Patient Exposure Optimisation Through Task-Based Assessment of a New Model-Based Iterative Reconstruction Technique

Ott, Julien G.; Ba, Alexandre; Racine, Damien; Ryckx, Nick; Bochud, François; Alkadhi, Hatem; Verdun, Francis R.

doi:10.1093/rpd/ncw019

Cited by 9 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MBIR, which may be applied at different strengths, perform noise reduction in both raw data and image domains and incorporate physical models to accurately correct for a variety of image degrading effects [ 13 , 14 ]. Several studies indicate that an increase in the strength of IR allows for larger dose reductions [ 13 , 15 , 16 ]. Evaluations of radiological imaging methods can be performed either by studying their ability to provide correct diagnoses [ 17 ] or by visual assessment of well-defined image quality features (visual grading) [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

et al. 2018

View full text Add to dashboard Cite

PurposeTo estimate potential dose reduction in abdominal CT by visually comparing images reconstructed with filtered back projection (FBP) and strengths of 3 and 5 of a specific MBIR.Material and methodsA dual-source scanner was used to obtain three data sets each for 50 recruited patients with 30, 70 and 100% tube loads (mean CTDIvol 1.9, 3.4 and 6.2 mGy). Six image criteria were assessed independently by five radiologists. Potential dose reduction was estimated with Visual Grading Regression (VGR).ResultsComparing 30 and 70% tube load, improved image quality was observed as a significant strong effect of log tube load and reconstruction method with potential dose reduction relative to FBP of 22–47% for MBIR strength 3 (p < 0.001). For MBIR strength 5 no dose reduction was possible for image criteria 1 (liver parenchyma), but dose reduction between 34 and 74% was achieved for other criteria. Interobserver reliability showed agreement of 71–76% (κw 0.201–0.286) and intra-observer reliability of 82–96% (κw 0.525–0.783).ConclusionMBIR showed improved image quality compared to FBP with positive correlation between MBIR strength and increasing potential dose reduction for all but one image criterion.Key Points• MBIR’s main advantage is its de-noising properties, which facilitates dose reduction.• MBIR allows for potential dose reduction in relation to FBP.• Visual Grading Regression (VGR) produces direct numerical estimates of potential dose reduction.• MBIR strengths 3 and 5 dose reductions were 22–34 and 34–74%.• MBIR strength 5 demonstrates inferior performance for liver parenchyma.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…In general, a better model observer software, with sophisticated interfaces and specific setup possibilities, should probably be adopted in future to assist better and predict human observer’s performance. Implementation of a more advanced software, which is beyond the aim of this study, is, however, very complex, as it requires a thorough knowledge of model observer theory, statistics and informatics [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers

et al. 2017

View full text Add to dashboard Cite

BackgroundThe aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment.MethodsImages of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images.ResultsCompared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result.ConclusionIR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

show abstract

“…A channelized Hotelling model observer has previously been used to assess the low-contrast detectability in images of an anthropomorphic phantom in computed tomography (5,6) . These studies showed a positive correlation and agreement with human observers in terms of percent correct in 4AFC experiments, and the mathematical model observer was shown to be less time consuming than human observers when performing receiver operating characteristics (ROC) studies.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Usefulness of the Quasi-Ideal Observer for Quality Control in Fluoroscopy

Tesselaar

Sandborg

2015

Radiat Prot Dosimetry

View full text Add to dashboard Cite

show abstract

Patient Exposure Optimisation Through Task-Based Assessment of a New Model-Based Iterative Reconstruction Technique

Cited by 9 publications

References 12 publications

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

Assessment of image quality in abdominal CT: potential dose reduction with model-based iterative reconstruction

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers

Assessing the Usefulness of the Quasi-Ideal Observer for Quality Control in Fluoroscopy

Contact Info

Product

Resources

About