Portal de Periódicos da CAPES

Abstracts of the Total Body PET conference 2018

2018; Springer Science+Business Media; Volume: 5; Issue: S1 Linguagem: Inglês

10.1186/s40658-018-0218-7

2197-7364

Digital Radiography and Breast Imaging

Part 2: Regular abstractsA1 Single-chip tomographic data processing platformGrzegorz Korcyl (grzegorz.korcyl@uj.edu.pl)Department of Information Technologies, Jagiellonian University, Kraków, Poland Background Efforts in tomographic data processing, both scientific and commercial are directed towards fastest generation of high-quality images. For this purpose, many sophisticated algorithms have been developed with Maximum Likelihood Expectation Maximization (MLEM) and Ordered Subsets MLEM (OSEM) being mostly evaluated [1, 2]. They are heavy computational iterative procedures, therefore requiring significant CPU and GPU power in order to deliver reconstructed image within reasonable amount of time [3].Current developments in scanners technology, mostly the researches on whole-body and three-dimensional data acquisition create new challenges for data processing systems. Extended Field-Of-View (FOV) and voxelization of large volumes renders current techniques inefficient in terms of required computing power and memory capacity and consequently required space, increased power consumption and costs [4, 5]. Materials and Methods Presented project is a new approach to the tomographic data processing chain in which all necessary steps towards image reconstruction are enclosed in a compact package working in real-time regime.The introduction of System-on-Chip FPGA devices with integrated ARM processors [6], allows to benefit from high amount of programmable logic resources for real-time 2.1.1.2.processing of the raw detector data and run high-level data analysis in the same time and in the same chip. Results We have developed a proof-of-concept system and demonstrated its operation on the J-PET scanner [7, 8]. The system processes 8 data streams performing following steps: Parsing raw data from digitizing electronics Extraction of the hits on the scanner channels Application of scanner geometry and calibration parameters Coincidence search for Line-of-Response (LOR) candidates Region-of-Response (ROR) calculation Transfer ROR data to the shared memory for visualization Transfer ROR data to external storage Visualization of the gathered data in a form of histograms and 3D point cloud The measurements show that the system is capable to process up to 42 MHits per second. Comparison between GATE simulations and measurements show agreement in terms of the estimated registered LORs and the number of LORs processed in a measurement under the same conditions. Data quality is verified by comparing a naïve reconstruction performed in the FPGA logic to the result of MLEM, computed on the same data set. Conclusions In the presence of whole-body, three-dimensional scanners, there is a need for exploring alternative data processing solutions. Computing platforms based on FPGA devices are perfectly suitable for processing multiple data streams in real-time, significantly reduce the generated data volume and generate instant visualization of the measurement. Acknowledgments This project has been developed on behalf of the J-PET Collaboration.TRB platform together with accompanying firmware and software is developed by the TRB3 Collaboration (trb.gsi.de).This project could be realized thanks to the support from Xilinx University Program and Altera University Program.We acknowledge support by the National Science Centre through the grant No. 2016/21/B/ST2/01222, by the National Centre for Research and Development through grants Nos. INNOTECH-K1/IN1/64/159174/NCBR/12, LIDER/274/L-6/14/NCBR/2015 and by the Ministry for Science and Higher Education through grants Nos. 6673/IA/SP/2016 - IA/SP/01555/2016, 7150/E-338/SPUB/2017/I and The Foundation for Polish Science (MPD). References 1. L. A. Shepp, Y. Vardi, “Maximum Likelihood Reconstruction for Emission Tomography”, Trans. Med Imaging, vol. 1.2, pp. 113-122, Oct. 19822. H. M. Hudson, R. S. Larkin, “Accelerated image reconstruction using ordered subsets of projection data”, IEEE Trans. Med. Imaging, vol. 13, pp. 601-609, 19943. B. Goldschmidt, et