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Dominique Neuhaus^1,2, Maria Janina Wendebourg^3,4, Eva Scheurer^1,2, Celine Berger^1,2, Melanie Bauer^1,2, Tanja Haas⁵, Regina Schlaeger^3,4, and Claudia Lenz^1,2
1Institute of Forensic Medicine, Dept. of Biomedical Engineering, University of Basel, Basel, Switzerland, ²Institute of Forensic Medicine, Health Department Basel-Stadt, Basel, Switzerland, ³Neurology Clinic and Polyclinic, Dept. of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland, ⁴Translational Imaging in Neurology (ThINk), Dept. of Biomedical Engineering, University of Basel, Basel, Switzerland, ⁵Division of Radiological Physics, Dept. of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland

Keywords: Quantitative Imaging, Diffusion Tensor Imaging

 The development of magnetic resonance imaging (MRI) biomarkers in neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) might alleviate current challenges. Generally, post mortem (PM) ex situ quantitative imaging is used for the validation of potential biomarkers, which shows several limitations. In this study, PM MRI brain scans of three deceased ALS patients have been conducted. The objective was to identify differences between in situ and ex situ scans regarding the diffusion tensor imaging (DTI) measures fractional anisotropy (FA) and mean diffusivity (MD). A significant difference was found for the FA values in white matter.

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Jing Li¹, Shao-yu Wang², and Xue-jun Chen^1
1Radiology, the Affiliated Cancer Hospital of Zhengzhou University (Henan Cancer Hospital), Zhengzhou, China, ²MR Scientific Marketing, Siemens Healthneers, Shanghai, Shanghai, China

Keywords: Quantitative Imaging, Tumor

This study firstly explored the potential of six diffusion-weighted MRI models for preoperative prediction of lymph node metastasis (LNM) in resectable gastric cancer (GC). The DKI_K, CTRW_D, DKI_D, FROC_D, IVIM_D*, IVIM_f, Mono_ADC, as well as morphologic indicators of tumor thickness, clinical T staging on MRI (cT), MRI reported LN status were significantly different between LNM positive and LNM negative groups. These parameters were statistically correlated with LNM. The cT, MRI reported LN, and CTRW_D were independent risk factors of LNM, combining these three parameters yielded the highest predictive efficacy.

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Wenhong Jiang¹, Siyao DU¹, Lizhi Xie², Min Zhao², Zichuan Xie³, and Lina Zhang^1
1Department of Radiology, The First Hospital of China Medical University, shenyang, China, ²GE Healthcare, Beijing, China, ³Guangzhou institute of technology, Xidian University, Guangzhou, China

Keywords: MR Fingerprinting/Synthetic MR, Breast

In this study, we evaluated and analyzed the potential correlation of first-order feature pairs from T1/T2/PD and ADC maps within different treatment response groups in locally advanced breast cancer patients undergoing neoadjuvant chemotherapy (NAC). In TNBC subtype, multiple features on ADC and PD maps strongly correlated only in pCR group, which may indicate PD map as complements to ADC for monitoring NAC response.

Zheng Ye¹, Shan Yao¹, Ting Yang¹, Qing Li², Robert Grimm³, Zhenlin Li¹, and Bin Song^1
1Department of Radiology, West China Hospital, Sichuan University, Chengdu, China, ²MR collaborations, Siemens Healthcare Ltd., Shanghai, China, ³MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

Keywords: Quantitative Imaging, Diffusion/other diffusion imaging techniques

Although diffusion weighted MR imaging is of great importance in the diagnosis and evaluation of hepatocellular carcinoma (HCC), it is limited by long acquisition times. Simultaneous multi-slice (SMS) technique can reduce scan time and has been proved to be feasible in liver imaging. Our study included twenty patients with HCC, and acquired conventional and SMS-accelerated diffusion data with different models (DWI, DKI and IVIM). By analyzing quantitative parametric maps, we found SMS technique did not affect the quantitative measurements of HCC. However, the difference of mean kurtosis between conventional and SMS-accelerated DKI in liver parenchyma should be noted.

Gaofeng Shi¹, Qi Wang¹, Hui Feng¹, Hui Liu¹, Mengyu Song¹, Xinyue Liang², and Yongming Dai^2
1Department of Radiology, Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Quantitative Imaging, Cancer

Analysis of lymph node metastasis (LNM) in lung cancer is vital for disease detection and treatment planning optimization. Multi-parametric MRI was widely used not only to characterize tumor size and anatomy, but also to assess the tissue metabolism and physiology. Conventionally, these are evaluated independently and/or are combined into an average parameter for the entire tumor, while the spatial information within the lesions was discarded. Habitat imaging allows to capture these subtle differences in tumors. In this context, a multi-parametric MRI based habitat analysis was approved to predict the LNM status. 

