Offset compensated baseline restoration and computationally efficient hybrid interpolation for the brain PET
-
Saeed Mian Qaisar
Abstract
The acquisition of positron emission tomography (PET) pulses introduces artifacts and limits the performance of the scanner. To minimize these inadequacies, this work focuses on the design of an offset compensated digital baseline restorer (BLR) along with a two-stage hybrid interpolator. They respectively treat the incoming pulse offsets and limited temporal resolution and improve the scanner performance in terms of calculating depth of interactions and line of responses. The offset of incoming PET pulses is compensated by the BLR and then their interesting parts are selected. The selected signal portion is up-sampled with a hybrid interpolator. It is composed of an optimized weighted least-squares interpolator (WLSI) and a simplified linear interpolator. The processes of calibrating the WLSI coefficients and characterizing the BLR and the interpolator modules are described. The functionality of the proposed modules is verified with an experimental setup. Results have shown that the devised BLR effectively compensates a dynamic range of bipolar offsets. The signal selection process allows focusing only on the relevant signal part and avoids the unnecessary operations during the post-interpolation process. Additionally, the hybrid nature allows improving the signal temporal resolution with an appropriate precession at a reduced computational complexity compared to the mono-interpolation-based arithmetically complex counterparts. The component-level architectures of the BLR and the interpolator modules are also described. It promises an efficient integration of these modules in modern PET scanners while using standard and economical analog-to-digital converters and field-programmable gate arrays. It avoids the development of high-performance and expensive application-specific integrated circuits and results in a cost-effective realization.
Acknowledgments
The author is thankful to the anonymous reviewers for their valuable feedback and to Drs. D. Liksonov, A. Bakkali, and N. Tamda for the fruitful discussions.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This project was funded by OSEO, UFC, France.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
[1] Watanabe M, Saito A, Isobe T, Ote K, Yamada R, Moriya T, et al. Performance evaluation of a high-resolution brain PET scanner using four-layer MPPC DOI detectors. Phys Med Biol 2017;62:7148.10.1088/1361-6560/aa82e8Suche in Google Scholar PubMed
[2] Gong K, Majewski S, Kinahan PE, Harrison RL, Elston BF, Manjeshwar R, et al. Designing a compact high performance brain PET scanner – simulation study. Phys Med Biol 2016;61:3681.10.1088/0031-9155/61/10/3681Suche in Google Scholar PubMed PubMed Central
[3] Orrison WW, Lewine J, Sanders J, Hartshorne MF. Functional brain imaging. Elsevier Health Sciences, 2017.Suche in Google Scholar
[4] Putzu A, Valtorta S, Di Grigoli G, Haenggi M, Belloli S, Malgaroli A, et al. Regional differences in cerebral glucose metabolism after cardiac arrest and resuscitation in rats using [18F] FDG positron emission tomography and autoradiography. Neurocrit Care 2018;28:370–8.10.1007/s12028-017-0445-0Suche in Google Scholar PubMed
[5] Kobayashi T, Aikata H, Honda F, Nakano N, Nakamura Y, Hatooka M, et al. Preoperative fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography for prediction of microvascular invasion in small hepatocellular carcinoma. J Comput Assist Tomogr 2016;40:524–30.10.1097/RCT.0000000000000405Suche in Google Scholar PubMed
[6] Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, et al. Different partial volume correction methods lead to different conclusions: an 18F-FDG-PET study of aging. Neuroimage 2016;132:334–43.10.1016/j.neuroimage.2016.02.042Suche in Google Scholar PubMed PubMed Central
[7] González-Montoro A, Aguilar A, Cañizares G, Conde P, Hernández L, Vidal LF, et al. Performance study of a large monolithic LYSO PET detector with accurate photon DOI using retroreflector layers. IEEE Trans Radiat Plasma Med Sci 2017;1:229–37.10.1109/TRPMS.2017.2692819Suche in Google Scholar
[8] Eriksson L, Melcher CL. Scintillators for PET and SPECT. In: Physics of PET and SPECT Imaging. CRC Press, 2017:65–84.10.1201/9781315374383-5Suche in Google Scholar
[9] Ronzhin A. High time-resolution photo-detectors for PET applications. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 2016;809:53–7.10.1016/j.nima.2015.11.069Suche in Google Scholar