al., “Software-Based Real-Time Acquisition and Processing of PET Detector Raw Data”, IEEE Trans Biomedical Eng., vol. 63, pp. 316- 326, Feb. 20164. P. Slomka, T. Pan, G. Germano, “Recent Advances and Future Progress in PET Instrumentation”, Semin. Nucl. Med., vol. 46, 20165. S. R. Cherry, et al., “Total-Body PET: Maximizing Sensitivity to Create New Opportunities for clinical Research and Patient Care”, J. Nucl. Med., vol. 59, pp. 3-12, Jan, 20186. B. Dammak, et al., “Hardware Resource Utilization Optimization in FPGA Heterogeneous MPSoC Architectures”, Microprocessors and Microsystems, vol. 39, pp. 108-1118, June 20157. P. Moskal, et al., “Novel detector systems for the Positron Emission Tomography”, Bio-Algorithms and Med-Systems, vol. 7, pp. 73-78, 20118. Sz. Niedzwiecki, et al., “J-PET: A New Technology for the Whole-Body PET Imaging”, Acta Phys. Polon. B., vol. 48, pp. 1567-1576, 2017A2 Timing calibration in TOF-PET using data consistency: the 3D caseMichel Defrise1, Ahmadreza Rezaei2, Johan Nuyts2 1Department of Nuclear Medicine, Vrije Universiteit Brussel, B-1090, Brussels, Belgium; 2Department of Nuclear Medicine, KU Leuven, B-3000, Leuven, Belgium Correspondence: Michel DefriseThe time calibration of a TOF-PET scanner is usually done by measuring a known activity distribution [1, 2, 3, 4]. Alternative, data driven, methods can be useful to monitor possible drifts of the TOF offsets directly from clinical data. A simple data driven method [5], [6] is the indirect method, which exploits the fact that the TOF summed data are not affected by a timing bias. Recently [7] we introduced a faster direct data driven calibration method for a single ring PET scanner, which does not require a non-TOF reconstruction. The algorithm was derived for a continuous 2D model, assuming as in [1, 2, 3, 4, 5] that the TOF misalignment of each LOR is the difference between the timing offsets of the two crystals in coincidence. The consistency equations for TOF PET [8] lead to a relation between the data and the crystal offsets, which is linear, only involves the two first moments of the TOF data, and is independent of the TOF resolution. Although derived for a continuous model the method was successfully implemented for a simulated single ring scanner with 48 blocks of 13 detectors, separated by a gap of one detector. We will present numerical results from [7], which show that this consistency based calibration, while not matching the accuracy of the indirect method, nevertheless recovers the timing offsets with small errors compared to the TOF resolution and have a limited impact on OSEM reconstructions.The present work generalizes the method to an arbitrary 3D scanner geometry, assuming as in 2D that the detector sampling is sufficiently fine. We prove the following property. Denote the fully corrected data for the LOR linking two detectors located at \( \overrightarrow{a}\in {R}^3 \)and \( \overrightarrow{b}\in {R}^3 \)as$$ m\left(\overrightarrow{a},\overrightarrow{b},t\right)=\int dl\ w\left(t-\eta \left(\overrightarrow{a}\right)+\eta \left(\overrightarrow{b}\right)-l\right)\ f\left(\overrightarrow{a}+\frac{\left(l+L/2\right)\left(\overrightarrow{b}-\overrightarrow{a}\right)}{L}\right) $$ (1) with f the activity, \( L=\left\Vert \overrightarrow{b}-\overrightarrow{a}\right\Vert \) and \( \eta \left(\overrightarrow{a}\right),\eta \left(\overrightarrow{b}\right) \)the offsets of the two detectors, and we assume an even TOF profile w(t) = w(-t). Define$$ {M}_0\left(\overrightarrow{a},\overrightarrow{b}\right)=\int dt\ m\left(\overrightarrow{a},\overrightarrow{b},t\right)\kern0.5em ,\kern0.5em {M}_1\left(\overrightarrow{a},\overrightarrow{b}\right)=\int dt\ t\ m\left(\overrightarrow{a},\overrightarrow{b},t\right) $$the two moments of the data. Then the offsets η must be solution of the following equation (∇ a denotes the gradient with respect to the detector location \( \overrightarrow{a} \)):$$ \frac{2}{L}\left({\nabla}_a+{\nabla}_b\right)\left({M}_1-\left(\eta \left(\overrightarrow{a}\right)-\eta \left(\overrightarrow{b}\right)\right){M}_0\right)+L\left({\nabla}_a-{\nabla}_b\right)\frac{M_0}{L} $$ (2) This generic equation can be specialized for any 3D scanner geometry (the case of a cylindrical