Literature DB >> 32060780

The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction.

Yoshie Kurita1, Yasutaka Ichikawa2, Toshihiro Nakanishi3, Yoya Tomita1, Daisuke Hasegawa3, Shuichi Murashima3, Tadanori Hirano3, Hajime Sakuma1.   

Abstract

OBJECTIVE: To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography computed tomography (PET/CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction.
METHODS: Twenty-nine patients with primary or metastatic lung cancers who underwent 18F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength β-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUVmax) was measured in each of the 50 lung lesions.
RESULTS: Liver SNR on the images reconstructed by BPL with β-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with β-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10 mm in diameter on images reconstructed by BPL with β-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10 mm in diameter was not significantly different between BPL with β-value of 600 and OSEM + TOF + PSF. The mean SUVmax was increased by BPL with β-value of 600 for the lung lesions with < 10 mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with β-value of 600 did not provide increased SUVmax for the lesions  ≥ 10 mm in diameter.
CONCLUSION: BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.

Entities:  

Keywords:  Bayesian penalized likelihood reconstruction; Image reconstruction; Lesion conspicuity; Malignant lung tumor; PET/CT

Mesh:

Substances:

Year:  2020        PMID: 32060780     DOI: 10.1007/s12149-020-01446-x

Source DB:  PubMed          Journal:  Ann Nucl Med        ISSN: 0914-7187            Impact factor:   2.668


  7 in total

Review 1.  Influences on PET Quantification and Interpretation.

Authors:  Julian M M Rogasch; Frank Hofheinz; Lutz van Heek; Conrad-Amadeus Voltin; Ronald Boellaard; Carsten Kobe
Journal:  Diagnostics (Basel)       Date:  2022-02-10

2.  Moving the goalposts while scoring-the dilemma posed by new PET technologies.

Authors:  Julian M M Rogasch; Ronald Boellaard; Lucy Pike; Peter Borchmann; Peter Johnson; Jürgen Wolf; Sally F Barrington; Carsten Kobe
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-14       Impact factor: 9.236

3.  The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR.

Authors:  Defeng Tian; Hongwei Yang; Yan Li; Bixiao Cui; Jie Lu
Journal:  EJNMMI Phys       Date:  2022-01-10

4.  Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations.

Authors:  Mitsuaki Tatsumi; Fumihiko Soeda; Takashi Kamiya; Junpei Ueda; Daisuke Katayama; Keiko Matsunaga; Tadashi Watabe; Hiroki Kato; Noriyuki Tomiyama
Journal:  Front Oncol       Date:  2021-08-16       Impact factor: 6.244

5.  Effect of Bayesian penalty likelihood algorithm on 18F-FDG PET/CT image of lymphoma.

Authors:  Yongtao Wang; Lejun Lin; Wei Quan; Jinyu Li; Weilong Li
Journal:  Nucl Med Commun       Date:  2022-03-01       Impact factor: 1.690

6.  The Impact of Total Variation Regularized Expectation Maximization Reconstruction on 68Ga-DOTA-TATE PET/CT Images in Patients With Neuroendocrine Tumor.

Authors:  Lin Liu; Hanxiang Liu; Shijie Xu; Shumao Zhang; Yi Tao; Greta S P Mok; Yue Chen
Journal:  Front Med (Lausanne)       Date:  2022-03-11

7.  Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study.

Authors:  Takato Tanaka; Masatoyo Nakajo; Hirofumi Kawakami; Eriko Motomura; Tomofumi Fujisaka; Satoko Ojima; Yasumasa Saigo; Takashi Yoshiura
Journal:  EJNMMI Res       Date:  2022-09-08       Impact factor: 3.434

  7 in total

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