[10] Slawomir S. Pi. A technical guide to silicon photomultipliers (SiPM) | Hamamatsu Photonics. Hamamatsu Corporation & New Jersey Institute of Technology, 2017.Suche in Google Scholar
[11] Doroud K, Rodriguez A, Williams MC, Zichichi A, Zuyeuski R. Comparative timing measurements of LYSO and LFS to achieve the best time resolution for TOF-PET. In: Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), November. IEEE, 2014:1–6.10.1109/NSSMIC.2014.7430853Suche in Google Scholar
[12] Qaisar SM. An automatic Peltier effect based MPPC gain regulation for brain-PET. In: Electrical and Computing Technologies and Applications (ICECTA), November, International Conference on. IEEE, 2017:1–6.10.1109/ICECTA.2017.8252028Suche in Google Scholar
[13] Qaisar SM. A VHDL based interpolator and base line restorer for a brain-PET scanner. In: Electrical and Computing Technologies and Applications (ICECTA), November, International Conference on. IEEE, 2017:1–6.10.1109/ICECTA.2017.8252056Suche in Google Scholar
[14] Yamamoto S, Imaizumi M, Watabe T, Watabe H, Kanai Y, Shimosegawa E, et al. Development of a Si-PM-based high-resolution PET system for small animals. Phys Med Biol 2010;55:5817.10.1088/0031-9155/55/19/013Suche in Google Scholar
[15] Korcyl G, Moskal P, Bednarski T, Białas P, Czerwiński E, Kapłon Ł, et al. Trigger-less and reconfigurable data acquisition system for positron emission tomography. Bio-Algorithms Med-Syst 2014;10:37–40.10.1515/bams-2013-0115Suche in Google Scholar
[16] Pelgrom MJ. Analog-to-digital conversion. 1st ed. Springer Netherlands, 2010. ASIN: B00BWZ1U4M.10.1007/978-90-481-8888-8Suche in Google Scholar
[17] Moskal P, Bednarski T, Białas P, Czerwiński E, Kapłon Ł, Kochanowski A, et al. A novel method based solely on field programmable gate array (FPGA) units enabling measurement of time and charge of analog signals in positron emission tomography (PET). Bio-Algorithms Med-Syst 2014;10:41–5.10.1515/bams-2013-0104Suche in Google Scholar
[18] Fontaine R, Tetrault MA, Belanger F, Viscogliosi N, Himmich R, Michaud JB, et al. Real time digital signal processing implementation for an APD-based PET scanner with pho switch detectors. IEEE Trans Nucl Sci 2006;53:784–8.10.1109/TNS.2006.875441Suche in Google Scholar
[19] Pullia A, Geraci A, Ripamonti G. Quasi-optimum γ and X spectroscopy based on real-time digital techniques. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 2000;439:378–84.10.1016/S0168-9002(99)00897-9Suche in Google Scholar
[20] Li H, Wang C, Baghaei H, Zhang Y, Ramirez R, Liu S, et al. A new statistics-based online baseline restorer for a high count-rate fully digital system. IEEE Trans Nucl Sci 2010;57:550–5.10.1109/RTC.2009.5321983Suche in Google Scholar
[21] Kawada Y, Yunoki A, Yamada T, Hino Y. Effect of time walk in the use of single channel analyzer/discriminator for saturated pulses in the 4πβ-γ coincidence experiments. Appl Radiat Isotopes 2016;109:369–73.10.1016/j.apradiso.2015.12.024Suche in Google Scholar PubMed
[22] Du J, Schmall JP, Judenhofer MS, Di K, Yang Y, Cherry SR. A time-walk correction method for PET detectors based on leading edge discriminators. IEEE Trans Radiat Plasma Med Sci 2017;1:385–90.10.1109/TRPMS.2017.2726534Suche in Google Scholar PubMed PubMed Central
[23] Kafaee M, Moussavi-Zarandi A. Baseline restoration and pile-up correction based on bipolar cusp-like shaping for high-resolution radiation spectroscopy. J Korean Phys Soc 2016;68:960–4.10.3938/jkps.68.960Suche in Google Scholar
[24] Moskal P, Zoń N, Bednarski T, Białas P, Czerwiński E, Gajos A, et al. A novel method for the line-of-response and time-of-flight reconstruction in TOF-PET detectors based on a library of synchronized model signals. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 2015;775:54–62.10.1016/j.nima.2014.12.005Suche in Google Scholar
[25] Ziskin V, Perticone D. U.S. Patent No. 9,239,303. U.S. Patent and Trademark Office, Washington, DC, 2016.Suche in Google Scholar
[26] Liksonov D, Qaisar SM. Method for calculating interaction time of gamma photon with scintillation crystal in sensor of positron emission tomography system, involves determining curve representing evolution of electrical signal by interpolation of sampling points. January 2013, FR 2983590.Suche in Google Scholar