scanner will be presented), and discretized as a system of Ndetector linear equations for the Ndetector offsets η. Implementation issues will be discussed, in particular: Integrating (2) over \( \overrightarrow{b} \)at fixed \( \overrightarrow{a} \) to obtain a well conditioned integral equation (as in [7] in 2D), The segmentation of this system of equation for scanners with very large axial FOV. References [1] Perkins A E,Werner M, Kuhn A, Surti S, Muehllehner G and Karp J S. Time-of-flight coincidence timing calibration techniques using radioactive sources. IEEE Nucl. Sci. Symp. Conf. Rec. 2005; 5: 2488-91[2] Thompson C J, Camborde M and Casey M E. A central positron source to perform the timing alignment of detectors in a PET scanner. IEEE Trans. Nucl. Sci. 2005; 52: 1300-4.[3] Li X, Burr K C, Wang G-C, Du H, Gagnon D. Timing Calibration for Time-of-Flight PET UsingPositron-Emitting Isotopes and Annihilation Targets. IEEE Trans. Nucl. Sci. 2016; 63:1351-1358.[4] Yu X, Isobe T, Watanabe M and Liu H. Novel crystal timing calibration method based on total variation. Phys. Med. Biol. 2016; 61: 7833-7847.[5] Werner M E and Karp J S. TOF PET offset calibration from clinical data. Phys. Med. Biol. 2013; 58: 4031-46.[6] Rezaei A, Schramm G and Nuyts J. Data driven time alignment for TOF-PET, Records of the 2017 IEEE Medical Imaging Conference, Atlanta (GA).[7] Defrise M, Rezaei A, Nuyts J. Time-of-flight PET time calibration using data consistency. To appear in Phys Med Biol 2018.[8] Defrise M, Panin V Y and Casey M E. New Consistency Equation for Time-of-Flight PET. IEEE Trans. Nucl. Sc 2013; 60: 124-33.A3 Pilot evaluation of the MINDView brain PET insert, based on monolithic LYSO blocks, in a 3T MRIA. J. Gonzalez1, A. Gonzalez-Montoro1, J. Barbera2, L. Moliner1, L. F. Vidal1, E. J. Pincay1, G. Cañizares1, E. Lamprou1, S. Sanchez1, F. Sanchez1, C. Correcher3, J. V. Catret2, S. Jimenez2, S. Aussenhofer4, J. Cabello5, M. Schwaiger5, A. Iborra6, J. M. Benlloch1 1Instituto de Instrumentación para Imagen Molecular (I3M), CSIC — Universitat Politècnica de València, 46022, Valencia, Spain; 2Oncovision, 46022, Valencia, Spain; 3Bruker Biospin, 46013, Valencia, Spain; 4NORAS MRI Products GmbH, Hochberg, Germany; 5Nuklearmedizin, Klinikum rechts de Isar, Technische Universität München, Munich, Germany; 6Brest INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, Brest, France Background We report in this work the current status performance of the brain PET insert developed under the MINDView project. Final construction has been accomplished. This PET insert uses, to our knowledge, the highest number of monolithic blocks in a scanner, a total of 60, with total LYSO volume of 3,000 cm3 and 1,440 signals. Materials and Methods Pilot performance studies have been carried out at the lab, partially following the NEMA protocol. It has later been installed at the nuclear medicine department in Klinikum rechts der Isar (Munich) and exhaustively tested inside the Siemens mMR, a whole body PET-MR with a 3T main magnetic field. The PET insert FOV is 154 mm axially and 240 mm transaxially, defined by 3 rings of 20 monolithic crystals each (50 x 50 x 20 mm), coupled to custom 12x12 SiPM arrays, and readout through custom electronics providing information of two projections of the scintillation light (X and Y). Results The system sensitivity is above 7% (350-650 keV) with a point-like source at the CFOV, and increases to about 10% for the range of (250-750 keV). Average energy resolution of the entire scintillation volume is about 17%. The spatial resolution, measured with the 0.25 mm NEMA source across the radial direction, showed values below 2 mm.A variety of MR sequences for brain imaging have been run (EPI, ASL, T1w, T2w and UTE), and the PET response measured, without showing any deterioration. Count rates as a function of sequences were studied not also exhibiting a system deterioration. Also the MR performance has been studied, among other tests the uniformity of the B0 and B1 fields, not showing significant changes when the dedicated brain PET is inserted.Regarding performance comparison with a state-of-the-art whole body PET system, it shows an improved performance as observed through the mini-Derenzo or