Systematic evaluation of human soft tissue attenuation correction in whole-body PET/MR: Implications from PET/CT for optimization of MR-based AC in patients with normal lung tissue.

AC in PET/CT attenuation correction (AC) improved soft tissue AC lung AC in PET/MR quantitative PET/MR whole-body PET/MR

Journal

Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746

Informations de publication

Date de publication:
07 Dec 2023
Historique:
revised: 10 11 2023
received: 04 05 2023
accepted: 16 11 2023
medline: 7 12 2023
pubmed: 7 12 2023
entrez: 7 12 2023
Statut: aheadofprint

Résumé

Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies. Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%). The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

Sections du résumé

BACKGROUND BACKGROUND
Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons.
PURPOSE OBJECTIVE
The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies.
METHODS METHODS
Data of n = 60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated.
RESULTS RESULTS
The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to < ±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%).
CONCLUSIONS CONCLUSIONS
The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

Identifiants

pubmed: 38060671
doi: 10.1002/mp.16863
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.

Références

Catana C, Quick HH, Zaidi H. Current commercial techniques for MRI-guided attenuation correction are insufficient and will limit the wider acceptance of PET/MRI technology in the clinic. Med Phys. 2018;45(9): 4007-4010. doi:10.1002/mp.12963
Mehranian A, Arabi H, Zaidi H. Vision 20/20: magnetic resonance imaging-guided attenuation correction in PET/MRI: challenges, solutions, and opportunities. Med Phys. 2016;43(3):1130-1155.
Sarabhai T, Tschischka A, Stebner V, et al. Simultaneous multiparametric PET/MRI for the assessment of therapeutic response to chemotherapy or concurrent chemoradiotherapy of cervical cancer patients: preliminary results. Clin Imaging. 2018;49:163-168.
Owrangi AM, Greer PB, Glide-Hurst CK. MRI-only treatment planning: benefits and challenges. Med Phys Biol. 2019;63:05TR01. doi:10.1088/1361-6560/aaaca4
Carney J, Townsend D, Rappoport V, Bendriem B. Method for transforming CT images for attenuation correction in PET/CT imaging. Med Phys. 2006;33:976-983.
Burger C, Goerres GW, Schoenes S, et al. PET attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients. Eur J Nucl Med Mol Imaging. 2002;29:922-927.
Kamel EM, Burger C, Buck A, et al. Impact of metallic dental implants on CT-based attenuation correction in a combined PET/CT scanner. Eur Radiol. 2003;13:724-728.
Brusaferri L, Bousse A, Efthimiou N, et al. Potential benefits of incorporating energy information when estimating attenuation from PET data. Paper presented at: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC); 2017; Atlanta, USA; 1-4. doi:10.1109/NSSMIC.2017.8532765
Efthimiou N, Karp JS, Surti S. Data-driven, energy-based method for estimation of scattered events in positron emission tomography. Phys Med Biol. 2022;67(9):10. 10.1088/1361-6560/ac62fc
Brusaferri L, Bousse A, Emond E, et al. Joint activity and attenuation reconstruction from multiple energy window data with photopeak scatter re-estimation in non-TOF 3-D PET. IEEE Trans Radiat Plasma Med Sci. 2020;4(4):410-421.
Popescu LM, Lewitt RM, Matej S, Karp JS. PET energy-based scatter estimation and image reconstruction with energy-dependent corrections. Phys Med Biol. 2006;51(11):2919-2937.
Hamill JJ. Phantom evaluation of energy-based scatter estimation in an SiPM PET scanner. Paper presented at: IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC); 2020; Boston, USA; 1-7. doi:10.1109/NSS/MIC42677.2020.9507822