[27] Braga LH, Gasparini L, Grant L, Henderson RK, Massari N, Perenzoni M, et al. (2014). A Fully Digital 8x16 SiPM Array for PET Applications With Per-Pixel TDCs and Real-Time Energy Output. IEEE J Solid-State Circuits 2014;49:301–14.10.1109/JSSC.2013.2284351Suche in Google Scholar
[28] Braga LH, Gasparini L, Stoppa D. A time of arrival estimator based on multiple timestamps for digital PET detectors. In: Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), October. IEEE, 2012:1250–2.10.1109/NSSMIC.2012.6551306Suche in Google Scholar
[29] Tetrault MA, Oliver JF, Bergeron M, Lecomte R, Fontaine R. Real time coincidence detection engine for high count rate timestamp based PET. IEEE Trans Nucl Sci 2010;57:117–24.10.1109/TNS.2009.2038055Suche in Google Scholar
[30] Solf TJ, Fischer P. U.S. Patent No. 7,750,305. U.S. Patent and Trademark Office, Washington, DC, 2010.Suche in Google Scholar
[31] Frach T, Prescher G. U.S. Patent No. 9,405,024. U.S. Patent and Trademark Office, Washington, DC, 2016.Suche in Google Scholar
[32] Xie Q, Kao CM, Wang X, Guo N, Zhu C, Frisch H, et al. Potential advantages of digitally sampling scintillation pulses in timing determination in PET. In: Nuclear Science Symposium Conference Record, October, NSS’07. Vol. 6. IEEE, 2007;4271–4.10.1109/NSSMIC.2007.4437060Suche in Google Scholar
[33] Polycarpou I, Soultanidis G, Tsoumpas C. Synthesis of realistic simultaneous positron emission tomography and magnetic resonance imaging data. IEEE Trans Med Imaging 2018;37:703–11.10.1109/TMI.2017.2768130Suche in Google Scholar
[34] Pałka M, Strzempek P, Korcyl G, Bednarski T, Niedźwiecki S, Białas P, et al. Multichannel FPGA based MVT system for high precision time (20 ps RMS) and charge measurement. J Instrum 2017;12:P08001.10.1088/1748-0221/12/08/P08001Suche in Google Scholar
[35] Saint-Gobain crystals physical properties of common inorganic scintillators. IEEE Trans Nucl Sci 2017;63:2471–7.Suche in Google Scholar
[36] Du J, Schmall JP, Yang Y, Di K, Roncali E, Mitchell GS, et al. Evaluation of Matrix9 silicon photomultiplier array for small-animal PET. Med Phys 2015:42:585–99.10.1118/1.4905088Suche in Google Scholar
[37] Kamrani E, Lesage F, Sawan M. Low-noise, high-gain transimpedance amplifier integrated with SiAPD for low-intensity near-infrared light detection. IEEE Sensors J 2014;14:258–69.10.1109/JSEN.2013.2282624Suche in Google Scholar
[38] Rivera NC, Seitz B. EP-1445: performance evaluation of scintillators for SiPM PET/MRI brain imaging. Radiother Oncol 2017;123:S771.10.1016/S0167-8140(17)31880-7Suche in Google Scholar
[39] Hanu AR, Prestwich WV, Byun SH. A data acquisition system for two-dimensional position sensitive micropattern gas detectors with delay-line readout. Nucl Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 2015;780:33–9.10.1016/j.nima.2015.01.053Suche in Google Scholar
[40] AD9623, D. S. 12-bit, 170 MSPS/210 MSPS/250 MSPS, 1.8 V dual analog-to-digital converter (ADC).Suche in Google Scholar
[41] Qaisar SM, Yahiaoui R, Gharbi T. An efficient signal acquisition with an adaptive rate A/D conversion. In: IEEE, ICCAS’13, Kuala Lumpur, Malaysia, 2013.10.1109/CircuitsAndSystems.2013.6671611Suche in Google Scholar
[42] Golub GH, Ortega JM. Scientific computing and differential equations: an introduction to numerical methods. London: Academic Press Inc., 2014.Suche in Google Scholar
[43] Statistics, MATLAB and Machine Learning Toolbox Releaseand. 2016. Natick, MA, USA: The MathWorks Inc. Available at: http://www.mathworks.com.Suche in Google Scholar
[44] Sadrozinski HF, Wu J. Applications of field-programmable gate arrays in scientific research. USA: Taylor and Francis, 2016.10.1201/b10480Suche in Google Scholar
©2018 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Research Articles
- Brain abnormality detection using template matching
- Offset compensated baseline restoration and computationally efficient hybrid interpolation for the brain PET
- The similarity of selected statins – a comparative analysis
- Cell proliferation induced by modified cationic dextran
- An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals
Artikel in diesem Heft
- Research Articles
- Brain abnormality detection using template matching
- Offset compensated baseline restoration and computationally efficient hybrid interpolation for the brain PET
- The similarity of selected statins – a comparative analysis
- Cell proliferation induced by modified cationic dextran
- An empirical wavelet transform based approach for multivariate data processing application to cardiovascular physiological signals
