BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:New technologies for Total Body PET imaging DTSTART;VALUE=DATE-TIME:20211011T070000Z DTEND;VALUE=DATE-TIME:20211011T072000Z DTSTAMP;VALUE=DATE-TIME:20260904T174621Z UID:indico-contribution-20-306@indico.koza.if.uj.edu.pl DESCRIPTION:Speakers: Stefaan Vandenberghe ()\nThe lecture will concern ne w technologies for Total Body PET imaging.\n\nhttps://indico.koza.if.uj.ed u.pl/event/4/contributions/306/ LOCATION:Theranostics Center / on-line URL:https://indico.koza.if.uj.edu.pl/event/4/contributions/306/ END:VEVENT BEGIN:VEVENT SUMMARY:Theranostic and Monte Carlo simulation DTSTART;VALUE=DATE-TIME:20211011T082000Z DTEND;VALUE=DATE-TIME:20211011T084000Z DTSTAMP;VALUE=DATE-TIME:20260904T174621Z UID:indico-contribution-20-288@indico.koza.if.uj.edu.pl DESCRIPTION:Speakers: David Sarrut (CNRS)\nWe will describe our experience s regarding dosimetry in radionuclide therapy (Lu177 and SIRT) performed a t our institution. Dose estimation from post and per-treatment SPECT image s is performed via dose-rate computation with Monte-Carlo simulation (Gate /Geant4). We will also describe current investigations of deep learning to speed up Monte Carlo simulations.\n\nhttps://indico.koza.if.uj.edu.pl/eve nt/4/contributions/288/ LOCATION:Theranostics Center / on-line URL:https://indico.koza.if.uj.edu.pl/event/4/contributions/288/ END:VEVENT BEGIN:VEVENT SUMMARY:Micro-CT journey - from bones to personalized medicine DTSTART;VALUE=DATE-TIME:20211011T074000Z DTEND;VALUE=DATE-TIME:20211011T080000Z DTSTAMP;VALUE=DATE-TIME:20260904T174621Z UID:indico-contribution-20-264@indico.koza.if.uj.edu.pl DESCRIPTION:Speakers: Bartosz Leszczyński (M. Smoluchowski Institute of P hysics\, Jagiellonian University)\nX-ray microtomography (micro-CT) is a w ell establised nondestructive 3D method for small sample internal structur e imaging. For over 20 years\, micro-CT is known as a golden standard in b one microarchitecture analysis\, as an alternative to histological section ing method for preclinical research [1\, 2]. Micro-CT surpasses histologi cal analysis because it provides 3D information with several micron sampli ng.\n\nIn recent years\, micro-CT has been succesfully used in micro-angio graphy research. For this purpose it needs addition of contrast agents eit her by staining the sample for ex-vivo scanning or using perfusion in smal l animal in-vivo micro-CT [3\, 4]. Staining methods enhance imaging contra st globally by diffusion process in examined tissue\, particullary in area s with high affinity to a specific contrasting solutions. Recent research proofs the potential of this metod in imaging of 3D cell cultures called s pheroids [5]. The injected contrast agent works more locally. It can enhan ce image contrast of blood vessels\, heart\, kidneys and urinary bladder.\ n\nFrom the other hand micro-CT is an indispensable tool in material scien ce including drug design for a personalized medicine. This work shows how micro-CT can help in design and quality control of individualy 3D printed tablets [6\, 7]. \n\n\nReferences\n\n[1] Leszczyński\, et al. (2014). Thr ee dimensional visualisation and morphometry of bone samples studied in mi crocomputed tomography (micro-CT). Folia morphologica\, 73(4)\, 422–428. https://doi.org/10.5603/FM.2014.0064\n[2]Pilutin\, A.\, et al. (2021). Mo rphology and serum and bone tissue calcium and magnesium concentrations in the bones of male rats chronically treated with letrozole\, a nonsteroida l cytochrome P450 aromatase inhibitor. Connective tissue research\, 62(4)\ , 454–463. https://doi.org/10.1080/03008207.2020.1771329\n[3]Leszczyńsk i\, et al.(2018). Visualization and Quantitative 3D Analysis of Intraocula r Melanoma and Its Vascularization in a Hamster Eye. International journal of molecular sciences\, 19(2)\, 332. https://doi.org/10.3390/ijms19020332 \n[4] Tielemans\, B.