Álvarez-Gómez JM, Santos-Blasco J, Moliner Martínez L, Rodríguez-Álvarez MJ. Fast energy dependent scatter correction for list-mode PET data. J Imaging. 2021;7(10):199. doi:10.3390/jimaging7100199
Catana C. Attenuation correction for human PET/MRI studies. Phys Med Biol. 2020;65:23TR02.
Beyer T, Lassen ML, Boellaard R, et al. Investigating the state-of-the-art in whole-body MR-based attenuation correction: an intra-individual, inter-system, inventory study on three clinical PET/MR systems. MAGMA. 2016;29:75-87.
Martinez-Möller A, Souvatzoglou M, Delso G, et al. Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med. 2009;50:520-526.
Wollenweber SD, Ambwani S, Lonn AHR, et al. Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction. IEEE Trans Nucl Sci. 2013;60:3391-3398.
Schulz V, Torres-Espallardo I, Renisch S, et al. Automatic, three-segment, MR-based attenuation correction for whole-body PET/MR data. Eur J Nucl Med Mol Imaging. 2011;38:138-152.
Freitag MT, Fenchel M, Bäumer P, et al. Improved clinical workflow for simultaneous whole-body PET/MRI using high-resolution CAIPIRINHA-accelerated MR-based attenuation correction. Eur J Radiol. 2017;96:12-20.
Oehmigen M, Lindemann ME, Gratz M, et al. Impact of improved attenuation correction featuring a bone atlas and truncation correction on PET quantification in whole-body PET/MR. Eur J Nucl Med Mol Imaging. 2017;45:642-653.
Grafe H, Lindemann ME, Ruhlmann V, et al. Evaluation of improved attenuation correction in whole-body PET/MR on patients with bone metastasis using various radiotracers. Eur J Nucl Med Mol Imaging. 2020. doi:10.1007/s00259-020-04738-6
Arabi H, Zaidi H. One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI. Eur J Nucl Med Mol Imaging. 2016;43:2021-2035.
Paulus DH, Quick HH, Geppert C, et al. Whole-body PET/MR imaging: quantitative evaluation of a novel model-basedMR attenuation correction method including bone. J Nucl Med. 2015;56:1061-1066.
Keller SH, Holm S, Hansen AE, et al. Image artifacts from MR-based attenuation correction in clinical, whole-body PET/MRI. MAGMA. 2013;26(1):173-181.
Brendle C, Schmidt H, Oergel A, et al. Segmentation-based attenuation correction in PET/MR: erroneous tissue identification and its impact on positron emission tomography interpretation. Invest Radiol. 2015;50(5):339-346.
Ladefoged CN, Hansen AE, Keller SH, et al. Impact of incorrect tissue classification in Dixon-based MR-AC: fat-water tissue inversion. EJNMMI Phys. 2014;1(1):101.
Bruckmann NM, Lindemann ME, Grueneisen J, et al. Comparison of pre- and post-contrast-enhanced attenuation correction using a CAIPI-accelerated T1-weighted Dixon 3D-VIBE sequence in 68Ga-DOTATOC PET/MRI. Eur J Radiol. 2021;139:109691. 10.1016/j.ejrad.2021.109691
Lillington J, Brusaferri L, Kläser K, et al. PET/MRI attenuation estimation in the lung: a review of past, present, and potential techniques. Med Phys. 2020;47:790-811.
Lindemann ME, Oehmigen M, Blumhagen JO, Gratz M, Quick HH. MR-based truncation and attenuation correction in integrated PET/MR hybrid imaging using HUGE with continuous table motion. Med Phys. 2017;44:4559-4572.
Lindemann ME, Gratz M, Blumhagen JO, Jakoby B, Quick HH. MR-based truncation correction using an advanced HUGE method to improve attenuation correction in PET/MR imaging of obese patients. Med Phys. 2022;49(2):865-877.
Seith F, Schmidt H, Gatidis S, et al. SUV-quantification of physiological lung tissue in an integrated PET/MR-system: impact of lung density and bone tissue. PLoS ONE. 2017;12(5):e0177856. doi:10.1371/journal.pone.0177856
