D Racine1, H G Brat2, B Dufour2, J M Steity3, M Hussenot4, B Rizk5, D Fournier2, F Zanca6. 1. Institute of Radiation Physics (IRA), Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue du Grand-Pré 1, 1007 Lausanne, Switzerland. Electronic address: damien.racine@chuv.ch. 2. Institut de Radiologie de Sion, Groupe 3R, Rue du scex, 2, 1950 Sion, Switzerland. 3. Centre d'imagerie de la Riviera, Groupe 3R, Rue des Moulins 5B, 1800 Vevey, Switzerland. 4. GE Medical Systems (Schweiz) AG, Europa-Strasse 31, 8152 Glattbrugg, Switzerland. 5. Centre d'Imagerie de Fribourg, Groupe 3R, Rue du Centre 10, 1752 Fribourg, Switzerland. 6. Palindromo Consulting, Willem de Croylaan 51, 3000 Leuven, Belgium.
Abstract
OBJECTIVES: To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. METHODS: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. RESULTS: FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. CONCLUSION: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.
OBJECTIVES: To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction. METHODS: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V. RESULTS:FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively. CONCLUSION: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose.
Authors: Corey T Jensen; Shiva Gupta; Mohammed M Saleh; Xinming Liu; Vincenzo K Wong; Usama Salem; Wei Qiao; Ehsan Samei; Nicolaus A Wagner-Bartak Journal: Radiology Date: 2022-01-11 Impact factor: 29.146