other phantoms. Rods of about 2 mm are clearly distinguished with standard iterative reconstruction methods and voxel/pixel sizes. Conclusions We have designed and implemented a brain PET insert using 60 large monolithic LYSO blocks, defining a large FOV with uniform performance in it, and not showing any deterioration when brain imaging MR sequences are used, including EPI or UTE. Detailed analyses will be presented. Currently, the system is in Klinikum rechts der Isar (Munich) and patients recruitment is undergoing.A4 Full body intelligent scanning preclinical PET: characterization and first animal tests of a small-scale systemP. M. M. Correia1, F. M. Ribeiro1, J. Menoita1, A. L. M. Silva1, N. O. Romanyshyn1, F. Rolo1, I. F. Castro2, P. M. C. C. Encarnação2, F. Rodrigues2, A. C. Santos3, C. Ramos3, F. Caramelo3, N. C. Ferreira3,4, D. A. Sá3,4, N. Matela5, P. Almeida5, P. M. Sá5, J. F. C. A. Veloso1 1i3N – Departamento de Física da Universidade de Aveiro, 3810-193, Aveiro, Portugal; 2RI-TE - Radiation Imaging Technologies, Lda, UA Incubator, PCI – Creative Science Park, 3830-352, Ílhavo, Portugal; 3IBILI/FMUC - Instituto de Biofisica/Biomatemática, Faculdade de Medicina da Univ. de Coimbra 3000-545, Coimbra, Portugal; 4ICNAS, Universidade de Coimbra 3000-545, Coimbra, Portugal; 5Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal Correspondence: J. F. C. A. Veloso (joao.veloso@ua.pt) Background A new concept of preclinical PET scanner using an innovative acquisition method based on two rotation axes for the movement of detector pairs is being developed for whole body small animal imaging purposes. An intelligent scanning can be applied as a function of the imaged subject. This innovative concept allows achieving high and uniform position resolution over the whole field of view (FoV), by eliminating parallax errors due to the depth of interaction, which are typical of ring-based PET systems. The absence of parallax effect in transaxial plane does not impose limitations on the proximity of the detector elements to the FoV favoring the system sensitivity. Full axial imaging is possible using only a small number of detector elements, allowing for an unprecedented performance/cost ratio. A demonstrator prototype was built with 16 + 16 detector cells, based on LYSO scintillators coupled to SiPMs, covering a FoV of 50 mm Ø × 35 mm. Patent: PCT/IB2016/051487 Materials and Methods Detector blocks are made of linear arrays of 16 LYSO with 2×2×30 mm3 coupled to individual SiPMs, with custom-developed readout electronics. Spatial resolution (radial and tangential) was measured according to NEMA NU4-2008 standards, using a 0.25 mm Ø 22Na source embedded in a 1 cm3 PMMA and with a 25 μCi activity. A Micro-PET Image Quality (IQ) phantom with 400 μCi of FDG was imaged following the NEMA NU4-2008. The acquisition was performed covering the entire phantom. Filtered Back Projection (FBP) reconstruction method was used for the spatial resolution and IQ phantom acquisitions. Animal imaging tests were carried out on a 27 g mouse injected with 200 μCi of FDG. Results The spatial resolution obtained in radial and tangential directions was around 1 mm over the entire FoV. This result compares favorably with ring-based micro-PET scanners using 2×2 mm2 scintillators. The IQ phantom reveals details that allow us to identify the 1 mm Ø filled FDG rod, demonstrating the excellent image quality of the system, even considering the small number of detector cells used. Mouse imaging reveals details that show the high image quality and useful information that can be extracted from the system, Fig. 1. Conclusion The results indicate that a spatial resolution below 1 mm can be reached for the entire FoV (using FBP reconstruction) as well as good quality details observed in animal imaging, indicating very promising prospects for the development of a high performance preclinical system for small animal imaging. Keywords PET; SIPM; DOI; easyPET; preclinical Acknowledgements This work was partially supported by projects POCI-01-0145-FEDER-016855 and PTDC/BBB-IMG/4909/2014, CENTRO-01-0247-FEDER-017823, and CENTRO-01-0145-FEDER-000003, through CENTRO2020, COMPETE, FEDER, POCI and FCT (Lisbon) programs. Fig. 1 (abstract A4).Example