iQMetrix-CT: New software for task-based image quality assessment of phantom CT images

Willemink, 2019, The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence, Eur Radiol, 29, 2185, 10.1007/s00330-018-5810-7 Greffier, 2020, CT iterative reconstruction algorithms: a task-based image quality assessment, Eur Radiol, 30, 487, 10.1007/s00330-019-06359-6 Greffier, 2020, CT dose optimization for the detection of pulmonary arteriovenous malformation (PAVM): a phantom study, Diagn Interv Imaging, 101, 289, 10.1016/j.diii.2019.12.009 Greffier, 2013, Which dose for what image? Iterative reconstruction for CT scan, Diagn Interv Imaging, 94, 1117, 10.1016/j.diii.2013.03.008 Greffier, 2015, Dose reduction with iterative reconstruction: optimization of CT protocols in clinical practice, Diagn Interv Imaging, 96, 477, 10.1016/j.diii.2015.02.007 Hamard, 2020, Impact of ultra-low dose CT acquisition on semi-automated RECIST tool in the evaluation of malignant focal liver lesions, Diagn Interv Imaging, 101, 473, 10.1016/j.diii.2020.05.003 Macri, 2016, Value of ultra-low-dose chest CT with iterative reconstruction for selected emergency room patients with acute dyspnea, Eur J Radiol, 85, 1637, 10.1016/j.ejrad.2016.06.024 Verdun, 2015, Image quality in CT: from physical measurements to model observers, Phys Med, 31, 823, 10.1016/j.ejmp.2015.08.007 Samei, 2019, Performance evaluation of computed tomography systems: summary of AAPM Task Group 233, Med Phys, 46, e735, 10.1002/mp.13763 Richard, 2012, Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms, Med Phys, 39, 4115, 10.1118/1.4725171 Maidment, 2003, Conditioning data for calculation of the modulation transfer function, Med Phys, 30, 248, 10.1118/1.1534111 Brunner, 2013, Material-specific transfer function model and SNR in CT, Phys Med Biol, 58, 7447, 10.1088/0031-9155/58/20/7447 Ott, 2014, Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms, Phys Med Biol, 59, 4047, 10.1088/0031-9155/59/4/4047 Samei, 2015, Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology, Med Phys, 42, 314, 10.1118/1.4903899 Chen, 2014, Evaluating iterative reconstruction performance in computed tomography, Med Phys, 41, 10.1118/1.4901670 Eckstein, 2003, Automated computer evaluation and optimization of image compression of x-ray coronary angiograms for signal known exactly detection tasks, Opt Express, 11, 460, 10.1364/OE.11.000460 Saunders, 2006, Resolution and noise measurements of five CRT and LCD medical displays, Med Phys, 33, 308, 10.1118/1.2150777 Burgess, 1994, Statistically defined backgrounds: performance of a modified nonprewhitening observer model, J Opt Soc Am A, 11, 1237, 10.1364/JOSAA.11.001237 Burgess, 1997, Visual signal detectability with two noise components: anomalous masking effects, J Opt Soc Am A, 14, 2420, 10.1364/JOSAA.14.002420 Greffier, 2020, Optimization of radiation dose for CT detection of lytic and sclerotic bone lesions: a phantom study, Eur Radiol, 30, 1075, 10.1007/s00330-019-06425-z Solomon, 2012, Quantitative comparison of noise texture across CT scanners from different manufacturers, Med Phys, 39, 6048, 10.1118/1.4752209 Chen, 2014, Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods, Med Phys, 41 Solomon, 2020, Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm, Med Phys, 47, 3961, 10.1002/mp.14319 Viry, 2022, Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels, Eur Radiol, 32, 1227, 10.1007/s00330-021-08185-1 Viry, 2018, Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: a quantitative task-based phantom study, Phys Med, 48, 111, 10.1016/j.ejmp.2018.04.006 Racine, 2020, Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: a phantom study, Phys Med, 76, 28, 10.1016/j.ejmp.2020.06.004 Racine, 2021, Image texture, low contrast liver lesion detectability and impact on dose: deep learning algorithm compared to partial model-based iterative reconstruction, Eur J Radiol, 141, 10.1016/j.ejrad.2021.109808 Greffier, 2020, Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study, Eur Radiol, 30, 3951, 10.1007/s00330-020-06724-w Greffier, 2021, Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: a phantom study, Med Phys, 48, 5743, 10.1002/mp.15180 Greffier, 2022, Comparison of two deep learning image reconstruction algorithms in chest CT images: a task-based image quality assessment on phantom data, Diagn Interv Imaging, 103, 21, 10.1016/j.diii.2021.08.001 Greffier, 2020, Effect of tin filter-based spectral shaping CT on image quality and radiation dose for routine use on ultralow-dose CT protocols: a phantom study, Diagn Interv Imaging, 101, 373, 10.1016/j.diii.2020.01.002 Greffier, 2020, Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images, Phys Med, 77, 36, 10.1016/j.ejmp.2020.07.024 Greffier, 2021, Impact of four kVp combinations available in a dual-source CT on the spectral performance of abdominal imaging: a task-based image quality assessment on phantom data, J Appl Clin Med Phys, 22, 243, 10.1002/acm2.13369 Greffier, 2021, Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: a task-based image quality assessment, Diagn Interv Imaging, 102, 405, 10.1016/j.diii.2021.03.002 Greffier, 2021, Performance of four dual-energy CT platforms for abdominal imaging: a task-based image quality assessment based on phantom data, Eur Radiol, 31, 5324, 10.1007/s00330-020-07671-2 Greffier, 2022, Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images, Med Phys, 49, 2233, 10.1002/mp.15558 Dabli, 2022, Optimization of image quality and accuracy of low iodine concentration quantification as function of dose level and reconstruction algorithm for abdominal imaging using dual-source CT: a phantom study, Diagn Interv Imaging, 103, 31, 10.1016/j.diii.2021.08.004 Greffier, 2021, Spectral photon-counting CT system: toward improved image quality performance in conventional and spectral CT imaging, Diagn Interv Imaging, 102, 271, 10.1016/j.diii.2021.02.003 Si-Mohamed, 2022, Coronary CT angiography with photon-counting CT: first in-human results, Radiology, 303, 303, 10.1148/radiol.211780 Boccalini, 2021, Feasibility of human vascular imaging of the neck with a large field-of-view spectral photon-counting CT system, Diagn Interv Imaging, 102, 329, 10.1016/j.diii.2020.12.004 Greffier, 2022, Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: a phantom study, Medical Physics, 10.1002/mp.15807