Ina Nora Kompan¹, Ben Dickie², Steven Sourbron³, Petra van Houdt⁴, Laura Bell⁵, Rianne van der Heijden⁶, Andrey Fedorov⁷, Jonathan Arvidsson⁸, Charlotte Debus⁹, David Clunie¹⁰, Ingomar Gutmann¹¹, Chad Quarles⁵, Zaki Ahmed¹², Ralf Floca¹, and David Buckley^13
1Medical Image Computing, German Cancer Research Center, Heidelberg, Germany, ²Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom, ³Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, ⁴Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands, ⁵Barrow Neurological Institute, Phoenix, AZ, United States, ⁶Department of Radiology, Erasmus MC, Rotterdam, Netherlands, ⁷Brigham and Women’s Hospital, Boston, MA, United States, ⁸Department of Radiation Physics, University of Gothenburg, Gothenburg, Sweden, ⁹Steinbuch Centre for Computing, Karlsruhe Institute for Technology, Karlsruhe, Germany, ¹⁰PixelMed Publishing, LLC, Bangor, PA, United States, ¹¹University of Vienna, Vienna, Austria, ¹²Mayo Clinic, Rochester, MN, United States, ¹³University of Leeds, Leeds, United Kingdom

As part of the Open Source Initiative for Perfusion Imaging (OSIPI), the aim of this work is to develop guidelines for reporting of contrast-based perfusion analysis in order to improve reproducibility, reusability and interoperability of perfusion analysis. For this, a perfusion analysis lexicon is developed, which provides standardized nomenclature for perfusion parameters and analysis processes, as well as a framework for reporting of analysis pipelines. The lexicon is intended to be a dynamically growing inventory updated by the perfusion community. For that, a variety of public feedback and review cycles are planned during the development process.   

Anahita Fathi Kazerooni¹, Laura C. Bell², Floris Van den Abeele³, Ruben Verhack³, Xinze Zhou⁴, Salman Rezaei⁵, Zaki Ahmed⁶, Rianne Van Der Heijden⁷, Seyed Ali Nabavizadeh⁴, Leland S. Hu⁸, Hamidreza Saligheh Rad⁵, and Steven Sourbron^9
1Department of Radiology, UPenn, Philadelphia, PA, United States, ²Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, PA, United States, ³Hyperfusion.ai, Gent, Belgium, ⁴University of Pennsylvania, Philadelphia, PA, United States, ⁵Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ⁶Mayo Clinic, Rochester, MN, United States, ⁷Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands, ⁸Neuroradiology Division, Department of Radiology, Mayo Clinic, Phoenix, AZ, United States, ⁹University of Sheffield, Sheffield, United Kingdom

Dynamic contrast enhanced (DCE-) MRI is widely acquired as a part of neuroimaging protocol for evaluation of glioblastoma tumors before the start of therapy or monitoring and assessment of treatment response in longitudinal scans. Nonetheless, lack of a standardized quantification method has limited its application in clinical settings, multi-institutional studies and clinical trials. These challenges have motivated efforts to validate DCE-MRI using benchmark biomedical image analysis methods. The Open-Source Initiative for Perfusion Imaging (OSIPI) has designed the OSIPI-DCE challenge to evaluate and compare DCE tools in terms of accuracy, repeatability, and reproducibility of K^trans estimation in the brain.

Idan Bressler^1,2, Dafna Ben Bashat^1,3,4, Orna Aizenstein^3,5, Dror Limon^3,6, Felix Bokestein^3,7, Deborah T. Blumenthal^3,7, Uri Nevo^2,4, and Moran Artzi^1,3,4
1Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ²The Iby and Aladar Fleischman Faculty of Engineering Tel Aviv University, Tel Aviv, Israel, ³Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel, ⁴Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, ⁵Division of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ⁶Division of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, ⁷Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

A DCE based method for differentiation between active tumor tissue and radiation necrosis in patients with Glioblastoma is proposed. The study included 31 MRI scans of patients with Glioblastoma (6 patients with 4 longitudinal scans and 7 scans with biopsy results). The system is comprised of automatic, feature based discrimination of DCE dynamic data based on population analysis. Differentiation results show correlation to theoretical DCE models, and agreement with biopsy results in in the majority of cases. RANO based assessment of the differentiated results demonstrate potential for early detection of tumor progression.

