Rapid Whole-Body FDG PET/MRI in Oncology Patients: Utility of Combining Bayesian Penalised Likelihood PET Reconstruction and Abbreviated MRI

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2023 Jun 10

PMID 37296723

Authors

Junko Inoue Inukai

Munenobu Nogami

Miho Tachibana

Feibi Zeng

Tatsuya Nishitani

Kazuhiro Kubo

Takamichi Murakami

Affiliations

Soon will be listed here.

Abstract

This study evaluated the diagnostic value of a rapid whole-body fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) approach, combining Bayesian penalised likelihood (BPL) PET with an optimised β value and abbreviated MRI (abb-MRI). The study compares the diagnostic performance of this approach with the standard PET/MRI that utilises ordered subsets expectation maximisation (OSEM) PET and standard MRI (std-MRI). The optimal β value was determined by evaluating the noise-equivalent count (NEC) phantom, background variability, contrast recovery, recovery coefficient, and visual scores (VS) for OSEM and BPL with β100-1000 at 2.5-, 1.5-, and 1.0-min scans, respectively. Clinical evaluations were conducted for , , liver signal-to-noise ratio (SNR), lesion maximum standardised uptake value, lesion signal-to-background ratio, lesion SNR, and VS in 49 patients. The diagnostic performance of BPL/abb-MRI was retrospectively assessed for lesion detection and differentiation in 156 patients using VS. The optimal β values were β600 for a 1.5-min scan and β700 for a 1.0-min scan. BPL/abb-MRI at these β values was equivalent to OSEM/std-MRI for a 2.5-min scan. By combining BPL with optimal β and abb-MRI, rapid whole-body PET/MRI could be achieved in ≤1.5 min per bed position, while maintaining comparable diagnostic performance to standard PET/MRI.

Citing Articles

The 13th World Federation of Nuclear Medicine and Biology congress (WFNMB 2022): summarize the past half century and discuss the next half century of WFNMB.

Fujii H, Toyama H, Kayano D, Ishii K, Kinuya S Ann Nucl Med. 2024; 39(2):87-97.

PMID: 39621202 DOI: 10.1007/s12149-024-01999-1.

References

Kurita Y, Ichikawa Y, Nakanishi T, Tomita Y, Hasegawa D, Murashima S . The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point.... Ann Nucl Med. 2020; 34(4):272-279. DOI: 10.1007/s12149-020-01446-x. View

Te Riet J, Rijnsdorp S, Roef M, Arends A . Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical F-FDG PET/CT. EJNMMI Phys. 2020; 6(1):32. PMC: 6937357. DOI: 10.1186/s40658-019-0262-y. View

Fukukita H, Suzuki K, Matsumoto K, Terauchi T, Daisaki H, Ikari Y . Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of Version 2.0. Ann Nucl Med. 2014; 28(7):693-705. PMC: 4332454. DOI: 10.1007/s12149-014-0849-2. View

Ribeiro D, Hallett W, Howes O, McCutcheon R, Nour M, Tavares A . Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [C]PHNO brain PET-MR studies. EJNMMI Res. 2022; 12(1):11. PMC: 8859021. DOI: 10.1186/s13550-022-00883-1. View

Yoshii T, Miwa K, Yamaguchi M, Shimada K, Wagatsuma K, Yamao T . Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom. EJNMMI Phys. 2020; 7(1):56. PMC: 7486353. DOI: 10.1186/s40658-020-00325-8. View

Tian D, Yang H, Li Y, Cui B, Lu J . The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR. EJNMMI Phys. 2022; 9(1):1. PMC: 8748582. DOI: 10.1186/s40658-021-00428-w. View

Chilcott A, Bradley K, McGowan D . Effect of a Bayesian Penalized Likelihood PET Reconstruction Compared With Ordered Subset Expectation Maximization on Clinical Image Quality Over a Wide Range of Patient Weights. AJR Am J Roentgenol. 2017; 210(1):153-157. DOI: 10.2214/AJR.17.18060. View

Lindstrom E, Sundin A, Trampal C, Lindsjo L, Ilan E, Danfors T . Evaluation of Penalized-Likelihood Estimation Reconstruction on a Digital Time-of-Flight PET/CT Scanner for F-FDG Whole-Body Examinations. J Nucl Med. 2018; 59(7):1152-1158. DOI: 10.2967/jnumed.117.200790. View

Matti A, Maria Lima G, Pettinato C, Pietrobon F, Martinelli F, Fanti S . How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?. Nucl Med Mol Imaging. 2019; 53(3):216-222. PMC: 6554360. DOI: 10.1007/s13139-019-00594-x. View

10.

