Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?

Parvizi, Nassim; Franklin, James; McGowan, Daniel R.; Teoh, Eugene; Bradley, Kevin M.; Gleeson, Fergus

doi:10.1016/j.ejrad.2015.06.025

Cited by 76 publications

(70 citation statements)

References 28 publications

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“…Our results are in line with the previously published studies that provided a more quantitative evaluation compared with the study presented here [24,25,33,34]. Differences in the SUV-values on different scanners represent a problem in imaging, both in clinical routine as well as in research studies [2,21,36].…”

Section: Discussionsupporting

confidence: 82%

“…On the basis of its technical properties and depending on the applied regularization parameter β, BSREM is expected to significantly improve this challenge by 'smoothing' the areas with higher background and at the same time emphasizing hot lesions. This was supported by a group around Parvizi et al [34] who compared the properties of 42 liver metastases reconstructed with BSREM-TOF and OSEM-TOF. They reported a higher SUV max of the lesion without an increased image noise using the new penalized algorithm [34].…”

Section: Discussionmentioning

confidence: 49%

“…This was supported by a group around Parvizi et al [34] who compared the properties of 42 liver metastases reconstructed with BSREM-TOF and OSEM-TOF. They reported a higher SUV max of the lesion without an increased image noise using the new penalized algorithm [34]. An improvement in lesion detection is also expected especially in cold regions (e.g.…”

Section: Discussionmentioning

confidence: 49%

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Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

Sah

Stolzmann

Delso

et al. 2017

Nuclear Medicine Communications

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PURPOSE To investigate the clinical performance of a block sequential regularized expectation maximization (BSREM) penalized likelihood reconstruction algorithm in oncologic PET/computed tomography (CT) studies. METHODS A total of 410 reconstructions of 41 fluorine-18 fluorodeoxyglucose-PET/CT studies of 41 patients with a total of 2010 lesions were analyzed by two experienced nuclear medicine physicians. Images were reconstructed with BSREM (with four different values) or ordered subset expectation maximization (OSEM) algorithm with/without time-of-flight (TOF/non-TOF) corrections. OSEM reconstruction postfiltering was 4.0 mm full-width at half-maximum; BSREM did not use postfiltering. Evaluation of general image quality was performed with a five-point scale using maximum intensity projections. Artifacts (category 1), image sharpness (category 2), noise (category 3), and lesion detectability (category 4) were analyzed using a four-point scale. Size and maximum standardized uptake value (SUVmax) of lesions were measured by a third reader not involved in the image evaluation. RESULTS BSREM-TOF reconstructions showed the best results in all categories, independent of different body compartments. In all categories, BSREM non-TOF reconstructions were significantly better than OSEM non-TOF reconstructions (P<0.001). In almost all categories, BSREM non-TOF reconstruction was comparable to or better than the OSEM-TOF algorithm (P<0.001 for general image quality, image sharpness, noise, and P=1.0 for artifact). Only in lesion detectability was OSEM-TOF significantly better than BSREM non-TOF (P<0.001). Both BSREM-TOF and BSREM non-TOF showed a decreasing SUVmax with increasing values (P<0.001) and TOF reconstructions showed a significantly higher SUVmax than non-TOF reconstructions (P<0.001). CONCLUSION The BSREM reconstruction algorithm showed a relevant improvement compared with OSEM reconstruction in PET/CT studies in all evaluated categories. BSREM might be used in clinical routine in conjunction with TOF to achieve better/higher image quality and lesion detectability or in PET/CT-systems without TOF-capability for enhancement of overall image quality as well. Purpose To investigate the clinical performance of a block sequential regularized expectation maximization (BSREM) penalized likelihood reconstruction algorithm in oncologic PET/computed tomography (CT) studies.Methods A total of 410 reconstructions of 41 fluorine-18 fluorodeoxyglucose-PET/CT studies of 41 patients with a total of 2010 lesions were analyzed by two experienced nuclear medicine physicians. Images were reconstructed with BSREM (with four different β values) or ordered subset expectation maximization (OSEM) algorithm with/without time-of-flight (TOF/non-TOF) corrections. OSEM reconstruction postfiltering was 4.0 mm full-width at halfmaximum; BSREM did not use postfiltering. Evaluation of general image quality was performed with a five-point scale using maximum intensity projections. Artifacts (category 1), image sharpness (category 2), n...

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 49%

Section: Discussionmentioning

confidence: 49%

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Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

Sah

Stolzmann

Delso

et al. 2017

Nuclear Medicine Communications

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“…The penalty term imposes more smoothing in lower activity regions and less smoothing in higher activity regions or in the neighborhood of high intensity edges, resulting in smoother cold backgrounds and improved hot lesions signal‐to‐noise ratio . At the same time, the use of a penalty function allows an effective SUV convergence, providing more accurate values . On its commercial implementation, the behavior of the Q.Clear reconstruction is controlled by a single parameter, called β , which determines the strength of the penalty function.…”

Section: Introductionmentioning

confidence: 99%

Phantom, clinical, and texture indices evaluation and optimization of a penalized‐likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner

et al. 2018

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“…This recent algorithm has been used clinically in oncological imaging and has shown favorable characteristics compared to first-generation standard iterative technique, ordered subsets expectation maximization (OSEM). 6 Although the authors study dynamic blood flow of 13-N-Ammonia, the studies were performed for assessment of cardiac sarcoidosis, and therefore no stress blood flow data were analyzed. The authors conclude that the dynamic reconstruction with advanced techniques results in less noise than comparable reconstructions obtained with filtered back projection (FBP) and OSEM, and do not cause bias in the estimation of the input function.…”

Section: Introductionmentioning

confidence: 99%

How to reconstruct dynamic cardiac PET data?

Slomka

Alessio

Germano

2017

Journal of Nuclear Cardiology

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Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases?

Cited by 76 publications

References 28 publications

Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies

Phantom, clinical, and texture indices evaluation and optimization of a penalized‐likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner

How to reconstruct dynamic cardiac PET data?

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