Dinil Sasi S¹, Rakesh K Gupta², Rana Patir², Suneeta Ahlawat², and Anup Singh^1,3
1Indian Institute of Technology Delhi, New Delhi, India, ²Fortis Memorial Research Institute, Gurugram, India, ³All India Institute of Medical Science, New Delhi, India

Arterial-Input-Function(AIF) is a pre-requisite in fitting generalized-tracer-kinetic-model(GTKM) to dynamic-contrast-enhanced(DCE) MRI data for computing tracer-kinetic-parameters(TKP). TKP are highly sensitive to peak and shape of AIF, and this results in variation in computed parameter values across studies. These variations can reduce accuracy of TKP in glioma grading. We hypothesize that propagation of these AIF related errors to TKP can be mitigated using normalization w.r.t. corresponding average TKP values of healthy tissue and normalized TKP might improve glioma grading. The proposed normalization w.r.t. healthy gray-matter tissue has significantly reduced variations of TKP and improved accuracy of glioma grading particularly using Ktrans.

Jin Zhang¹, Ayesha Bharadwaj Das¹, James Tranos², Karl Kiser¹, Youssef Zaim Wadghiri², and Gene Kim^1,2
1Weill Cornell Medicine, New York, NY, United States, ²NYU Langone Health, New York, NY, United States

The repeatability of dynamic contrast enhanced (DCE)-MRI has not been fully studied, particularly with contrast kinetic parameters including intracellular water lifetime (τ_i). The purposes of this study were: (1) to investigate the repeatability of DCE-MRI with the newly proposed technique, 3D-UTE-GRASP (Golden angle Radial Sparse Parallel), which can provide isotropic high-resolution images for quantitative pharmacokinetic model analysis; and (2) to investigate the repeatability of intracellular water lifetime estimation using the two-flip angle DCE-MRI approach.

Wan Dong¹, Ailian Liu¹, Anliang Chen¹, Yuhui Liu¹, and Lizhi Xie^2
1Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China, ²GE Healthcare, MR Research, Beijing, China

Vascular tumor thrombus has been proved to be closely related to the poor prognosis of rectal cance, of which the current gold standard for the diagnosis of is postoperative pathology. In this work, we explored the feasibility of spectral CT and DCE-MRI in quantitatively predicting vascular invasion of rectal cancer. Results showed that the arterial NIC combined with Ktrans can differentiate the vascular invasion from normal status more accurately. Therefore, spectral CT combined with DCE-MRI may serve as a feasible and non-invasive way in predicting vascular invasion of rectal cancer preoperatively, that is of great significance for clinical diagnosis.

Karl Kiser¹, Jin Zhang¹, and S. Gene Kim^1,2
1Radiology, Weil Cornell Medical College, New York, NY, United States, ²Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States

Intracellular water residence time (τ_i) is an important property of solid tumors, with implications in cellular energy turnover. Measurement of τ_i using the active contrast encoding MRI method offers insight into tumor microenvironment heterogeneity and potentially metabolic activity. Our study compares τ_i, measured using ACE-MRI, in mouse gliomas with the standardized uptake value (SUV) from 18F-FDG PET in order to investigate the feasibility of using τ_i as an imaging marker for cellular metabolic activity.

Kazuyuki Makihara¹, Kazuya Sakaguchi¹, Masayuki Yamaguchi², Ken Ito³, Yusaku Hori³, Taro Semba³, Yasuhiro Funabashi³, Hirofumi Fujii², and Yasuhiko Terada^1
1Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan, ²National Cancer Center, EPOC, Division of Functional Imaging, Kashiwa, Chiba, Japan, ³Eisai Co.,Ltd. Oncology Business Group, Bunkyo City, Tokyo, Japan

Dynamic contrast-enhanced (DCE) -MRI has been widely used to assess tumor responses to anticancer drugs. K^trans, one of the pharmacokinetic parameters of DCE-MRI, reflects blood perfusion and permeability in tumor tissue and is a good indicator of perfusion assessment. However, conventional analyses of DCE-MRI do not reflect drug response well, since tumor is extremely heterogeneous. In this study, we have developed a new method for quantitatively assessing the heterogeneous response of tumors during anticancer treatment, and applied it to examine the drug response of E7130, a novel anti-cancer agent with spatiotemporal variation in human breast cancer xenograft model.