\, et al. (2020). From Mouse to Man and Back: Closing the Correlation Gap between Imaging and Histopathology for Lung Diseases. Diagnostics\, 10(9)\, 636. https://doi.org/10.3390/diagnostics10090636\n[5 ] Karimi\, H.\, et al.(2020). X-ray microtomography as a new approach for imaging and analysis of tumor spheroids. Micron\, 137\, 102917. https://do i.org/10.1016/j.micron.2020.102917\n[6] Jamróz\, W.\, et al. (2020). Spee d it up\, slow it down…An issue of bicalutamide release from 3D printed tablets. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences\, 143\, 105169. https ://doi.org/10.1016/j.ejps.2019.105169\n[7] Jamróz W.\, et al.(2020). Mult ivariate Design of 3D Printed Immediate-Release Tablets with Liquid Crysta l-Forming Drug-Itraconazole. Materials\, 13(21)\, 4961. https://doi.org/10 .3390/ma13214961\n\nhttps://indico.koza.if.uj.edu.pl/event/4/contributions /264/ LOCATION:Theranostics Center / on-line URL:https://indico.koza.if.uj.edu.pl/event/4/contributions/264/ END:VEVENT BEGIN:VEVENT SUMMARY:Introduction of non-image PET data transformation to image-form fo r classification using Convolutional Neural Networks DTSTART;VALUE=DATE-TIME:20211011T080000Z DTEND;VALUE=DATE-TIME:20211011T082000Z DTSTAMP;VALUE=DATE-TIME:20260904T174621Z UID:indico-contribution-20-249@indico.koza.if.uj.edu.pl DESCRIPTION:Speakers: Lech Raczyński (Department of Complex Systems\, Nat ional Centre for Nuclear Research)\nRecently\, Convolutional Neural Net works (CNNs) [1] have achieved state-of-the-art performance in many a reas including medical sciences\, and are the method of choice commonly us ed for data recognition or classification. CNNs have proven to work most efficiently on 2-dimensional data that are in form of images. \n\nIn case of Positron Emission Tomography (PET) [2\,3] studies\, CNN may be applied directly to the reconstructed distribution of radioactive tracer in jected to the patient's body\, as for example a pattern recognitio n tool. Nonetheless\, much PET data still exists in non-image format and t herefore \nopens challenging research questions on whether they can be eff ectively trained using CNN. Examples of such tasks are estimation of time- of-flight from signals registered in scintillators [4] or classification of coincidence events acquired by PET scanner [5]. \n\nThe goal of this p resentation is the introduction of scheme of non-image data transformation into 2-dimensional matrices\, as a preparation stage for classification b ased on CNNs. The first work to apply CNN on different kinds of non-image datasets\, e.g.\, gene expression or text information\, was \nproposed in [6]. Here\, we will focus mainly on the problem of processing of vectors with small number of features in comparison to the numb er of pixels in the output images. As an example\, a discussion of application of the proposed methodology to classification of PET coinciden ce events will provided [7]. \n\n**References** \n \n[1] Lecun Y\, Bengio Y and Hinton G. Deep learning\, *Nature* vol. 521\, pp. 436\, 2015. \n\n [2] Humm J L\, Rosenfeld A\, Del Guerra A. From PET detectors to PET scan ners\, \n*European J. of Nucl. Med. & Mol. Imag.* vol. 30\, pp. 1574\, 200 3. \n \n[3] Bailey D L. *Positron Emission Tomography: Basic Sciences*\, Springer-Verlag\, New York\, 2005. \n\n[4] Berg E and Cherry S R. Using convolutional neural networks to estimate time-of-flight from PET detect or waveforms\, *Phys. Med. Biol.*\, vol. 63\, pp. 02LT01\, 2018. \n\n[5] Bielecki J. Application of the machine learning methods to the multi-phot on event classification in the J-PET scanner\, *Msc thesis*\, *Warsaw Uni versity of Technology*\, 2019. \n\n[6] Sharma A\, Vans E\, Shigemizu D\ , Boroevich K A and Tsunoda T. Deepinsight: A methodology transform a non -image data to an image for convolution neural network architecture. *Scie ntific Report*s vol. 9\, pp. 11399\, 2019. \n \n[7] Konieczka P. Convolu tional Neural Networks in classification of multi-photon coincidences in J -PET scanner\, *1st Symposium on Theranostics*\, 9-11 October 2021.