of a brain image of the 27 g mouseFull size image A5 Time Over Thresholds as a measure of energy loss by incident gamma in the J-PET scannerSushil K. Sharma1, Sz. Niedźwiecki1 1M. Smoluchowski Institute of Physics, Jagiellonian University, Lojasiewicza 11, 30-348 Cracow, Poland Correspondence: Sushil K. Sharma (sushil.sharma@uj.edu.pl) Background The Jagiellonian-Positron Emission Tomograph (J-PET) is the first PET composed of plastic scintillators. J-PET consists of 192 individual modules of dimension 500 X 19 X 7 mm3. The modules are arranged axially in the three layers of diameter 85, 93.5 and 115 cm respectively. The long strips of plastic scintillators used in J-PET provide superior timing properties, small light attenuation, larger axial field-of-view (AFOV) and thus qualify for the use in whole-body PET Imaging [1, 2, 3]. Moreover, using plastics as detecting material allow for constructing a cost-effective whole body scanner. A major advantage of the plastic scintillator is that the signals are very fast [4]. Such signals allow for superior time resolution and decrease the pile-ups with respect to crystal-based detectors. In order to take advantage of excellent timing properties of plastic scintillators, in the frame of J-PET the charge collection is replaced with time over threshold (TOT) measurements, which is a well-established method for the signal processing particularly in multi-channel readout systems [5,6]. Materials and methods The challenge in adopting the TOT technique with plastic scintillators is due to the partial deposition of energy by incident gamma as they interact predominantly via Compton scattering. However, implementing the Multi-Voltage-Threshold (MVT) and probing the signal at four different thresholds help greatly in improving the energy loss resolution [5] of tomograph. In order to determine the relationship between TOT and energy loss, the data was collected using 22Na source placed at the center of tomograph surrounded by porous material. The geometrical acceptance of J-PET offers the possibility to study the scattering of incident gamma. Results The algorithm used for the analysis allows to tag the energy and scattering angle of the incident gamma which in turn gives the estimate about the energy deposition in the scintillator. Thus for each interaction in the scintillator one can obtain one-to-one information of the energy deposition by the gamma photon and corresponding TOT values. Conclusions We have established the relationship between TOT and energy loss by gamma quanta in the J-PET scanner built from the plastic scintillators. The relationship obtained from the analysis of experimental data can be well described by using the function TOT = A + B * ln(Eloss). Acknowledgement The results presented here are on the behalf of J-PET collaboration. We acknowledge the support of the National Science Centre through the grant No. 2016/21/B/ST2/01222, by the National Centre for Research and Development through grants Nos. INNOTECH-K1/IN1/64/159174/NCBR/12, by the Ministry for Science and Higher Education through grants Nos. 6673/IA/SP/2016 - IA/SP/01555/2016, 7150/E-338/SPUB/2017/I and The Foundation for Polish Science (MPD & TEAM). References [1] S. Niedzwiecki et al., Acta Physica Pol B 48, (2017) 1567 - 1576[2] P. Moskal et al., Phys. Med. Biol. 61, (2016) 2025 - 2047[3] P. Moskal et al., Bio-Alg. Med.-Sys. 7, (2011) 73 - 78[4] A. Wieczorek et al., PLOS ONE 12(11): (2017) e0186728[5] M. Palka et al., JINST 12, (2017) P08001[6] G. Korcyl et al., Acta Phys. Polon. B 47, (2016) 491 - 496A6 Image reconstruction of the simulated NEMA IEC phantom in J-PET scanner using multivariate kernel density estimationR. Y. Shopa (Roman.Shopa@ncbj.gov.pl)Świerk Computing Centre, National Centre for Nuclear Research, Otwock-Świerk, PolandJagiellonian PET (J-PET) scanner is a novel PET detector with the large axial field of view, which operating principle is based on the Compton scattering of photons inside plastic scintillator strips [1,2]. The tomograph exhibits excellent time resolution below 100 ps and is expected to provide a superior figure of merit for the total body imaging. Current prototype comprises three cylindrical layers, each composed of 50-cm long scintillator