Qihao Zhang¹, Jin Zhang², Gene Kim², John Morgan², Thanh Nguyen², Pascal Spincemaille², and Yi Wang^1
1Cornell University, New York, NY, United States, ²Weill Cornell Medical College, New York, NY, United States

In this study, we introduce compartment modeling into quantitative transport mapping (QTM), with the goal of mapping both the blood flow and the tracer exchange between vascular space and extravascular space. The nonlinear optimization problem is solved using the Levenberg–Marquardt method. We validate two compartment QTM (2c-QTM) in a porous media simulation and apply this method to dynamic PET data.

Lixian Zou^1,2, Changjun Tie¹, Jian Xu³, Hairong Zheng¹, Xin Liu¹, and Yuan Zheng^3
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China, ²Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China, ³UIH America Inc., Houston, TX, United States

Auto-calibrated multiband CAIPIRINHA with through-time encoding (tMB) has been proposed to acquire multiple slices simultaneously without extra reference scans. Reference images are estimated from the consecutive cardiac phases at a lower temporal resolution for subsequent slice separation. We implemented the tMB method in a myocardial perfusion sequence and demonstrated the feasibility of whole heart perfusion imaging with tMB and iterative reconstruction using in a clinical setting.

Junyu Wang¹, Daniel Weller², Patricia Rodriguez Lozano³, and Michael Salerno^1,3,4
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, ²Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, ³Medicine, University of Virginia, Charlottesville, VA, United States, ⁴Radiology, University of Virginia, Charlottesville, VA, United States

First-pass contrast-enhanced myocardial perfusion imaging is valuable for evaluating coronary artery disease (CAD). Spiral perfusion imaging techniques, using a motion-compensated L1-SPIRiT based reconstruction, are capable of whole-heart high-resolution perfusion imaging. However, this reconstruction is performed off-line and takes ~1 hour per slice. To address this limitation, we developed a DEep learning-based Spiral Image REconstruction technique (DESIRE) for spiral first-pass myocardial perfusion imaging, for both single-slice (SS) and simultaneous multi-slice (SMS) MB=2 acquisitions, to provide fast and high-quality image reconstruction and make rapid online reconstruction feasible. High image quality was demonstrated using the proposed technique for healthy volunteers and patients.

Xenios Milidonis¹, Richard Crawley¹, and Amedeo Chiribiri^1
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom

Myocardial perfusion quantification by cardiovascular MR imaging has shown great promise in the detection of coronary artery disease. However, the lack of true standardization of methods across centers hinders the effective comparison and pooling of measurements. We sought to develop a novel cardiac phantom mimicking dynamic contrast exchange between two myocardial compartments and use it for the calibration of perfusion measured using three common quantification methods. Without calibration, perfusion measurements differed significantly between methods. Calibration led to accurate and non-significantly different measurements, suggesting that it could be an effective and reliable approach for the universal standardization of quantification methods.

Tzu-Ming Hung¹, Yu-Ting Jiang¹, Cheng-Tao Ho¹, Po-Hung Hsu², Hao-Li Liu³, Chih-Kuang Yeh¹, and Hsu-Hsia Peng^1
1Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ²Center for Advanced Molecular Imaging and Translation, Chang Gung Memorial Hospital, Taoyuan, Taiwan, ³Department of Electrical Engineering, Chang-gung University, Taoyuan, Taiwan

Blood brain barrier (BBB) can be opened transiently by the cavitation effect with the assist of focused ultrasound (FUS) and microbubble (MB). A previous study proved that FUS-induced MR signal changes can monitor cavitation effect in in vitro phantom experiments. However, whether MR signal changes can reflect BBB opening levels in vivo remains uncertain. In this study, a cavitation index (CI) map was employed to evaluate MR signal changes during FUS sonication in in vivo experiments. We aim to examine the relationship between MR signal change and BBB opening levels by associating CI map with Gd-enhancement and K^trans maps.

Qihao Zhang¹, Xianfu Luo², Thanh Nguyen², Pascal Spincemaille², and Yi Wang^1
1Cornell University, New York, NY, United States, ²Weill Cornell Medical College, New York, NY, United States

We propose to assess the severity of Nonalcoholic fatty liver disease (NAFLD) using quantitative transport mapping (QTM), a recently introduced flow quantification method. A numerical simulation was performed to compare QTM with traditional kinetic modeling. QTM successfully reconstructed blood flow with high accuracy (relative root mean square error = 0.27). Using DCE MRI in 5 adult rats with methionine choline-deficient diet-induced NAFLD (grade F3) and in 13 untreated control rats, only the QTM derived velocity |u| showed a significant difference between NAFLD and healthy controls.