Zhijun Geng¹, Shaolei Li², Yunfei Zhang², Yongming Dai², and Chuanmiao Xie^1
1Sun Yat-sen University Cancer Center, Guangzhou, China, ²MR Collaboration, Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

Machine learning offers a principled approach for developing automatic algorithms for analysis of high-dimensional biomedical data. The continuous-time random-walk model (CTRW) is novel non-Gaussian diffusion model that provides promising evidence indicating a possible link between voxel-level spatiotemporal diffusion heterogeneity and microscopic intravoxel tissue heterogeneity giving the model advantages in diagnosing many diseases¹. In this study, we apply a machine-learning algorithm, the principal component analysis (PCA), for quantitative automatic diagnosis to grade rectal cancer using parameters from CTRW model. Our study shows that PCA has the potential to grade rectal cancer with higher accuracy than the original parameters. 

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Annemarie Knill^1,2, Jessica Winfield^1,2, Hannah Barnsley², Benjamin Malawo², Georgina Hopkinson², Dow-Mu Koh^1,2, Christina Messiou^1,2, and Matthew Blackledge^1
1The Institute of Cancer Research, London, United Kingdom, ²The Royal Marsden NHS Foundation Trust, London, United Kingdom

Keywords: Quantitative Imaging, Modelling

Joint fitting of ADC and R₂ improves the noise characteristics of whole-body R₂ maps when using an iterative weighted least squares (IWLS) estimation that accounts for the signal offset, ρ, between diffusion-weighted and multi-echo-time imaging sequences. This is demonstrated using simulation experiments using relevant values of R₂, ADC, and signal-to-noise ratio (SNR), where we find an improvement in R₂ precision using the joint-fitting approach. Initial validation was further performed in clinical imaging examples to demonstrate the improvement in R₂ map detail.

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Ting Yang¹, Zheng Ye¹, Shan Yao¹, Qing Li², and Bin Song^1
1West China Hospital, Chengdu, China, ²MR Collaborations, Siemens Healthineers Ltd., Shanghai, China

Keywords: Quantitative Imaging, Liver, simultaneous multi-slice diffusion weighted imaging

Diffusion weighted imaging (DWI) and gadoxetic acid-enhanced magnetic resonance imaging (MRI) are important in diagnosing hepatocellular carcinoma (HCC). Previous studies have validated that gadoxetic acid administration during enhanced MRI improves signal-to-noise ratio without influencing apparent diffusion coefficient (ADC), while the impact of simultaneous multi-slice (SMS) technique on the measurement of ADC is still unknown. Our study assessed the influence of gadoxetic acid administration on ADC values of liver with and without SMS acceleration. We found that SMS technique and gadoxetic acid administration did not affect the ADC values of liver parenchyma and HCC.

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Ning Hua¹, Andrew Ellison¹, Yansong Zhao², and Hernan Jara^1
1Radiology, Boston University, Boston, MA, United States, ²Philips Healthcare, Boston, MA, United States

Keywords: Quantitative Imaging, Diffusion Tensor Imaging, qMRI, DTI

Purpose: To augment the qMRI multi-parametricity of the Triple TSE to additionally include primary diffusion tensor imaging (DTI) functionality and with seamless geometric compatibility with the nPD, T1, and T2 maps of the Triple-TSE framework. Methods: qMRI maps were generated with algorithms based on Bloch and Bloch-Torrey equations. Results: histogram analyses for the phantom and volunteer reveal well behaved qMRI data for nPD, T1, T2, MD, and FA. Conclusion: Creating a unified clinical MP-qMRI protocol for PD, T1, T2 and diffusion tensor seems within reach.

