Impact of Point-Spread Function Modeling on PET Image Quality in Integrated PET/MR Hybrid Imaging
Open accessAbstract
UNLABELLED: The aim of this study was to systematically assess the quantitative and qualitative impact of including point-spread function (PSF) modeling into the process of iterative PET image reconstruction in integrated PET/MR imaging. METHODS: All measurements were performed on an integrated whole-body PET/MR system. Three substudies were performed: an (18)F-filled Jaszczak phantom was measured, and the impact of including PSF modeling in ordinary Poisson ordered-subset expectation maximization reconstruction on quantitative accuracy and image noise was evaluated for a range of radial phantom positions, iteration numbers, and postreconstruction smoothing settings; 5 representative datasets from a patient population (total n = 20, all oncologic (18)F-FDG PET/MR) were selected, and the impact of PSF on lesion activity concentration and image noise for various iteration numbers and postsmoothing settings was evaluated; and for all 20 patients, the influence of PSF modeling was investigated on visual image quality and number of detected lesions, both assessed by clinical experts. Additionally, the influence on objective metrics such as changes in SUVmean, SUVpeak, SUVmax, and lesion volume was assessed using the manufacturer-recommended reconstruction settings. RESULTS: In the phantom study, PSF modeling significantly improved activity recovery and reduced the image noise at all radial positions. This effect was measurable only at a high number of iterations (>10 iterations, 21 subsets). In the patient study, again, PSF increased the detected activity in the patient's lesions at concurrently reduced image noise. Contrary to the phantom results, the effect was notable already at a lower number of iterations (>1 iteration, 21 subsets). Lastly, for all 20 patients, when PSF and no-PSF reconstructions were compared, an identical number of congruent lesions was found. The overall image quality of the PSF reconstructions was rated better when compared with no-PSF data. The SUVs of the detected lesions with PSF were substantially increased in the range of 6%-75%, 5%-131%, and 5%-148% for SUVmean, SUVpeak, and SUVmax, respectively. A regression analysis showed that the relative increase in SUVmean/peak/max decreases with increasing lesion size, whereas it increases with the distance from the center of the PET field of view. CONCLUSION: In whole-body PET/MR hybrid imaging, PSF-based PET reconstructions can improve activity recovery and image noise, especially at lateral positions of the PET field of view. This has been demonstrated quantitatively in phantom experiments as well as in patient imaging, for which additionally an improvement of image quality could be observed.
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Conflicting results were reported by other authors who did not find higher lesion conspicuity or detection rates with PSF in small patient samples.
“Conflicting results were reported by other authors who did not find higher lesion conspicuity or detection rates with PSF in small patient samples [162,164,165].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [162]
The Patient Study reports that the 20-patient sample had an identical number of lesions detected with and without PSF, supporting the detection part of the claim. But the Discussion says PSF images showed a considerable improvement in lesion contrast, so this paper does not support the suggestion that lesion conspicuity failed to improve. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPSF compensation can increase the SNR and subjectively rated image quality.
“PSF compensation can also increase the SNR [157,158] and thereby the subjectively rated image quality [157,161,162].”
From Influences on PET Quantification and Interpretation (2022) · cites this paper as [162]
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Cites
Performance Measurements of the Siemens mMR Integrated Whole-Body PET/MR Scanner
Delso, G., Fürst, S., Jakoby, B., Ladebeck, R., Ganter, C., Nekolla, S. G., Schwaiger, M., & Ziegler, S. I. (2011) · Journal of Nuclear Medicine · doi:10.2967/jnumed.111.092726 · Cited by 870 · see citing works
Self-citationFully 3-D PET reconstruction with system matrix derived from point source measurements
Panin, V. Y., Kehren, F., Michel, C., & Casey, M. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.876171 · Cited by 536 · see citing works
Physical and clinical performance of the mCT time-of-flight PET/CT scanner
Jakoby, B., Bercier, Y., Conti, M., Casey, M. E., Bendriem, B., & Townsend, D. W. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/8/004 · Cited by 487 · see citing works
Self-citationDesign and performance evaluation of a whole-body Ingenuity TF PET–MRI system
Zaidi, H., Ojha, N., Morich, M. A., Griesmer, J. J., Hu, Z., Maniawski, P. J., Ratib, O. M., Izquierdo‐Garcia, D., Fayad, Z. A., & Shao, L. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/10/013 · Cited by 400 · see citing works
Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls
Rahmim, A., Qi, J., & Sossi, V. (2013) · Medical Physics · doi:10.1118/1.4800806 · Cited by 354 · see citing works
Akamatsu, G., Ishikawa, K., Mitsumoto, K., Taniguchi, T., Ohya, N., Baba, S., Abe, K., & Sasaki, M. (2012) · Journal of Nuclear Medicine · doi:10.2967/jnumed.112.103861 · Cited by 206 · see citing works
Modeling and incorporation of system response functions in 3-D whole body PET