Izquierdo-Garcia D, Sawiak S, Knesaurek K, et al. Comparison of MR-based attenuation correction vs. CT-based attenuation correction of Whole Body PET/MR imaging. Eur J Nucl Med Mol Imaging. 2014;41:1574-1584.
Izquierdo-Garcia D, Catana C. Magnetic resonance imaging-guided attenuation correction of positron emission tomography data in PET/MRI. PET Clin. 2016;11:129-149.
Mehranian A, Zaidi H. Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR. Phys Med Biol. 2015;60(12):4813-4833.
Keereman V, Holen RV, Mollet P, Vandenberghe S. The effect of errors in segmented attenuation maps on PET quantification. Med Phys. 2011;38(11):6010-6019.
Arabi H, Zaidi H. Application of artificial intelligence and deep learning in molecular imaging and radiotherapy. Eur J Hybrid Imaging. 2020;4:17.
Bradshaw TJ, Zhao G, Jang H, et al. Feasibility of deep learning-based PET/MR attenuation correction in the pelvis using only diagnostic MR images. Tomography. 2018;4:138-147. https://doi.org/10.18383/j.tom.2018.00016
Liu F, Jang F, Kijowski R, et al. Deep learning MR Imaging-based attenuation correction for PET/MR imaging. Radiology. 2018;286:676-684.
Torrado-Carvajal A, Herraiz JL, Alcain E, et al. Fast patch-based pseudo-CT synthesis from T1-weighted MR images for PET/MR attenuation correction in brain studies. J Nucl Med. 2016;57:136-143.
Torrado-Carvajal A, Vera-Olmos J, Izquierdo-Garcia D, et al. Dixon-VIBE deep learning (DIVIDE) pseudo-CT synthesis for pelvis PET/MR attenuation correction. J Nucl Med. 2019;60:429-435.
Arabi H, Zaidi H. MRI-guided attenuation correction in torso PET/MRI: assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers. Magn Reson Med. 2021;87:686-701.
Buchbender C, Hartung-Knemeyer V, Forsting M, et al. Positron emission tomography (PET) attenuation correction artefacts in PET/CT and PET/MRI. Br J Radiol. 2013;86:20120570.
Hofmann M, Bezrukov I, Mantlik F, et al. MRI-based attenuation correction for whole-body PET/MRI: quantitative evaluation of segmentation- and atlas-based methods. J Nucl Med. 2011;52:1392-1399.
Lindemann ME, Nensa F, Quick HH. Impact of improved attenuation correction on 18F-FDG PET/MR hybrid imaging of the heart. PLoS ONE. 2019;14(3):e0214095.
Grafe H, Lindemann ME, Weber M, et al. Intra-individual comparison of 124I-PET/CT and 124I-PET/MR hbrid imaging of patients with resected differentiated thyriod carcinoma: aspects of attenuation correction. Cancers. 2022;14:3040. 10.3390/cancers14133040
Samarin A, Burger C, Wollenweber SD, et al. PET/MR imaging of bone lesions-implications for PET quantification from imperfect attenuation correction. Eur J Nucl Med Mol Imaging. 2012;39:1154-1160.
Keereman V, Fierens Y, Vanhove C, et al. Magnetic resonace-based attenuation correction for micro-single-photon emission computed tomography. Mol Imaging. 2012;11(2):155-165.
Zeimpekis K, Delso G, Wiesinger F, et al. Investigation of 3D UTE MRI for lung PET attenuation correction. J Nucl Med. 2014;55:2103.
Rischpler C, Nekolla SG, Heusch G, et al. Cardiac PET/MRI-an update. Eur J Hybrid Imaging. 2019. doi:10.1186/s41824-018-0050-2

Auteurs

Maike E Lindemann (ME)

High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany.

Marcel Gratz (M)

High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany.

Hong Grafe (H)

Department of Nuclear Medicine, University Hospital Essen, University Duisburg-Essen, Essen, Germany.

Kai Jannusch (K)

Department of Diagnostic and Interventional Radiology, University Hospital Duesseldorf, University Duesseldorf, Duesseldorf, Germany.

Lale Umutlu (L)

Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.

Harald H Quick (HH)

High-Field and Hybrid MR Imaging, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany.

Classifications MeSH