Zhu Fang¹, Hong Pu¹, Xiaoli Chen², Yi Yuan¹, Feng Zhang¹, and Hang Li^1
1Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China, ²Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China

Keywords: Radiomics, Radiomics

To develop and validate clinical-radiomics models for predicting lymph node metastasis following neoadjuvant chemoradiation therapy in locally advanced rectal cancer .83 patients were retrospectively enrolled.pre-,post- and delta radiomics signatures of T2WI and ADC images were constructed by support vector machine model.These models were applied to predict LNM and 5-year disease-free survival.The clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training cohort (AUC=0.895) and validation cohort (AUC=0.900). In ypT0-T2 stage, this model could predict 5-year RFS. Clinical-deltaADC radiomics combined model has good performance to predict LNM after nCRT and helped identify patients with poor prognosis.

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Tingting Xu¹, Xueli Zhang¹, Ting Hua¹, Guangyu Tang¹, lin Zhang¹, and Xiance Zhao^2
1Radiology, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, China, ²Philips Healthcare, Shanghai, China

Keywords: Radiomics, Machine Learning/Artificial Intelligence, Triple-negative breast cancer. DCE-MRI. ADC maps

43 TNBCs and 84 Non-TNBCs were allocated in this retrospective study.The lesions were manually segmented with ITK-SNAP software then whole volume radiomics features were extracted with Radcloud radiomics platform based on DCE-MRI and ADC maps, respectively. Three prediction models were constructed by using support vector machine (SVM) classifier, including Model A (based on the selected features of ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). The radiomics features model combining DCE-MRI and ADC maps can improve the diagnostic performance of predicting TNBC.

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Kilian Hett¹, Colin D. McKnight², Jarrod Eisma¹, Adreanna B. Hernandez¹, Melanie Leguizamon¹, Jason Elenberger¹, Ciaran M. Considine¹, Daniel O. Claassen¹, and Manus J. Donahue^1
1Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ²Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, United States

Keywords: Data Analysis, Neurodegeneration

The overall goal of this work is to apply novel MRI methods, sensitive to CSF production and clearance, to test fundamental hypotheses regarding altered neurofluid circulation in patients with Huntington’s disease (HD). In participants with HD and healthy age- and sex-matched controls, we applied multi-modal MRI to investigate deviations of neuroimaging biomarkers of the neurofluid circuit at different locations: choroid plexus, cerebral aqueduct, and PSD. Analyses show a significant increase in ChP volume and PSD volume, and a decrease in ChP perfusion and CSF net flow, in HD participants relative to healthy controls.

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Dafa Shi¹, Haoran Zhang¹, Guangsong Wang¹, and Ke Ren^1
1Department of Radiology, Xiang’an Hospital of Xiamen Uneversity,School of Medicine, Xiamen University, Xiamen, China

Keywords: Data Analysis, Brain Connectivity, high-order functional connectivity

Schizophrenia (SZ) is one of the most prevalent mental disorders; however, its accurate diagnosis is difficult in clinical practice. Currently, the underlying mechanism of SZ remains poorly understood. The associated higher-order functional connectivity (HOFC) which constructed based on the conventional FC is promising for understanding pathological changes of brain connectome. In our study,we found the model constructed with associated HOFC outperformed the model constructed with conventional FC. SZ-related brain regions were widely distributed in frontal, parietal, insula, occipital, subcortical, and limbic lobes, which are the core brain areas of the subcortical, fronto-parietal, sensorimotor, limbic, and default mode networks.

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Nasrin Akbari¹, Saurabh Garg¹, Thanh-Duc Nguyen¹, Saqib Basar¹, Mostafa Fatehi², Yosef Chodakiewitz³, Rajpaul Attariwala^1,3, Sean London³, and Sam Hashemi^1,3
1Voxelwise Imaging Technology Inc, Vancouver, BC, Canada, ²Vancouver General Hospital, Vancouver, BC, Canada, ³Prenuvo, Vancouver, BC, Canada

Keywords: Data Analysis, Brain, A cross-sectional study, Brain quantification, Brain volume analysis

Cross-sectional neuroimaging studies have indicated hemispheric asymmetry, gender differences and a decline in brain volume with advancing age. However, these studies often have a small sample size (≤ 500) and unbalanced groupings, resulting in contradictory findings that might not be reproducible. Precise estimates on a large cohort will facilitate the assessment and quantification of brain volume, resulting in improved diagnostics and establishing statistical norms. In this study we analyze 6739 individuals to draw normative conclusions about typical patterns of age-related brain changes. We provide whole-brain and regional volume aging-curves for males and females for raw and head-size adjusted data.