Huellner M, Appenzeller P, Kuhn F, Husmann L, Pietsch C, Burger I . Whole-body nonenhanced PET/MR versus PET/CT in the staging and restaging of cancers: preliminary observations. Radiology. 2014; 273(3):859-69. DOI: 10.1148/radiol.14140090. View

11.

Yokoo T, Masaki N, Parikh N, Lane B, Feng Z, Mendiratta-Lala M . Multicenter Validation of Abbreviated MRI for Detecting Early-Stage Hepatocellular Carcinoma. Radiology. 2023; 307(2):e220917. PMC: 10102624. DOI: 10.1148/radiol.220917. View

12.

Wu Z, Guo B, Huang B, Zhao B, Qin Z, Hao X . Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?. J Appl Clin Med Phys. 2021; 22(3):224-233. PMC: 7984479. DOI: 10.1002/acm2.13129. View

13.

Texte E, Gouel P, Thureau S, Lequesne J, Barres B, Edet-Sanson A . Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images. EJNMMI Phys. 2020; 7(1):28. PMC: 7218037. DOI: 10.1186/s40658-020-00300-3. View

14.

Rijnsdorp S, Roef M, Arends A . Impact of the Noise Penalty Factor on Quantification in Bayesian Penalized Likelihood (Q.Clear) Reconstructions of Ga-PSMA PET/CT Scans. Diagnostics (Basel). 2021; 11(5). PMC: 8150604. DOI: 10.3390/diagnostics11050847. View

15.

Yamaguchi T, Sofue K, Ueshima E, Ueno Y, Tsujita Y, Yabe S . Abbreviated Gadoxetic Acid-Enhanced MRI for the Detection of Liver Metastases in Patients With Potentially Resectable Pancreatic Ductal Adenocarcinoma. J Magn Reson Imaging. 2022; 56(3):725-736. DOI: 10.1002/jmri.28059. View

16.

Kim S, Cho N, Hong H, Lee Y, Yoen H, Kim Y . Abbreviated Screening MRI for Women with a History of Breast Cancer: Comparison with Full-Protocol Breast MRI. Radiology. 2022; 305(1):36-45. DOI: 10.1148/radiol.213310. View

17.

Grant A, Deller T, Khalighi M, Maramraju S, Delso G, Levin C . NEMA NU 2-2012 performance studies for the SiPM-based ToF-PET component of the GE SIGNA PET/MR system. Med Phys. 2016; 43(5):2334. DOI: 10.1118/1.4945416. View

18.

Nakamoto Y, Kitajima K, Toriihara A, Nakajo M, Hirata K . Recent topics of the clinical utility of PET/MRI in oncology and neuroscience. Ann Nucl Med. 2022; 36(9):798-803. DOI: 10.1007/s12149-022-01780-2. View

19.

Teoh E, McGowan D, Macpherson R, Bradley K, Gleeson F . Phantom and Clinical Evaluation of the Bayesian Penalized Likelihood Reconstruction Algorithm Q.Clear on an LYSO PET/CT System. J Nucl Med. 2015; 56(9):1447-52. PMC: 4558942. DOI: 10.2967/jnumed.115.159301. View

20.

Kang Z, Min X, Weinreb J, Li Q, Feng Z, Wang L . Abbreviated Biparametric Versus Standard Multiparametric MRI for Diagnosis of Prostate Cancer: A Systematic Review and Meta-Analysis. AJR Am J Roentgenol. 2018; 212(2):357-365. DOI: 10.2214/AJR.18.20103. View