65 hypervascular liver tumours underwent dual energy CT. The 80 kV set of the dual energy acquisition was reconstructed with standard filtered backprojection (FBP) and ASiR at different blending levels. Lesion contrast-to-noise ratio (CNR), reader’s confidence for lesion detection and characterisation, and reader’s evaluation of image quality were recorded.
ASiR yielded significantly higher CNR values compared with FBP (P < 0.0001 for all comparisons). Reader’s perception of lesion conspicuity and confidence in the diagnosis of malignancy were also higher with 60 % and 80 % ASiR, compared with FBP (P = 0.01 and < 0.001, respectively). Compared with FBP, ASiR yielded nearly significantly lower specificity for lesion detection and a substantial decrease in the reader’s perception of image quality.
Compared with the standard FBP algorithm, ASiR significantly improves conspicuity of hypervascular liver lesions. This improvement may come at the cost of decreased specificity and reader’s perception of image quality.
• Adaptive statistical iterative reconstruction algorithms (ASiRs) offer increasing potential in multidetector CT.
• An ASiR algorithm significantly improves conspicuity of hypervascular liver lesions at MDCT.
• Improved lesion conspicuity translates into increased reader’s confidence for diagnosis of malignancy.
• False positive findings may increase with ASiR, leading to potentially lower specificity.
KeywordsDiagnostic imaging Multidetector computed tomography Magnetic resonance (MR) Abdominal neoplasms Early detection of cancer Diagnostic accuracy
Adaptive statistical iterative reconstruction algorithm
This study received equipment and financial support from GE Healthcare, Inc. (Milwaukee, USA). The authors had control of the data and the information submitted for publication. Two authors of the study were, respectively, an employee (J.G.C.) and a medical consultant (R.C.N.) to GE Healthcare. Another author (D.M.), who is not an employee of or a consultant for GE Healthcare, had control of inclusion of any data and information that might present a conflict of interest.
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