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Marianna Inglese^1,2, Matteo Ferrante¹, Tommaso Boccato¹, Shah Islam³, Matthew Williams^4,5, Adam D Waldman⁶, Kevin O'Neill⁷, Eric O Aboagye³, and Nicola Toschi^1,8
1Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy, ²Surgery and Cancer, Imperial College London, London, United Kingdom, ³Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁴Computational Oncology Group - Department of Surgery and Cancer, Imperial College London, London, United Kingdom, ⁵Institute for Global Health Innovation, Imperial College London, London, United Kingdom, ⁶Centre for Clinical Brain Sciences, University of Edinburgh, Edimburgh, United Kingdom, ⁷Imperial College Healthcare NHS Trust, London, United Kingdom, ⁸Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, NY, United States

Keywords: Data Processing, Modelling, Deep Learning, convolutional filters

Stratifying human brain gliomas using imaging techniques is extremely challenging. Valuable insight into the characterization and classification of gliomas can be provided by integrating two imaging modalities, i.e. ¹⁸F-FPIA PET and MRI. This study introduces a new approach for glioma stratification based on the extraction of temporal features from tissue time activity curves (TACs) extracted from dynamic PET/MRI data. We exploit tissue-specific biochemical properties embedded in the TACs through deep learning and achieve good discrimination results while foregoing pharmacokinetic fitting and hence invasive measurement of the AIF.

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Owen Bleddyn Woodward¹, Ian Driver¹, Michael Germuska¹, and Richard Wise^2
1CUBRIC, Cardiff University, Cardiff, United Kingdom, ²Department of Neuroscience, Imaging and Clinical Science, 'G. D'Annunzio University' of Chieti-Pescara, Chieti, Italy

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Co-registration

Accurate brainstem co-registration is important when analysing brainstem functional MRI data. We trained a convolutional neural network (CNN) to detect a set of brainstem landmarks and to define a brainstem region-of-interest and used these to co-register the brainstem between functional and anatomical space using previously developed landmark-based and automated brainstem co-registration (LBC and ABC) methods. The use of CNNs to supply these features to LBC and ABC produced results that compared well to conventional methods. Similar CNNs could be applied to other brain regions and such methods may be useful to automate the analysis of large functional datasets.

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Maruf Ahmed¹, Jingjia Chen¹, Yann Cobigo², Suzanne Baker³, and Chunlei Liu^1,4
1EECS, University of California, Berkeley, Berkeley, CA, United States, ²University of California, San Francisco, San Francisco, CA, United States, ³Lawrence Berkeley National Laboratory, Berkeley, CA, United States, ⁴Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States

Keywords: Data Processing, Quantitative Susceptibility mapping

DECOMPOSE-QSM method is used to extract a sub-voxel level paramagnetic component susceptibility (PCS) in Alzheimer’s disease (AD) related neurodegeneration. Correlation between both PCS and thresholded QSM values with tau PET SUVr are studied. PCS shows stronger association strength with tau PET measures compared to susceptibility measured using only the thresholded positive voxels (positive QSM) in the QSM map in known regions related to tau pathological progression. By separating sub-voxel paramagnetic and diamagnetic sources, DECOMPOSE-QSM provides a more specific and sensitive measure of iron-related pathology.

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Gianna Nossa¹, Humberto Monsivais², Chang Guen Lee², Jae Hong Park², and Ulrike Dydak^2,3
1Purdue University, West Lafayette, IN, United States, ²School of Health Science, Purdue University, West Lafayette, IN, United States, ³Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States

Keywords: Data Analysis, Toxicity

Chronic exposure to Mn and Fe, through inhalation of welding fumes, is known to cause neurotoxicity. In this MRS study, we aim to determine if toenail Mn and Fe levels can be predictive of neurotoxicity identified through changes in GABA, GSH, and/or Glu concentrations. Significant GABA and GSH correlations with Fe indicate that toenail Fe concentrations can reflect decreased GABA and GSH levels in the welders’ brain. This emphasizes that Fe should not be ignored and might contribute to oxidative stress in Mn neurotoxicity.