\n\nhtt ps://indico.koza.if.uj.edu.pl/event/4/contributions/249/ LOCATION:Theranostics Center / on-line URL:https://indico.koza.if.uj.edu.pl/event/4/contributions/249/ END:VEVENT BEGIN:VEVENT SUMMARY:List-mode TOF MLEM reconstruction for the total-body J-PET with a realistic system response matrix DTSTART;VALUE=DATE-TIME:20211011T072000Z DTEND;VALUE=DATE-TIME:20211011T074000Z DTSTAMP;VALUE=DATE-TIME:20260904T174621Z UID:indico-contribution-20-241@indico.koza.if.uj.edu.pl DESCRIPTION:Speakers: Roman Shopa (National Centre for Nuclear Research\, Poland)\nWe modify the time-of-flight maximum likelihood expectation maxim isation (TOF MLEM) image reconstruction algorithm by an updated model for the system response matrix (SRM) of the total-body Jagiellonian PET (J-PET ) scanners\, which modular multi-layer geometry complicates SRM estimation and requires more computational power to calculate correction factors [1] .\nThe elongated plastic scintillators of the J-PET\, which use Compton sc attering for the detection of positron-electron annihilation photons\, imp ly the smooth dependence of SRM on the obliqueness angle $\\theta$. We thu s represent it as a set of functions unique for each bin and acquired by a log-polynomial fit of the Monte Carlo simulated emissions of $\\gamma$-ph otons on 2D transverse planes with different $\\theta$.\nBy utilising the GATE software [2]\, a NEMA IEC phantom [3] was simulated in a 140-cm long 24-module J-PET\, comprised of 2 detector layers (inner radius 393 mm) and a layer of wavelength shifters [4]. The data collected from a 500-s long scan was post-smeared according to the assessed temporal (191 ps) and axia l (5 mm) resolution. Only true coincidences were considered. \nThe updated SRM was employed for the list-mode TOF MLEM reconstruction. For the prede fined NEMA IEC attenuation map\, two versions of attenuation correction we re applied: a conventional (integration over bins) and a simplified on-the -fly recalculation for each measurement\, which improves performance and i s less sensitive to boundary effects. \nCompared to the reference list-mod e TOF MLEM from the CASToR framework [5]\, a substantial improvement in th e image quality and mean squared error with respect to ground truth were o bserved. The simplified attenuation correction proved to be a reliable alt ernative\, producing outcomes similar or better than the conventional appr oach.\nTo summarise\, the proposed analytical SRM model for the total-body J-PET proved to be superior to the reference method employed for crystal- based scanners. The modified TOF MLEM and attenuation correction do not re quire high computational power and can be extended to account for the non- collinearity\, positron range and other factors.\n\n[1] P. Moskal et al.\, “Simulating NEMA characteristics of the modular total-body J-PET scanne r—an economic total-body PET from plastic scintillators\,” Phys. Med. Biol.\, vol. 66\, no. 17\, pp. 175015\, Sep. 2021.\n[2] S. Jan et al.\, “GATE: a simulation toolkit for PET and SPECT\,” Phys. Med. Biol.\, vo l. 49\, no. 19\, pp. 4543-4561\, Oct. 2004. \n[3] Performance Measurements of Positron Emission Tomographs\, NEMA NU 2-2012\, 2013.\n[4] J. Smyrski et al.\, "Measurement of gamma quantum interaction point in plastic scinti llator with WLS strips\," Nucl. Instrum. Methods Phys. Res. A\, vol. 851\, pp. 39-42\, Apr. 2017.\n[5] T. Merlin et al.\, "CASToR: a generic data or ganization and processing code framework for multi-modal and multi-dimensi onal tomographic reconstruction\," Phys. Med. Biol.\, vol. 63\, no. 18\, p p. 185005\, Sep. 2018.\n\nhttps://indico.koza.if.uj.edu.pl/event/4/contrib utions/241/ LOCATION:Theranostics Center / on-line URL:https://indico.koza.if.uj.edu.pl/event/4/contributions/241/ END:VEVENT END:VCALENDAR