Cemre Ariyurek¹, Onur Afacan¹, Jeanne Chow¹, Simon K Warfield¹, and Sila Kurugol^1
1Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States

Estimating glomerular filtration rate (GFR) is crucial for diagnostic purposes. DCE-MRI is capable of measuring tracer kinetic parameters of kidney function. Tracer kinetic models require arterial input function to find tracer kinetic parameters such as filtration rate. However, determining the arterial input function concentration is challenging and important for estimating GFR accurately. Here, we propose to use reference region model to improve arterial input function selection to estimate GFR more accurately. Comparing the GFR values estimated using the improved arterial input function with the ground truth GFR, we have observed a significant decrease in the mean absolute error in GFR.

Yousef Mazaheri¹, Nathanael Kim¹, Yuliya Lakhman², Ramin Jafari¹, Hebert Vargas Alvarez², and Ricardo Otazo^1
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

To develop a personalized DCE-MRI parametric mapping technique using high spatial and temporal resolution golden-angle radial sparse parallel MRI (GRASP) and patient specific iterative estimation of the arterial input function (AIF) and pharmacokinetic parameters.

Karl Kiser¹, Jin Zhang¹, and S. Gene Kim^1,2
1Radiology, Weil Cornell Medical College, New York, NY, United States, ²Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States

Quantitative analysis of MRI image features for estimating tumor grading and treatment response is a growing area of research, however a lack of reproducibility and validation present a major challenge in the field. Our study investigates how spatial resolution affects texture features of DCE-MRI images by comparing features generated from 3D isotropic high resolution kinetic parameter maps with typical thick slice maps. We demonstrate that 3D textural features can differ by several orders of magnitude when extracted from isotropic versus thick slice images. These findings have potentially significant implications in the predictive capabilities of texture features.

Jean-Sébastien Louis¹, Jacques Felblinger^1,2, Olivier Huttin³, and Marine Beaumont^1,2
1IADI, Inserm U1254, Université de Lorraine, Nancy, France, ²CIC-IT, Inserm 1433, Université de Lorraine and CHRU Nancy, Nancy, France, ³Pôle cardiologie, CHRU Nancy, Nancy, France

Arterial Input Function (AIF) is fundamental for quantitative perfusion analysis. However, AIF peak is underestimated when using common perfusion sequence such SR-turboFLASH due to signal saturation effect. This leads to biased perfusion parameters estimation. Accurate AIF sampling requires specific sequence or protocol imaging not widely available. We proposed a solution to correct AIF peak retrospectively from standard perfusion data. We evaluated the proposed algorithm in simulation on more than 90000 different AIF sampling scenarios. Eventually, we tested our algorithm on clinical data and compared the population AIF estimated from our solution to literature population AIF.

Yupeng Cao^1,2, Jun Zhao^1,2, Weinan Tang³, Wentao Liu¹, and Dong Han^1,2
1National Center for Nanoscience and Technology, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Wandong Medical Technology, Beijing, China

The combination of DCE and DSC MRI contribute to the higher accuracy of diagnosis than either alone. However, high spatial-temporal resolution sequences for DCE and DSC are absent in the clinical use. Herein, we propose an improved Spiral-Out-In (iSOI) sequence to simultaneously sample DCE and DSC signal with high spatial resolution. A model-based strategy for reconstruction of 8-fold accelerated MRI were employed to realize high temporal resolution imaging. The in-plane spatial resolution is 0.78 mm and the temporal resolution is 0.52 s. The perfusion parameters were calculated to verify the proposed method which has potential to benefit the accurate diagnosis.

Ramesh Paudyal¹, Milan Grkovski¹, Jung Hun Oh¹, Heiko Schoder², David Aramburd Nunez¹, Vaios Hatzoglou², Joseph O Deasy¹, John L Hum¹, Nancy Lee³, and Amita Shukla-Dave^1,2
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

This study aims to assess the correlation between the pre-treatment quantitative imaging metrics obtained from multimodality imaging (MMI) techniques such as 18[F]-FMISO PET/CT, 18[F]-FDG PET/CT, DW- and DCE- MRI describing tumor metabolism, hypoxia, diffusion, perfusion,  and cell metabolic activity, using a community detection algorithm. The method partitioned the network into four groups with strong and weak connections.  The community connection results show complementary, rather than competitive, information about tumor metabolism, hypoxia, diffusion, and per­fusion.