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Anuja Sharma¹ and Edward DiBella^1
1Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States

Keywords: Data Analysis, Machine Learning/Artificial Intelligence, Stroke diffusion modeling

Limited prior work has explored longitudinal modeling of human brain stroke using advanced diffusion techniques. We aim to address this gap by analyzing longitudinal stroke data from diffusion spectrum imaging for modeling and predicting clinical markers of stroke recovery. Our proposed data analysis method uses a mixed-effect model which exploits stochastic distances from these images for improved regression model statistics and handling of imbalanced, inconsistent longitudinal data. We also demonstrate that this aids in differentiating between population-level and patient-level effects and the corresponding key contributing predictors at each of these levels.

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Juan Pablo Esparza¹, Cara Morin^2,3, Chris Goode³, Zachary Abramson³, and Aaryani Tipirneni-Sajja^1,3
1Biomedical Engineering, The University of Memphis, Memphis, TN, United States, ²Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, ³Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States

Keywords: Data Analysis, Liver

FerriScan is considered the gold standard to quantify liver iron content (LIC) by MRI. Recently, FerriSmart, an AI—based algorithm was developed to permit faster postprocessing of R2-based LIC. To our knowledge, FerriSmart results have not been compared to R2*-LIC methods. In this work, R2*, R2*-LIC, and FerriSmart LIC were compared to evaluate their agreement.

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Keywords: Machine Learning/Artificial Intelligence, Urogenital

Ovarian sex cord-stromal tumors (SCSTs) are rare nonepithelial neoplasms that usually are benign or at early stages, but sometimes they are confused with malignant tumors such as epithelial ovarian cancers (EOCs). We constructed five models including clinical model, conventional MR model, traditional model, radiomics model and mixed model based on logistic regression classifier to distinguish SCSTs and EOCs. The performance of each model was evaluated. The radiomics approach showed excellent prediction results, and the mixed model stood out among all the models.

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Fatemeh Zabihollahy¹, Qi Miao², Ida Sonni¹, Harrison Kim³, Robert E Reiter⁴, Steven S Raman¹, and Kyunghyun Sung^1
1Department of Radiological Sciences, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, United States, ²The First Affiliated Hospital of China Medical University, Shenyang City, China, ³Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States, ⁴Department of Urology, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, United States

Keywords: Data Analysis, Multimodal

Race and ethnicity strongly impact the risk of prostate cancer (PCa). In this study, the differences in the performance of mpMRI for PCa diagnosis are investigated between African American (AA) and Caucasian American (CA) men. After matching commonly known clinical risk factors, the diagnostic performances were compared between two racial groups regarding cancer prevalence, detection rates, and positive predictive values. Our result demonstrated that there are significant differences between AA and CA men in detecting clinically significant PCa when stratified by different prostate zones.

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Guimian Zhong¹, Mengzhu Wang², Qijia Han¹, and Zhiming Xiang^1
1Department of Radiology, Guangzhou Panyu Center Hospital, Guangzhou, China, ²Siemens Healthineers Ltd, Guangzhou, China

Keywords: Radiomics, Diffusion/other diffusion imaging techniques, Histogram analysis；magnetic resonance imaging；Renal function

This study established and validated a predictive model based on histogram features of four diffusion models to identify and evaluate early renal impairment in CDK. The results suggested that the model based on the ADC and MK could distinguish the normal and mild CDK well, and could accurately and noninvasively evaluate and predict early CDK renal dysfunction.

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Eryn E Kwon^1,2,3, Maryam Tayebi^1,3, Joshua McGeown^1,2, Matthew McDonald^1,2, Patrick McHugh^1,4, Paul Condron^1,2, Leigh Potter^1,5, Davidson Taylor^1,6, Jerome Maller⁷, Miao Qiao⁸, Alan Wang^1,2,3, Poul Nielsen^3,8, Justin Fernandez^1,3,8, Miriam Scadeng^1,2, Samantha Holdsworth^1,2, and Vickie Shim^1,3
1Mātai Medical Research Institute, Tairāwhiti/Gisborne, New Zealand, ²Faculty of Medical and Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand, ³Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, ⁴Tūranga Health, Tūranganui-a-kiwa, Tairāwhiti/Gisborne, New Zealand, ⁵Ngāti Porou, Ngāti Kahungunu, Rongomaiwahine, Rongowhakaata, Tairāwhiti/Gisborne, New Zealand, ⁶Ngai Tāmanuhiri, Rongowhakaata, Ngāti Porou, Tairāwhiti/Gisborne, New Zealand, ⁷General Electric Healthcare, Victoria, Australia, ⁸Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland, New Zealand

Keywords: Data Analysis, Data Analysis, DTI, diffusion MRI, PCA, Mesh-fitting, subject-specific model, data aggregation

Averaging is commonly used for data reduction/aggregation to summarise such high-dimensional data, resulting in information loss. However, individual variability makes group-wise comparisons difficult without data reduction/aggregation. To address these issues, we developed a novel technique that integrates diffusion tensor (DTI) metrics along the whole volumes of the fibre bundle using a mesh-fitting technique. Using the right Corticospinal Tract (rCST) as an example, we demonstrate the utility of the method in detecting differences in DTI metrics of contact-sports and non-contact-sports athletes.

