scholarly journals Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering

2021 ◽  
Vol 11 (18) ◽  
pp. 8707
Author(s):  
Hyeon-Sik Kim ◽  
Byeong-il Lee ◽  
Jae-Sung Ahn
Keyword(s):  
Image Quality ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Emission Tomography ◽  
Recovery Coefficient ◽  
Filtering Method ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Image Blurring ◽  
Selection Of

The accuracy of positron emission tomography (PET) imaging is hampered by the partial volume effect (PVE), which causes image blurring and sampling. The PVE produces spillover phenomena, making PET analysis difficult. Generally, the PVE values vary based on reconstruction methods and filtering. Thus, selection of the proper reconstruction and filtering method can ensure accurate and high-quality PET images. This study compared the values of factors (recovery coefficient (RC), uniformity, and spillover ratio (SOR)) associated with different reconstruction and post-filtering methods using a mouse image quality phantom (NEMA NU 4), and we present an effective approach for microPET images. The PET images were obtained using a microPET scanner (Inveon, Siemens Medical Solutions, Malvern, PA, USA). PET data were reconstructed and/or post-filtered. For tumors smaller than 3 mm, iterative reconstruction methods provided better image quality. For tumor sizes bigger than 3 mm, reconstruction methods without post-filtering showed better results.

scholarly journals Hypoperfusion without Ischemia Surrounding Acute Intracerebral Hemorrhage

2001 ◽  
Vol 21 (7) ◽  
pp. 804-810 ◽  
Author(s):  
Allyson R. Zazulia ◽  
Michael N. Diringer ◽  
Tom O. Videen ◽  
Robert E. Adams ◽  
Kent Yundt ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Emission Tomography ◽  
Oxygen Extraction Fraction ◽  
Midline Shift ◽  
Cerebral Metabolic Rate ◽  
Extraction Fraction ◽  
Positron Emission ◽  
Acute Intracerebral Hemorrhage

A zone of hypoperfusion surrounding acute intracerebral hemorrhage (ICH) has been interpreted as regional ischemia. To determine if ischemia is present in the periclot area, the authors measured cerebral blood flow (CBF), cerebral metabolic rate of oxygen (CMRO2), and oxygen extraction fraction (OEF) with positron emission tomography (PET) in 19 patients 5 to 22 hours after hemorrhage onset. Periclot CBF, CMRO2, and OEF were determined in a 1-cm-wide area around the clot. In the 16 patients without midline shift, periclot data were compared with mirror contralateral regions. All PET images were masked to exclude noncerebral structures, and all PET measurements were corrected for partial volume effect due to clot and ventricles. Both periclot CBF and CMRO2 were significantly reduced compared with contralateral values (CBF: 20.9 ± 7.6 vs. 37.0 ± 13.9 mL 100 g−1 min−1, P = 0.0004; CMRO2: 1.4 ± 0.5 vs. 2.9 ± 0.9 mL 100 g−1 min−1, P = 0.00001). Periclot OEF was less than both hemispheric OEF (0.42 ± 0.15 vs. 0.47 ± 0.13, P = 0.05; n = 19) and contralateral regional OEF (0.44 ± 0.16 vs. 0.51 ± 0.13, P = 0.05; n = 16). In conclusion, CMRO2 was reduced to a greater degree than CBF in the periclot region in acute ICH, resulting in reduced OEF rather than the increased OEF that occurs in ischemia. Thus, the authors found no evidence for ischemia in the periclot zone of hypoperfusion in acute ICH patients studied 5 to 22 hours after hemorrhage onset.

scholarly journals Deep learning-based image quality improvement of 18F-fluorodeoxyglucose positron emission tomography: a retrospective observational study

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Junichi Tsuchiya ◽  
Kota Yokoyama ◽  
Ken Yamagiwa ◽  
Ryosuke Watanabe ◽  
Koichiro Kimura ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Emission Tomography ◽  
Gaussian Filter ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
Tumor Delineation ◽  
Standardized Uptake Values ◽  
Positron Emission ◽  
Image Quality Improvement ◽  
Fluorodeoxyglucose Positron Emission

Abstract Background Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images obtained with the DL method with those obtained using a Gaussian filter. Methods Fifty patients with a mean age of 64.4 (range, 19–88) years who underwent 18F-FDG PET/CT between April 2019 and May 2019 were included in the study. PET images were obtained with the DL method in addition to conventional images reconstructed with three-dimensional time of flight-ordered subset expectation maximization and filtered with a Gaussian filter as a baseline for comparison. The reconstructed images were reviewed by two nuclear medicine physicians and scored from 1 (poor) to 5 (excellent) for tumor delineation, overall image quality, and image noise. For the semi-quantitative analysis, standardized uptake values in tumors and healthy tissues were compared between images obtained using the DL method and those obtained with a Gaussian filter. Results Images acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss’ kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001). Conclusions Deep learning method improves the quality of PET images.

scholarly journals Preclinical dosimetry models and the prediction of clinical doses of novel positron emission tomography radiotracers

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adam A. Garrow ◽  
Jack P. M. Andrews ◽  
Zaniah N. Gonzalez ◽  
Carlos A. Corral ◽  
Christophe Portal ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Studies ◽  
Emission Tomography ◽  
Whole Body ◽  
Preclinical Model ◽  
Reconstruction Method ◽  
Preclinical Research ◽  
Reconstruction Methods ◽  
Positron Emission ◽  
Pet Radiotracers

Abstract Dosimetry models using preclinical positron emission tomography (PET) data are commonly employed to predict the clinical radiological safety of novel radiotracers. However, unbiased clinical safety profiling remains difficult during the translational exercise from preclinical research to first-in-human studies for novel PET radiotracers. In this study, we assessed PET dosimetry data of six 18F-labelled radiotracers using preclinical dosimetry models, different reconstruction methods and quantified the biases of these predictions relative to measured clinical doses to ease translation of new PET radiotracers to first-in-human studies. Whole-body PET images were taken from rats over 240 min after intravenous radiotracer bolus injection. Four existing and two novel PET radiotracers were investigated: [18F]FDG, [18F]AlF-NOTA-RGDfK, [18F]AlF-NOTA-octreotide ([18F]AlF-NOTA-OC), [18F]AlF-NOTA-NOC, [18F]ENC2015 and [18F]ENC2018. Filtered-back projection (FBP) and iterative methods were used for reconstruction of PET data. Predicted and true clinical absorbed doses for [18F]FDG and [18F]AlF-NOTA-OC were then used to quantify bias of preclinical model predictions versus clinical measurements. Our results show that most dosimetry models were biased in their predicted clinical dosimetry compared to empirical values. Therefore, normalization of rat:human organ sizes and correction for reconstruction method biases are required to achieve higher precision of dosimetry estimates.

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