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Alyssa C Pollard^1,2, Jorge de la Cerda¹, F William Schuler¹, Tyler R Pollard¹, Aikaterini Kotrotsou¹, Loreno Palagi³, Chetan Dhakan¹, Federica Pisaneschi¹, and Mark D Pagel^4
1Department of Cancer Systems Imaging, MD Anderson Cancer Center, Houston, TX, United States, ²Department of Chemistry, Rice University, Houston, TX, United States, ³Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy, ⁴Department of Cancer Systems Imaging, UT MD Anderson Cancer Center, Houston, TX, United States

Keywords: PET/MR, Contrast Agent, pH imaging

Acidosis is a useful biomarker for tumor diagnoses and for evaluating early response to anti-cancer treatments. T1-based MRI contrast agents have been developed with a r1 relaxivity that is responsive to extracellular pH (pHe) in the tumor microenvironment.  However, the concentration of these agents in the tumor must also be known to determine r1 relaxivity.  We have developed PET/MRI co-agents that can be evaluated with simultaneous PET/MRI during pre-clinical studies with mouse models of human cancers.  The PET agent reports on the concentration of the agents, which can be used to determine r1 relaxivity and estimate pHe in tumors.

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Renesmee Kuo¹, Chinmoy Saayujya², Kim Hwang Yeo², Irati Rodrigo², Prashant Chandrasekharan², Quincy Huynh², Benjamin Fellows², John Pauly¹, and Steven Conolly^2
1Stanford University, Stanford, CA, United States, ²University of California, Berkeley, Berkeley, CA, United States

Keywords: Molecular Imaging, Cell Tracking & Reporter Genes

Molecular imaging tools can noninvasively track cells in vivo. However, no techniques today can rapidly monitor cell therapies to allow for nimble treatment optimization for each patient, the epitome of Personalized Medicine. Magnetic Particle Imaging (MPI) is a new tracer imaging technology that could soon provide MDs unequivocal therapy treatment feedback in just three days. MPI with Brownian SPIOs shows promise towards noninvasive sensing of cell viability via viscosity changes in apoptotic cells. This unique ability could greatly improve the efficacy of cell therapies by enabling rapid personalization of the treatment.

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Tianyun Zhao¹, Thomas Hagan¹, Christine DeLorenzo^1,2, and Chuan Huang^1,3
1Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States, ³Radiology, Stony Brook Medicine, Stony Brook, NY, United States

Keywords: PET/MR, PET/MR

The advent of simultaneous PET/MRI enables the possibility of using MRI to guide PET image reconstruction/recovery. Deep-learning approaches have been explored in low-count PET recovery, with current approaches focus on supervised learning, which requires a large amount of training data. A recently proposed unsupervised learning image-recovery approach does not require this but relies on the optimal stopping criterion. In this work, we developed an unsupervised learning-based PET image recovery approach using anatomical MRI as input and a novel stopping criterion. Our method achieved better image recovery in both global image similarity metrics and regional standard uptake value (SUV) accuracy.

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Chunwei Ying¹, Yasheng Chen², Matthew R. Brier², Shaney Flores¹, Richard Laforest¹, Tammie L. S. Benzinger^1,2,3, and Hongyu An^1,2
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States, ²Department of Neurology, Washington University School of Medicine, St Louis, MO, United States, ³Department of Neurosurgery, Washington University School of Medicine, St Louis, MO, United States

Keywords: PET/MR, Brain, attenuation correction

We evaluated the accuracy of a deep learning-based PET/MR attenuation correction (AC) method with vendor-provided high-resolution Dixon in- and opp-phase images as inputs (DL-HiRes). We found that the DL-HiRes AC method significantly outperformed the vendor-provided skull model AC method for both 16-channel head-neck coil and 32-channel head coil (p<0.001). Moreover, the DL-HiRes method had similar AC accuracy using different head coils.

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Koji Sagiyama¹, Osamu Togao², Takeshi Kamitani¹, Koji Yamashita³, Yuzo Yamasaki¹, Kazufumi Kikuchi¹, Takuya Hino¹, and Kousei Ishigami^1
1Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ²Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan, ³Department of Radiology Informatics and Network, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

Keywords: Tumors, PET/MR, Amide Proton Imaging, Apparent Diffusion Coefficient, ADC

Amide proton transfer (APT) imaging is useful for grading brain tumors; however, its diagnostic accuracy is compromised by the heterogeneity of the tumor's structure. In this study, we investigated using a PET-magnetic resonance (PET/MR) system whether dividing the APT by the apparent diffusion coefficient (ADC) improves the diagnostic ability of high-grade and low-grade brain tumors by correcting for the effects of tissue structure. The corrected APT (APT/ADC) showed the best diagnostic performance in grading brain tumors compared to APT, ADC, and standardized uptake value (SUV) alone.