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Eros Montin^1,2, Marco Muccio¹, Yulin Ge¹, and Riccardo Lattanzi^1,2,3
1Center for Advanced Imaging Innovation and Research (CAI2R) Department of Radiology, Radiology Department, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States, ²Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States, ³Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA, New York, NY, United States

Keywords: Radiomics, Multiple Sclerosis

In this study, we applied imaged-based radiomic techniques to phase contrast (PC) MRI images to distinguish the blood flow through the neck-feeding arteries of healthy controls (HC) from MS patients. By applying a simple machine learning model, k nearest neighbor, we found that first order features of the arteries’ regions of interest (ROI), drawn on phase images, reported the best accuracy (0.80) in labeling MS patients and HCs. PC-MRI is a fast and reliable imaging technique that, in conjunction with radiomics, offers great clinical potential to further quantify the diagnosis of MS, currently relying on qualitative approaches.

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Lucas Murilo da Costa¹, André Monteiro Paschoal², and Renata Leoni^1
1University of São Paulo, Ribeirão Preto, Brazil, ²Instituto de Radiologia e Departamento de Radiologia e Oncologia, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil

Keywords: Data Processing, Diffusion/other diffusion imaging techniques

We proposed an Artificial Neural Network (ANN) to fit the IVIM images and estimate diffusion, pseudo-diffusion, and perfusion fraction values. The ANN was first trained with simulated data and tested with images of 40 healthy controls and one patient with glioma. ANN provided parameter values similar to those obtained with a standard fitting method, but D* maps with less noise. The ANN fitting process was less time-consuming and was shown to be a possible tool for replacing a standard fitting algorithm.

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Hiroyuki Kabasawa¹, Masatoshi Kojima², Daisuke Yajima³, and Yohsuke Makino^4
1Department of Radiological Sciences, Internationa University of Health and Walfare, Narita, Japan, ²Department of Legal Medicine, Chiba University, Chiba, Japan, ³Department of Forensic Medicine, International University of Health and Welfare, Narita, Japan, ⁴Department of Forensic Medicine, The University of Tokyo, Tokyo, Japan

Keywords: Software Tools, Software Tools, post mortem imaging

A MR Image simulation system for post mortem subject scanning is developed and validated. The system has MR console like user interface. It has body temperature input as well as tissue fixation condition input, so that post mortem MR imaging can be simulated for various scanning condition. The system was validated for T1 weighted image and FLAIR for low body temperature condition. The result showed the system could simulate the actual acquired image contrast change associated with body temperature change. The proposed system can be used to predict MR contrast change associated with body temperature as well as fixation condition.

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Jinwon Kim¹, Hyebin Lee², Jinhee Jang², and Hyunyeol Lee^1
1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Korea, Republic of, ²Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of

Keywords: Machine Learning/Artificial Intelligence, Quantitative Imaging

In this work, we aim to develop a deep learning (DL)-based processing pipeline that enables rapid and correct segmentation of brain-feeding arteries in neck phase-contrast (PC) MR images, thereby achieving accurate quantification of total cerebral blood flow (tCBF) in an automated manner. To this end, we implemented a U-Net architecture where magnitude/phase-combined PC images are provided for network training. The results suggest that the present, automated method yields accurate measurements of tCBF in comparison to ground truth values obtained from manual vessel segmentation. 

Rajesh Harsh¹, URVASHI SINGH¹, and Dharmesh Verma^1
1Medical Systems Division, S.A.M.E.E.R. Mumbai, Mumbai, India

Keywords: Data Analysis, Artifacts

Study demonstrate the analysis of surface morphology and sample localization with variation from iso-center in the presence of non-linearity in gradient. In these experiments, we have analyzed that gradient non-linearity plays a crucial role in surface morphology and localization of samples. For the end position 200 mm and -200 mm, size of the object appears small. Additionally, the results suggest that when a large sample size is taken into account, gradient non-linearity exhibits greater negative impacts.