Partial volume correction of brain PET studies using iterative deconvolution in combination with HYPR denoising

Springer Science and Business Media LLC - Tập 7 - Trang 1-12 - 2017
Sandeep S. V. Golla1, Mark Lubberink2, Bart N. M. van Berckel1, Adriaan A. Lammertsma1, Ronald Boellaard1,3
1Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
2Nuclear Medicine and PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
3Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Tóm tắt

Accurate quantification of PET studies depends on the spatial resolution of the PET data. The commonly limited PET resolution results in partial volume effects (PVE). Iterative deconvolution methods (IDM) have been proposed as a means to correct for PVE. IDM improves spatial resolution of PET studies without the need for structural information (e.g. MR scans). On the other hand, deconvolution also increases noise, which results in lower signal-to-noise ratios (SNR). The aim of this study was to implement IDM in combination with HighlY constrained back-PRojection (HYPR) denoising to mitigate poor SNR properties of conventional IDM. An anthropomorphic Hoffman brain phantom was filled with an [18F]FDG solution of ~25 kBq mL−1 and scanned for 30 min on a Philips Ingenuity TF PET/CT scanner (Philips, Cleveland, USA) using a dynamic brain protocol with various frame durations ranging from 10 to 300 s. Van Cittert IDM was used for PVC of the scans. In addition, HYPR was used to improve SNR of the dynamic PET images, applying it both before and/or after IDM. The Hoffman phantom dataset was used to optimise IDM parameters (number of iterations, type of algorithm, with/without HYPR) and the order of HYPR implementation based on the best average agreement of measured and actual activity concentrations in the regions. Next, dynamic [11C]flumazenil (five healthy subjects) and [11C]PIB (four healthy subjects and four patients with Alzheimer’s disease) scans were used to assess the impact of IDM with and without HYPR on plasma input-derived distribution volumes (V T) across various regions of the brain. In the case of [11C]flumazenil scans, Hypr-IDM-Hypr showed an increase of 5 to 20% in the regional V T whereas a 0 to 10% increase or decrease was seen in the case of [11C]PIB depending on the volume of interest or type of subject (healthy or patient). References for these comparisons were the V Ts from the PVE-uncorrected scans. IDM improved quantitative accuracy of measured activity concentrations. Moreover, the use of IDM in combination with HYPR (Hypr-IDM-Hypr) was able to correct for PVE without increasing noise.