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Michael J Dubec^1,2, John Gaffney³, David L Buckley^2,4, Michael Berks¹, Anubhav Datta^1,5, Damien J McHugh^1,2, Ross A Little¹, Susan Cheung¹, Christina Hague³, Julian C Matthews⁶, Ananya Choudhury^1,3, Marcel van Herk¹, Andrew McPartlin⁷, Geoff JM Parker^8,9, and James PB O'Connor^1,5,10
1Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom, ²Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, United Kingdom, ³Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom, ⁴Biomedical Imaging, University of Leeds, Leeds, United Kingdom, ⁵Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom, ⁶Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom, ⁷Radiation Medicine, Princess Margaret Cancer Centre, Toronto, ON, Canada, ⁸Bioxydyn Ltd, Manchester, United Kingdom, ⁹Centre for Medical Image Computing, University College London, London, United Kingdom, ¹⁰Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom

Keywords: Cancer, Head & Neck/ENT, Tumour Hypoxia, Radiotherapy, OE-MRI, MR Linac

Hypoxia is common to most solid tumours and mediates treatment resistance. Oxygen-enhanced (OE)-MRI can identify, map and quantify hypoxia in multiple tumour types including H&N cancer. We build upon previous work to show that OE-MRI biomarkers: (i) can be deployed on multiple MRI systems; (ii) are repeatable; (iii) can detect cohort level response to radiotherapy; (iv) can identify which individual lesions have significant changes in their oxygenation, hypoxic volume and hypoxic fraction.

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Ramanpreet K. Sembhi¹, Matthew S. Fox^1,2, Heeseung Lim^2,3, Justin W. Hicks^2,4, Shawn N. Whitehead⁵, Jonathan D. Thiessen^2,4, Grace Parraga^4,6, and Alexei V. Ouriadov^1,2,7
1Department of Physics and Astronomy, The University of Western Ontario, London, ON, Canada, ²Lawson Health Research Institute, London, ON, Canada, ³Siemens Healthcare Limited, London, ON, Canada, ⁴Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada, ⁵Department of Anatomy and Cell Biology, The University of Western Ontario, London, ON, Canada, ⁶Robarts Research Institute, London, ON, Canada, ⁷School of Biomedical Engineering, The University of Western Ontario, London, ON, Canada

Keywords: PET/MR, Hyperpolarized MR (Gas)

In this proof-of-concept study, simultaneous ¹²⁹Xe-based MRI and [¹⁵O]-water PET images were collected and compared from rat brain.  For initial validation phase in phantoms, we have dissolved ¹²⁹Xe in [¹⁵O]-water to simultaneously use both imaging modalities and confirm that PET/MRI images reflect the true density of the ¹⁵O/¹²⁹Xe.  The comparison results show similarity between both imaging modalities and tracers, moving towards the next step in validating the Xenon imaging technique as a potential for brain perfusion measurement.  The acquisitions were carried out using a 3T PET/MRI (Siemens Biograph mMR).

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Jose Santiago Enriquez^1,2, Prasanta Dutta¹, Ryan C Armijo¹, Muxin Wang¹, Jenny Jie Han¹, Peter Shepherd³, Daniel Frigo^1,2, Mark Titus³, Federica Pisaneschi¹, and Pratip Bhattacharya^1,2
1Cancer System Imaging, UT MD Anderson Cancer Center, Houston, TX, United States, ²UT MD Anderson Cancer Center UT Health Science Center Houston Graduate School of Biomedical Sciences, Houston, TX, United States, ³Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, United States

Keywords: PET/MR, Cancer, Prostate Cancer, Metabolic Imaging, Hyperpolarized MR

Many advanced prostate cancer patients receiving anti-androgens (Enzalutamide) as the first line of treatment, develop resistance which relapses into metastatic castrate-resistant prostate cancer (mCRPC). Treatment options for mCRPC patients are limited and continue to pose a significant oncological challenge with an alarming low survival rate of only 28%.  The overarching goal of this research is thus to develop personalized metabolic imaging modality to target treatment strategies of different metabolic sub-types of prostate cancer by targeting pyruvate-to-lactate metabolism by hyperpolarized [1-¹³C]-pyruvate MR and fatty acid oxidation by ¹⁸F-FPIA PET.

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Anum Masood^1,2, Sølvi Knapstad², Håkon Johansen³, Trine Husby³, Live Eikenes ¹, Pål Erik Goa^2,3, and Mattijs Elschot ^1,3
1Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway, ²Department of Physics, NTNU, Trondheim, Norway, ³Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Keywords: Segmentation, Data Processing, Automated Segmentation, Deep Learning, nnUnet

Widespread of lymphoma cancer makes manual segmentation of metastatic lymph nodes a tedious task. Lymphoma cancer is assigned an anatomic stage using the Ann Arbor system which relies on the segmentation and localization of affected lymph nodes with respect to anatomical stations. We present a framework for multi-organ segmentation for multiparametric MRI images. Our modified nnUnet using a transfer learning approach achieved 0.8313 mean DSC and 0.659 IoU in lymphoma cancer dataset. 

xiang liu¹ and xiaoying wang^1
1peking university first hospital, Beijing, China

Keywords: Cancer, Prostate, metastases

This retrospective study aims to develop and evaluate a deep learning-based algorithm for semiautomated treatment response assessment of pelvic lymph nodes. A total of 162 patients who had undergone at least two scans for follow-up assessment after advanced prostate cancer metastasis treatment were enrolled. A previously reported deep learning model was used to perform automated segmentation of pelvic lymph nodes. Our results showed that the accuracies of automated segmentation-based response assessment were high for all the target lesions, nontarget lesions and nonpathological lesions according to MET-RADS-P criteria and achieved good consistency with the attending radiologist and fellow radiologist.