strips. The ongoing work is to add another module with more complex geometry.Software products for image reconstruction, developed mainly for commercial PET scanners, do not support long and continuous scintillator strips in J-PET, so that additional errors would be imposed. Besides, some reconstruction frameworks, such as STIR [3], do not incorporate time of flight (TOF), which is one of the advantages of J-PET. As a reasonable alternative, we utilize a statistical tool – kernel density estimation (KDE), for the points of positron and electron annihilation, estimated from TOF.Figure 1 shows the transverse (XY) and the coronal (XZ) cross-sections of the NEMA IEC phantom [4], simulated inside the ideal 1-layer J-PET scanner in the GATE framework [5] and reconstructed using multivariate KDE from the “ks” package, developed for R software environment [6]. Spatial and temporal components of the data were smeared according to the perspective readout of the silicon photomultiplier matrices (SiPM), combined with wavelength shifters (WLS) [7].Reconstructed 3D images for the IEC phantom, obtained for various readouts, were compared for both KDE and STIR algorithms. The advantages of TOF incorporation are clearly visible, since it produces satisfactory results even without filtering the data – by the exclusion of scattered and accidental events along with weighting added due to attenuation effects. Acknowledgements The results are presented on behalf of J-PET Collaboration [http://koza.if.uj.edu.pl/pet/]. References 1. Moskal P, Zoń N, Bednarski T, Białas P, Czerwińskia 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. Instr. Meth. Phys. Res. A. 2015 Mar 1; 775:54-62.2. Moskal P, Rundel O, Alfs D, Bednarski T, Białas P et al. Time resolution of the plastic scintillator strips with matrix photomultiplier readout for J-PET tomograph. Phys. Med. Biol. 2016 Feb 19; 61(5):2025–2047.3. Thielemans K, Tsoumpas C, Mustafovic S, Beisel T, Aguiar P, Dikaios N et al. STIR: software for tomographic image reconstruction release 2. Phys. Med. Biol. 2012 Feb 21; 57(4):867-883.4. International Standard: Radionuclide imaging devices – Characteristics and test conditions – Part 1: Positron emission tomographs, International Electrotechnical Commission (IEC), 61675-1. Geneva, Switzerland; 1998.5. Jan S, Santin G, Strul D, Staelens S, Assié K, Autret D et al. GATE: a simulation toolkit for PET and SPECT. Phys. Med. Biol. 2004 Oct 7; 49(19):4543-4561.6. Duong T. ks: Kernel Smoothing [Internet]. 2018 Jan 16 [cited 2018 March 26]; Available from: http://cran.r-project.org/web/packages/ks/.7. Smyrski J, Alfs D, Bednarski T, Białas P, Czerwiński E, Dulski K et al. Measurement of gamma quantum interaction point in plastic scintillator with WLS strips, Nucl. Instrum. Methods Phys. Res. A. 2017 Jan 24; 851:39-42. Fig. 1 (abstract A6).Transverse (left) and coronal (right) cross-sections of the reconstructed IEC phantom, simulated for the ideal J-PET scanner with SiPM+WLS readout, using multivariate KDEFull size image A7 Total body dynamic FDG PET imaging of spontaneously hypertensive ratsQiao Huang1, Jie Li1, R. Jack Roy1, Mahendra D. Chordia1, Stan Majewski1, Stuart S. Berr1,2, Katelyenn McCauley1, Kiel Neumann1, Susanna R. Keller3, Bijoy K. Kundu1,2 1Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA 22908, USA; 2Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; 3Department of Medicine-Endocrinology and Metabolism, University of Virginia, Charlottesville, VA 22908, USA Correspondence: Bijoy K. Kundu (bkk5a@virginia.edu)Background: Left ventricular hypertrophy (LVH) due to hypertension (HTN) is a key risk factor for the development of heart failure (HF). HF in turn can lead to decreased perfusion and consequently functional impairments of different tissues. In this study we evaluated myocardial and cerebral glucose metabolism, using dynamic FDG PET imaging, in the spontaneously hypertensive rat (SHR) at ~18 months of age. LV function in SHR exhibits compensation until ~17 months of age and systolic HF at ~20 months of age [1].Materials and Methods: Dynamic FDG PET imaging [2] (~250-300 μCi) of rats at ~18 months of age was performed using the Bruker Albira Si Trimodal imager (150 mm axial FOV) [3]. The 60 minute