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 |
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| Authors: | Joël Greffier Djamel Dabli Aymeric Hamard Philippe Akessoul Asmaa Belaouni Jean-Paul Beregi Julien Frandon |
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| Abstract: | PurposeTo assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform.Materials and methodsAcquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDIvol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d′) was computed to model the detection task of two enhanced iodine lesions as function of keV.ResultsNoise-magnitude was significantly reduced between 40 to 70 keV by ?56 ± 0% (SD) (range: ?56%–?55%) with FBP; ?56 ± 0% (SD) (?56%–?56%) with A3; and ?57 ± 1% (SD) (range: ?57%–?56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2 mg/mL, d’ values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d’ peaked at 60 keV. For the other simulated lesion, d’ values were highest at 40 keV and decreased beyond.ConclusionAt low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions. |
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| Keywords: | Multidetector computed tomography Dual energy Iterative reconstruction Task-based image quality assessment Phantom study ADMIRE" },{" #name" :" keyword" ," $" :{" id" :" kw0035" }," $$" :[{" #name" :" text" ," _" :" Advanced modeled iterative reconstruction CT" },{" #name" :" keyword" ," $" :{" id" :" kw0045" }," $$" :[{" #name" :" text" ," _" :" Computed Tomography Volume CT dose index DECT" },{" #name" :" keyword" ," $" :{" id" :" kw0065" }," $$" :[{" #name" :" text" ," _" :" Dual-energy CT FBP" },{" #name" :" keyword" ," $" :{" id" :" kw0075" }," $$" :[{" #name" :" text" ," _" :" Filtered back projection HU" },{" #name" :" keyword" ," $" :{" id" :" kw0085" }," $$" :[{" #name" :" text" ," _" :" Hounsfield unit IR" },{" #name" :" keyword" ," $" :{" id" :" kw0095" }," $$" :[{" #name" :" text" ," _" :" Iterative reconstruction MBIR" },{" #name" :" keyword" ," $" :{" id" :" kw0115" }," $$" :[{" #name" :" text" ," _" :" Model-based iterative reconstruction NPWE" },{" #name" :" keyword" ," $" :{" id" :" kw0125" }," $$" :[{" #name" :" text" ," _" :" Non-pre whitening model observer with eye filter NPS" },{" #name" :" keyword" ," $" :{" id" :" kw0135" }," $$" :[{" #name" :" text" ," _" :" Noise power spectrum ROI" },{" #name" :" keyword" ," $" :{" id" :" kw0145" }," $$" :[{" #name" :" text" ," _" :" Region of interest SECT" },{" #name" :" keyword" ," $" :{" id" :" kw0155" }," $$" :[{" #name" :" text" ," _" :" Single-energy CT SD" },{" #name" :" keyword" ," $" :{" id" :" kw0165" }," $$" :[{" #name" :" text" ," _" :" Standard deviation TTF" },{" #name" :" keyword" ," $" :{" id" :" kw0175" }," $$" :[{" #name" :" text" ," _" :" Task-based transfer function VMI" },{" #name" :" keyword" ," $" :{" id" :" kw0185" }," $$" :[{" #name" :" text" ," _" :" Virtual monoenergetic image |
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