Alessio, A. M., Kinahan, P. E., & Lewellen, T. K. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.873222 · Cited by 176 · see citing works
Image-based point spread function implementation in a fully 3D OSEM reconstruction algorithm for PET
Rapisarda, E., Bettinardi, V, Thielemans, K., & Gilardi, M. C. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/14/012 · Cited by 142 · see citing works
Quick, H. H. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24523 · Cited by 137 · see citing works
Self-citationPET/MRI hybrid imaging: devices and initial results
Pichler, B. J., Judenhofer, M. S., & Wehrl, H. F. (2008) · European Radiology · doi:10.1007/s00330-008-0857-5 · Cited by 134 · see citing works
Use of a magnetic field to increase the spatial resolution of positron emission tomography
Hammer, B. E., Christensen, N. L., & Heil, B. (1994) · Medical Physics · doi:10.1118/1.597178 · Cited by 115 · see citing works
Analytic system matrix resolution modeling in PET: an application to Rb-82 cardiac imaging
Rahmim, A., Tang, J., Lodge, M. A., Lashkari, S., Ay, M. R., Läutamaki, R., Tsui, B. M. W., & Bengel, F. M. (2008) · Physics in Medicine and Biology · doi:10.1088/0031-9155/53/21/004 · Cited by 80 · see citing works
The Integration of Positron Emission Tomography With Magnetic Resonance Imaging
Cherry, S., Louie, A., & Jacobs, R. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2007.913502 · Cited by 72 · see citing works
Sanders, J. C., Kuwert, T., Joachim Hornegger, A., & Ritt, P. (2014) · Molecular Imaging and Biology · doi:10.1007/s11307-014-0806-4 · Cited by 64 · see citing works
Self-citationClinical evaluation of PET image reconstruction using a spatial resolution model
Andersen, F. L., Klausen, T. L., Loft, A., Beyer, T., & Holm, S. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2012.11.015 · Cited by 62 · see citing works
Whole-body PET/MRI in oncology: current status and clinical applications
Pace, L., Nicolai, E., Aiello, M., Catalano, O. A., & Salvatore, M. (2013) · Clinical and Translational Imaging · doi:10.1007/s40336-013-0012-4 · Cited by 41 · see citing works
Relative role of motion and PSF compensation in whole‐body oncologic PET‐MR imaging
Petibon, Y., Huang, C., Ouyang, J., Reese, T. G., Li, Q., Syrkina, A., Chen, Y., & Fakhri, G. E. (2014) · Medical Physics · doi:10.1118/1.4868458 · Cited by 38 · see citing works
Staelens, S. G., D’Asseler, Y., Vandenberghe, S., Koole, M. J. B., Lemahieu, I. A., & Rik Van De Walle, B. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/49/11/015 · Cited by 33 · see citing works
Study of a high-resolution PET system using a Silicon detector probe
Brzeziński, K., Oliver, J., Gillam, J. E., & Rafecas, M. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/20/6117 · Cited by 14 · see citing works
Measurement of the physical PSF for an integrated PET/MR using targeted positron beams
Watson, C. C. (2012) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nssmic.2012.6551480 · Cited by 7 · see citing works
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Parisi, M. T., Bermo, M. S., Alessio, A. M., Sharp, S. E., Gelfand, M. J., & Shulkin, B. L. (2017) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2017.01.002 · Cited by 71 · see citing works
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Moving the goalposts while scoring―the dilemma posed by new PET technologies
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Zucchetta, P., Branchini, M., Zorz, A., Bodanza, V., Cecchin, D., Paiusco, M., & Bui, F. (2019) · British Journal of Radiology · doi:10.1259/bjr.20180438 · Cited by 18 · see citing works
Li, Y., Wu, J., Ma, J., Li, S., Xue, X., Dong, W., Shan, C., Hua, X., Zheng, M., & Xu, J. (2022) · Diabetes · doi:10.2337/db21-0600 · Cited by 18 · see citing works
Hu, J., Zhang, M., Zhang, Y., Zhuang, H., Zhao, Y., Li, Y., Jin, W., Qian, X., Wang, L., Ye, G., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14137 · Cited by 16 · see citing works
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Mannheim, J. G., Cheng, J.-C. (., Vafai, N. M., Shahinfard, E., English, C., McKenzie, J., Zhang, J., Barlow, L., & Sossi, V. (2021) · Figshare · doi:10.6084/m9.figshare.14129408
- Authors
- Aklan, B., Oehmigen, M., Beiderwellen, K., Ruhlmann, M., Paulus, D. H., Jakoby, B. W., Ritt, P., & Quick, H. H.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Essen University Hospital (DE)
- University of Surrey (GB)
- Universitätsklinikum Erlangen (DE)
- Venue
- Journal of Nuclear Medicine
- Publisher
- Society of Nuclear Medicine
- Published
- 2015-10-15
- Volume
- 57
- Issue
- 1
- Pages
- 78-84
- ISSN
- 0161-5505 (ISSN-L)
- 2159-662X
- OpenAlex
- W2169302293
- Cited by
- 25
- References
- 20
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 5 · 2020: 1 · 2019: 2 · 2018: 4 · 2017: 3 · 2016: 1
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Nuclear Medicine (publishedVersion)
- PubMed (publishedVersion)
- Universitätsbibliographie, Universität Duisburg-Essen (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
- Keywords
- point spread function modeling, PET/MR imaging, OSEM, standardized uptake value, image noise, lesion detection, PET image quality, 18F-FDG, whole-body PET, quantitative accuracy, tumor imaging, SUVmax, SUVmean, SUVpeak, lesion volume
- MeSH terms
- Adult, Aged, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Theoretical, Neoplasms, Phantoms, Imaging, Fluorodeoxyglucose F18, Positron-Emission Tomography, Multimodal Imaging
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