Longfei Li^1,2, Rui Yang³, Xin Chen⁴, Cheng Li², Hairong Zheng², Yusong Lin¹, Zaiyi Liu⁴, and Shanshan Wang^2
1the Collaborative Innovation Center for Internet Healthcare , School of Information Engineering, Zhengzhou University, Zhengzhou, China, ²Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ³Department of Urology, Renmin Hospital of Wuhan University, Wuhan, China, ⁴Department of Radiology, Guangdong Provincial People’s Hospital, Guangzhou, China

Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classifies patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance. However, there is no consensus on whether PI-RADS 3 patients should go through biopsies. Mining features from these hard samples (HS) is meaningful for physicians to achieve accurate diagnoses. Currently, the mining of HS biomarkers is insufficient, and the effectiveness and robustness of HS biomarkers for prostate cancer diagnosis have not been explored. In this study, biomarkers from different data distributions are constructed. Results show that HS biomarkers can achieve better performances in different data distributions.

Martin Buechert¹, Jan Lipovsek², Marco Reisert², Harald Horbach², Wilfried Reichardt², Christopher Schlett², Fabian Bamberg², Peggy Sekula ², Anna Köttgen², and Elias Kellner^2
1Magnetic Resonance Development and Applicatione Center, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany, ²Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany

A processing pipeline for kidney segmentation using a hierarchical patch-based stack of U-nets was implemented and applied to abdominal MRI images of the German National Cohort study.  The training data set included  300 cases and the final net was applied to the dataset of 11,207 MRIs. The compartments cortex, medulla and hilus could be segmented very robustly with the network. The relation of first parameters based on the segmentation withsex, age, weight, subject size and BMI are presented. This is an optimal starting point to identify more advanced biomarkers and their correlations, especially with kidney functional parameters.

Meshari T Alshammari^1,2, Ali S Alyami³, Victoria Wilkinson-Smith³, Luca Marciani¹, Gordon W. Moran¹, and Caroline L. Hoad^3,4
1Translational Medical Sciences and National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, United Kingdom, ²Department of Diagnostic Radiology, College of Applied Medical Sciences, University of Hail, Hail, Saudi Arabia, ³NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, United Kingdom, ⁴Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom

MRI Tagging techniques have been applied to the GI tract to assess bowel contractions and colon content mixing. Two independent datasets of healthy adults were used to evaluate the dependence of the percentage Coefficient of variation (%CoV) measurement on inter-observer variability in the ascending colon and descending colon. There was little difference between both AC and DC measurements of the %CoV with high inter-rater agreement (intra-class correlation coefficient > 0.95). This technique could be used to provide objective measures for the motility assessment of the colon in inflammatory and functional bowel diseases. 

 

Diana Bencikova^1,2, Veronika Janacova¹, Marcus Raudner¹, Ahmed Ba-Ssalamah¹, Siegfried Trattnig^1,2, and Martin Krssak^3
1Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria, ²Christian Doppler Laboratory for Clinical Molecular Imaging, MOLIMA, Vienna, Austria, ³Division of Endocrinology and Metabolism, Department of Medicine III, Medical University Vienna, Vienna, Austria

GLCM texture analysis is a promising technique for characterizing and classifying tissue pathologies. In liver applications it has been mostly performed on T2-weighted images. Fast radial T2 mapping techniques enable acquiring T2 maps in clinically feasible measurement time (during breath-hold). Here, we explored the performance of the different settings of GLCM texture analysis in liver T2 maps, which might be more advantageous compared to T2-weighted images. We identified the grey-level quantization of 8bits and direction of 90° as the best setting for discrimination between fibrotic and healthy livers. 

Victor Fritz¹, Martin Schwartz^1,2, Jens Kübler³, Simon Böke⁴, Jonas Habrich⁵, Daniel Zips⁴, Daniela Thorwarth⁵, Konstantin Nikolaou³, and Fritz Schick^1
1Department of Diagnostic and Interventional Radiology, University of Tuebingen, Section on Experimental Radiology, Tuebingen, Germany, ²University of Stuttgart, Institute of Signal Processing and System Theory, Stuttgart, Germany, ³Department of Diagnostic and Interventional Radiology, University of Tuebingen, University Hospital Tuebingen, Tuebingen, Germany, ⁴Department of Radiation Oncology, University of Tuebingen, University Hospital and Medical Faculty, Tuebingen, Germany, ⁵Department of Radiation Oncology, University of Tuebingen, Section for Biomedical Physics, Tuebingen, Germany

The aim of this work was to identify potential texture features as imaging biomarkers for monitoring treatment response in head and neck cancer (HNC). For this purpose, a total number of 93 texture features were extracted on segmented calculated ADC maps and compared at baseline and in the early treatment phase of radiation therapy (RT). Fifteen texture features showed a statistically difference in the course of RT. In particular, features suggesting that ADC-based HNC texture became finer but more heterogeneous changed significantly. Presented preliminary results offer initial findings that will be systematically investigated in upcoming studies.

Dyah Ekashanti Octorina Dewi¹, Mohammed R. S. Sunoqrot¹, Gabriel Addio Nketiah¹, Elise Sandsmark², Sverre Langørgen², Helena Bertilsson³, Mattijs Elschot^1,2, and Tone Frost Bathen^1,2
1Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ²Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, ³Department of Urology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

Identification of progressive lesions over time is important to monitor the prostate cancer progression in active surveillance. Delta Radiomics, computed as the relative changes of features among scans, provides temporal evolution of features that may indicate clinical changes along time periods. This pilot study aims to develop a framework to identify progressive lesions on two long-term scans in prostate T2WI based on Delta-radiomics. The current results show that four Delta-radiomics features from GLDM and Shape feature groups have potentials to identify progressive lesions compared to other selected reproducible features.

Simona Turco¹, Catarina Dinis Fernandes², Razvan Miclea³, Ivo Schoots⁴, Peet Nooijen⁵, Hans van der Linden⁵, Jelle Barentsz⁶, Stijn Heijmink ⁷, Hessel Wijkstra², and Massimo Mischi^2
1Electrical Engineering, Eindhoven Univeristy of Technology, Eindhoven, Netherlands, ²Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, ³Abdominal radiology, Maastricht University Medical Center, Maastricht, Netherlands, ⁴Abdominal radiology, Erasmus Medical Center, Rotterdam, Netherlands, ⁵Uropathology, Jeroen Bosch Ziekenhuis, 's-Hertogenbosch, Netherlands, ⁶Radiology, Radboud university medical center, Nijmegen, Netherlands, ⁷Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands

Multiparametric MRI, including T2-weighted, diffusion-weighted, and dynamic contrast enhanced (DCE) imaging, is the recommended imaging modality for prostate cancer (PCa). Although the role of DCE-MRI has been greatly limited in recent years, only qualitative analysis of DCE is currently performed.  Here we propose magnetic resonance dispersion imaging (MRDI) to obtain quantitative parametric maps from DCE-MRI. Comparing the performance of two radiologists, our results show that some clinically-significant PCa are only found by interpretation of either mpMRI alone or MRDI maps alone, suggesting that combined interpretation of MRDI and mpMRI may improve PCa diagnosis.