Partial volume effect and correction

Partial volume effect (PVE) is a combination of two factors, the limited resolution of PET, and image sampling. Image sampling refers to the fact that PET voxel has a definite volume, which may consist only partially of the desired tissue, reflecting underlying tissue heterogeneity (Aston et al., 2002). Together these factors blur the images. Multiple tissue types contribute to the measured radioactivity concentration of even single voxels, and more so to the volumes-of-interest (VOI) consisting of many voxels.

Typically PVE is seen in tumour and brain imaging as spill-out of radioactivity into surrounding tissue from a high-activity region (tumour, brain cortex), leading to underestimation of tracer uptake estimates. It can also be seen as spill-in (spill-over) into VOI from high-activity region (heart cavities, large arteries and veins, urine), leading to overestimation of tracer uptake estimates in for example myocardial muscle and bladder wall. This is also the major hindrance in extracting blood curve from arteries that are visible in PET image.


Figure 1. Spill-out from a high-activity area leads to reduced peak activity inside the object, if object is small in relation to the spatial resolution, and to even more reduced VOI average if VOI is defined based on true object size.


Figure 2. If surrounding tissue has higher radioactivity concentration than the VOI, then spill-in (spill-over) leads to overestimated concentration inside the volume-of-interest.

spill-out and spill-in

Figure 3. Spill-out and spill-in effects mostly cancel out, if radioactivity concentrations surrounding the object are similar, reducing the over- or underestimation. However, then image contrast is poor, which would make manual ROI-definition to PET image difficult.

Partial volume correction

Partial volume correction (PVC) becomes important when the object size is less than two times the spatial resolution (FWHM) in the image. Correction may be based on empiric recovery coefficients (RCs) (Geworski et al., 2000). More accurate correction is possible if the point-spread function (PSF) of the tomograph is known. Addressing the tissue-fraction effect requires coregistered high-resolution MR images (Rousset et al., 1998). The partial volume correction is studied in detail by Aston et al (2002). One of the international software projects is PVEOut. Alternative methods have been implemented, including accounting for PSF in the image reconstruction process; however, current PSF reconstruction may lead to serious image artifacts (Munk et al., 2017). Software for spill-over correction of PET images with simple deconvolution method, based only on image resolution (FWHM) is already available in TPC (contact Harri Merisaari).

There are several studies on the impact of PVE on clinical PET results, especially in oncology and brain research. For example, in [18F]FDOPA studies PVE leads to severe underestimation of Ki and k3D in certain brain structures, thus obscuring regional heterogeneity in the neurochemical pathology of Parkinson’s disease (Rousset et al 2000).

In brain studies, K1/k2 in regions of interest is often fixed to a value estimated first in the reference region. Because PVE is different between brain structures, this may lead into biases. For example, PVC increased the K1/k2 of cerebral cortex by 35 % in [18F]FDOPA studies (Rousset et al., 2000).

However, measured receptor occupancy is independent of partial volume effect (Martinez et al 2001).

Proper PVC is of critical importance in accurate quantitative PET, especially in aging studies, where the apparent reduction in metabolic activity is disappeared after PVC (Giovacchini et al., 2004).

Similar error sources

Different attenuation correction methods may result in apparent changes in local radioactivity concentrations, which may be misinterpreted as PVE. For instance, using CT-based attenuation correction may produce higher activities than Germanium source based attenuation correction, especially in radiodense tissue like bone (Nakamoto et al., 2002). Also movement between transmission measurement and PET scan may lead to spurious results.

See also:


Aston JAD, Cunningham VJ, Asselin M-C, Hammers A, Evans AC, Gunn RN. Positron emission tomography partial volume correction: estimation and algorithms. J Cereb Blood Flow Metab. 2002; 22: 1019-1034.

Bowen SL, Byars LG, Michel CJ, Chonde DB, Catana C. Influence of the partial volume correction method on 18F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM. Phys Med Biol. 2013; 58: 7081-7106.

Erlandsson K, Dickson J, Arridge S, Atkinson D, Ourselin S, Hutton BF. MR imaging-guided partial volume correction of PET data in PET/MR imaging. PET Clin. 2016; 11: 161-177. doi: 10.1016/j.cpet.2015.09.002.

Funck T, Larcher K, Toussaint PJ, Evans AC, Thiel A. APPIAN: Automated Pipeline for PET Image Analysis. Front Neuroinform. 2018; 12: 64. doi: 10.3389/fninf.2018.00064.

Geworski L, Knoop BO, de Cabrejas ML, Knapp WH, Munz DL. Recovery correction for quantitation in emission tomography: a feasibility study. Eur J Nucl Med. 2000; 27(2): 161-169.

Gigengack F, Jiang X, Dawood M, Schäfers KP: Motion Correction in Thoracic Positron Emission Tomography. Springer, 2015. doi: 10.1007/978-3-319-08392-6.

Giovacchini G, Lerner A, Toczek MT, Fraser C, Ma K, DeMar JC, Herscovitch P, Eckelman WC, Rapoport SI, Carson RE. Brain incorporation of 11C-arachidonic acid, blood volume, and blood flow in healthy aging: a study with partial-volume correction. J Nucl Med. 2004; 45: 1471-1479.

Golla SVS, Lubberink M, van Berckel BNM, Lammertsma AA, Boellaard R. Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising. EJNMMI Res. 2017; 7: 36.

Hofheinz F, Langner J, Petr J, Beuthien-Baumann B, Oehme L, Steinbach J, Kotzerke J, van den Hoff J. A method for model-free partial volume correction in oncological PET. EJNMMI Res. 2012; 2:16.

Kessler RM, Ellis JR Jr, Eden M. Analysis of emission tomographic scan data: limitations imposed by resolution and background. J Comput Assist Tomogr. 1984; 8: 514-522.

Martinez D, Hwang D-R, Mawlawi O, Slifstein M, Kent J, Simpson N, Parsey RV, Hashimoto T, Huang Y, Shinn A, Van Heertum R, Abi-Dargham A, Caltabiano S, Malizia A, Cowley H, Mann JJ, Laruelle M. Differential occupancy of somatodendritic and postsynaptic 5HT1A receptors by pindolol: a dose-occupancy study with [11C]WAY 100635 and positron emission tomography in humans. Neuropsychopharmacology 2001; 24:209-229.

Merisaari H. Algorithmic analysis techniques for molecular imaging. Turku Centre for Computer Science, TUCS Dissertations, 217, 2016.

Munk OL, Tolbod LP, Hansen SB, Bogsrud TV. Point-spread function reconstructed PET images of sub-centimeter lesions are not quantitative. EJNMMI Physics 2017; 4:5. doi: 10.1186/s40658-016-0169-9.

Nakamoto Y, Osman M, Cohade C, Marshall LT, Links JM, Kohlmyer S, Wahl RL. PET/CT: comparison of quantitative tracer uptake between germanium and CT transmission attenuation-corrected images. J Nucl Med. 2002; 43(9): 1137-1143.

Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998; 39(5): 904-911.

Rousset OG, Deep P, Kuwabara H, Evans AC, Gjedde AH, Cumming P. Effect of partial volume correction on estimates of the influx and cerebral metabolism of 6-[18F]fluoro-L-dopa studied with PET in normal control and Parkinson’s disease subjects. Synapse 2000; 37: 81-89.

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Created at: 2004-09-09
Updated at: 2018-11-08
Written by: Vesa Oikonen