Tài liệu tham khảo

Levin CS, Hoffman EJ. Calculation of positron range and its effect on the fundamental limit of positron emission tomography system spatial resolution. Phys Med Biol. 1999;44(3):781–99. Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr. 1979;3(3):299–308. Turkington TG. Introduction to PET instrumentation. J Nucl Med Technol. 2001;29(1):4–11. Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol. 2012;57(21):R119–59. Muller-Gartner HW, Links JM, Prince JL, Bryan RN, McVeigh E, Leal JP, Davatzikos C, Frost JJ. Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects. J Cereb Blood Flow Metab. 1992;12(4):571–83. Meltzer CC, Leal JP, Mayberg HS, Wagner Jr HN, Frost JJ. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14(4):561–70. Meltzer CC, Zubieta JK, Links JM, Brakeman P, Stumpf MJ, Frost JJ. MR-based correction of brain PET measurements for heterogeneous gray matter radioactivity distribution. J Cereb Blood Flow Metab. 1996;16(4):650–8. Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, Becker JA, Svarer C, Knudsen GM, Sperling RA, Johnson KA. Different partial volume correction methods lead to different conclusions: an (18)F-FDG-PET study of aging. Neuroimage. 2016;132:334–43. Teo BK, Seo Y, Bacharach SL, Carrasquillo JA, Libutti SK, Shukla H, Hasegawa BH, Hawkins RA, Franc BL. Partial-volume correction in PET: validation of an iterative postreconstruction method with phantom and patient data. J Nucl Med. 2007;48(5):802–10. Hoetjes NJ, van Velden FH, Hoekstra OS, Hoekstra CJ, Krak NC, Lammertsma AA, Boellaard R. Partial volume correction strategies for quantitative FDG PET in oncology. Eur J Nucl Med Mol Imaging. 2010;37(9):1679–87. Sureau FC, Reader AJ, Comtat C, Leroy C, Ribeiro MJ, Buvat I, Trebossen R. Impact of image-space resolution modeling for studies with the high-resolution research tomograph. J Nucl Med. 2008;49(6):1000–8. Mourik JE, Lubberink M, van Velden FH, Kloet RW, van Berckel BN, Lammertsma AA, Boellaard R. In vivo validation of reconstruction-based resolution recovery for human brain studies. J Cereb Blood Flow Metab. 2010;30(2):381–9. Tohka J, Reilhac A. Deconvolution-based partial volume correction in Raclopride-PET and Monte Carlo comparison to MR-based method. Neuroimage. 2008;39(4):1570–84. Sakaguchi K, Shinohara H, Hashimoto T, Yokoi T, Uno K. An iterative reconstruction using median root prior and anatomical prior from the segmented mu-map for count-limited transmission data in PET imaging. Ann Nucl Med. 2008;22(4):269–79. Reilhac A, Charil A, Wimberley C, Angelis G, Hamze H, Callaghan P, Garcia MP, Boisson F, Ryder W, Meikle SR, Gregoire MC. 4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging. Neuroimage. 2015;118:484–93. Christian BT, Vandehey NT, Floberg JM, Mistretta CA. Dynamic PET denoising with HYPR processing. J Nucl Med. 2010;51(7):1147–54. Floberg JM, Mistretta CA, Weichert JP, Hall LT, Holden JE, Christian BT. Improved kinetic analysis of dynamic PET data with optimized HYPR-LR. Med Phys. 2012;39(6):3319–31. Mistretta CA, Wieben O, Velikina J, Block W, Perry J, Wu Y, Johnson K, Wu Y. Highly constrained backprojection for time-resolved MRI. Magn Reson Med. 2006;55(1):30–40. Boussion N, Le Cheze RC, Hatt M, Visvikis D. Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging. Eur J Nucl Med Mol Imaging. 2009;36(7):1064–75. Yaqub M, Lubberink M, Van Velden FHP, Huisman MC, Lammertsma AA, Boellaard R. Multi scanner comparison using the Hoffman 3D brain phantom. EJNMMI. 2010;37(2):198–311. Ref Type: Abstract. van Velden FH, Mansor SM, van Assema DM, van Berckel BN, Froklage FE, Wang S, Schuit RC, Asselin MC, Lammertsma AA, Boellaard R, Huisman MC. Comparison of HRRT and HR+ scanners for quantitative (R)-[11C]verapamil, [11C]raclopride and [11C]flumazenil brain studies. Mol Imaging Biol. 2015;17(1):129–39. Kolthammer JA, Su KH, Grover A, Narayanan M, Jordan DW, Muzic RF. Performance evaluation of the Ingenuity TF PET/CT scanner with a focus on high count-rate conditions. Phys Med Biol. 2014;59(14):3843–59. Yaqub M, Tolboom N, Boellaard R, van Berckel BN, van Tilburg EW, Luurtsema G, Scheltens P, Lammertsma AA. Simplified parametric methods for [11C]PIB studies. Neuroimage. 2008;42(1):76–86. Boellaard R, van Lingen A, van Balen SC, Hoving BG, Lammertsma AA. Characteristics of a new fully programmable blood sampling device for monitoring blood radioactivity during PET. EurJNuclMed. 2001;28(1):81–9. Svarer C, Madsen K, Hasselbalch SG, Pinborg LH, Haugbol S, Frokjaer VG, Holm S, Paulson OB, Knudsen GM. MR-based automatic delineation of volumes of interest in human brain PET images using probability maps. Neuroimage. 2005;24(4):969–79. Hammers A, Allom R, Koepp MJ, Free SL, Myers R, Lemieux L, Mitchell TN, Brooks DJ, Duncan JS. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp. 2003;19(4):224–47. Logan J, Fowler JS, Volkow ND, Wolf AP, Dewey SL, Schlyer DJ, MacGregor RR, Hitzemann R, Bendriem B, Gatley SJ. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(-)-cocaine PET studies in human subjects. JCerebBlood Flow Metab. 1990;10(5):740–7. Chan C, Fulton R, Feng DD, Meikle S. Regularized image reconstruction with an anatomically adaptive prior for positron emission tomography. Phys Med Biol. 2009;54(24):7379–400. Kirov AS, Piao JZ, Schmidtlein CR. Partial volume effect correction in PET using regularized iterative deconvolution with variance control based on local topology. Phys Med Biol. 2008;53(10):2577–91. Slifstein M, Laruelle M. Effects of statistical noise on graphic analysis of PET neuroreceptor studies. J Nucl Med. 2000;41(12):2083–8. Quarantelli M, Berkouk K, Prinster A, Landeau B, Svarer C, Balkay L, Alfano B, Brunetti A, Baron JC, Salvatore M. Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. J Nucl Med. 2004;45(2):192–201. Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39(5):904–11.