list-mode data was histogrammed into 23 time bins and reconstructed with attenuation correction using MLEM algorithm. Regions of interest (ROI) were drawn in early and late time frames in the regions corresponding to the inferior vena cava (IVC) (Fig. 1A) and myocardium (Fig. 1B) respectively and time activity curves (TAC) generated. The blood TAC from IVC was first corrected for partial volume averaging by simulating the effect of convoluting a ~3-4 mm object (IVC) with a Gaussian distribution of FWHM spatial resolution of ~1 mm to generate recovery coefficients (RC). The blood TAC boosted by RC was then used in a 3-compartment kinetic model correcting for spill-out from the blood to the myocardium at the early time points and radioactivity recovery for the myocardium to compute rate of myocardial FDG influx, Ki(1/min). ROI was also drawn in the region corresponding to the brain (Fig. 1C) and cerebral FDG uptake rate, Ki(1/min), determined using computed input curve (Fig. 1D). Cardiac MRI was performed using the Bruker Clinscan 7T MR scanner on the same rats to assess LV structure and systolic function.Results: A 1.8-fold increase in myocardial FDG Ki (Fig. 1E) was observed in non-failing SHR hearts (n=2) (Fig. 1F) when compared to control Wistar-Kyoto (WKY) hearts. There was, however, a significant reduction in myocardial FDG Ki (Fig. 1E) in failing SHR hearts (n=3) (Fig. 1F) with a significant increase in cardiac mass (Fig. 1G) when compared to WKY. Cerebral FDG Ki (Fig. 1E) was lower (~3.6-fold) in SHR than in WKY rats.Conclusions: The pressure overload non-failing SHR heart enhances glucose metabolism to maintain cardiac function. The failing SHR heart, however, exhibits reduced glucose metabolism together with impaired function and significant LVH. Decreased heart function could result in decreased cerebral glucose metabolism and cerebral dysfunction. Acknowledgements NIH R01HL123627-03 (to BKK) and 1S10OD021672 (to SSB). References 1. Brooks WW, Shen SS, Conrad CH, Goldstein RH, Bing OH: Transition from compensated hypertrophy to systolic heart failure in the spontaneously hypertensive rat: Structure, function, and transcript analysis. Genomics. 2010;95:84-92.2. Zhong M, Kundu BK: Optimization of a Model Corrected Blood Input Function From Dynamic FDG-PET Images of Small Animal Heart In Vivo. IEEE Trans Nucl Sci. 2013;60:3417-3422.3. González AJ, Aguilar A, Conde P et al: A PET Design Based on SiPM and Monolithic LYSO Crystals: Performance Evaluation. IEEE Trans Nucl Sci. 2016;63:2471-2477. Fig. 1 (abstract A7).Total Body Dynamic FDG PET imaging. Regions of interest (ROI) shown on representative (A) Inferior vena cava (IVC) at early time point (inset: sagittal view), (B) Myocardium and (C) Brain transverse images at late time point from dynamic FDG PET images in vivo, (D) Time activity curves (TAC) shown for the blood (IVC) corrected by recovery coefficient, myocardium and the brain and model fits in a 3-compartment kinetic model (inset: TAC at early time points), (E) Computed rates of myocardial and cerebral FDG uptake, (F) Ejection fraction and (G) Heart weight to Body weight ratios measured using MRI in vivo. Student t-tests were performed only between WKY (n=4) and SHR (n=3)Full size image A8 TOFPET 2 based whole body PETRicardo Bugalho1, Luís Ferramacho1, Carlos Leong1, Tahereh Nikjnejad2, José C. Silva1,2, Rui Silva1,2, Miguel Silveira1, Stefaan Tavernier2,3, João Varela1,2 1PETsys Electronics, Oeiras, Portugal; 2LIP, Lisbon, Portugal; 3Vrije Universiteit Brussel, Brussels, Belgium Correspondence: Ricardo BugalhoSmall crystals with 1:1 coupling to SiPM yield the best time resolution for Time of Flight PET. However, applied to a whole body PET this results in a large number of electronics channels. A PET with 80 cm diameter and 2 meter axial length built on crystal and SiPM with a 3.2 mm pitch requires ≈512’000 electronics channels.A system based on PETsys Electronics’ readout system is proposed. The elementary detector module consists an array of 16x16 LYSO crystals with a pitch of 3.2 mm, individually coupled to SiPM pixels and read by 4 64-channel TOFPET 2 ASICs. The system consists of 40 rings of 48 modules per ring, for a total of 1920 detector modules and a detector modu