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Daiki Sakamoto¹, Xinnan Li¹, Hiroyuki Hamaguchi¹, Katsuhisa Yamada², Hideki sudo², and Khin Khin Tha^1,3
1Laboratory for Biomarker Imaging Science, Hokkaido University Graduate School of Biomedical Science and Engineering, Hokkaido, Japan, ²Department of Orthopedic Surgery, Hokkaido University Faculty of Medicine, Hokkaido, Japan, ³Grobal Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Hokkaido, Japan

Keywords: Quantitative Imaging, Degenerative, Intervertebral disc degeneration, ADC, T1rho, T2*, Lumbar

This prospective study aimed to evaluate the role of T1ρ, T2*, and ADC – the quantitative MRI indices, in determining the severity of intervertebral disc degeneration and treatment effectiveness. All three indices decreased with increasing Pfirmman grade, which determines the severity, suggestive of their potential as indicators of disease severity. The mean ADC of the degenerated disc increased significantly after surgery, suggesting its potential as a treatment effectiveness indicator.

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Amaresha Shridhar Konar¹, Jaemin Shin², Ramesh Paudyal¹, Akash Deelip Shah³, Abhay Dave⁴, Maggie Fung², Eve LoCastro¹, Suchandrima Banerjee⁵, Nancy Lee⁶, and Amita Shukla-Dave^1,3
1Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY, United States, ²GE Healthcare, New York City, NY, United States, ³Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY, United States, ⁴Touro College of Osteopathic Medicine, New York City, NY, United States, ⁵GE Healthcare, Menlo Park, CA, United States, ⁶Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY, United States

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

A Deep learning (DL)-based reconstruction is a promising method to achieve higher resolution for diffusion-weighted Kurtosis imaging (DKI) without increasing signal averaging. The DKI phantom and patient results demonstrated improved image quality and reduced Gibbs (ringing) artifact, aiding in the robust estimation of D_app and K_app. In all phantom and patient data, the standard deviation of D_app and K_app measured in images reconstructed without DL was higher than in images reconstructed using DL. The NEX=1 significantly reduced the multi-b-value data acquisition time, and the DL-based reconstruction can produce images comparable to the standard NEX=2 or 4, depending on the b-value.

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Yanglei Wu¹, Xiaoyue Zhou², Ke Xu³, and Huayan Xu^3
1MR Collaboration, SIEMENS Healthineers Ltd., Beijing, China, ²MR Collaboration, SIEMENS Healthineers Ltd., Shanghai, China, ³Department of radiology, West China Second University Hospital, Chengdu, China

Keywords: Quantitative Imaging, Fat

The quantification of fat in humans is plays an important role in clinical diagnosis and scientific research. Especially in some cardiomyopathy characterized by fat deposition like arrhythmogenic right ventricular cardiomyopathy, and duchenne muscular dystrophy-associated cardiomyopathy. Current magnetic resonance imaging-based fat quantification methods have difficulty to characterizing cardiac etc. fat fractions. But the application of cardiac T1 mapping has been very extensive, and our proposed approach to build a partial volume model to utilize the T1 value of voxel obtained by T1 mapping can complete the measurement of cardiac fat content.

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Alexandre Triay Bagur^1,2, Paul Aljabar², Matthew Robson², Michael Brady², and Daniel P Bulte^1
1University of Oxford, Oxford, United Kingdom, ²Perspectum Ltd, Oxford, United Kingdom

Keywords: Quantitative Imaging, Relaxometry

Image analysts would welcome a measure of proton density fat fraction (PDFF) and R2* mapping error for image quality assessment in-vivo. This work adopts prior methodology using Cramér-Rao lower bound formulation and applies it to PDFF and R2* precision mapping in-vivo. The method is demonstrated in a software phantom and on one subject with fatty liver.  The calculated maps agree with the ground truth and are informative in regions where the reconstruction signal model holds. The method should be extended to calculation of PDFF error maps.

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Nathan T Roberts, PhD¹, Nikolaos Panagiotopoulos, MD², Ty Cashen, MD, PhD¹, Daiki Tamada, PhD², Diego Hernando, PhD^2,3,4,5, Arnaud Guidon, PhD¹, and Scott B Reeder, MD, PhD^2,3,4,6,7
1GE HealthCare, Waukesha, WI, United States, ²Radiology, University of Wisconsin - Madison, Madison, WI, United States, ³Medical Physics, University of Wisconsin - Madison, Madison, WI, United States, ⁴Biomedical Engineering, University of Wisconsin - Madison, Madison, WI, United States, ⁵Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, WI, United States, ⁶Medicine, University of Wisconsin - Madison, Madison, WI, United States, ⁷Emergency Medicine, University of Wisconsin - Madison, Madison, WI, United States

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

Low bias and high precision are important for accurate diagnosis, staging, and treatment monitoring of chronic liver disease using chemical shift-encoded (CSE)-MRI. However, CSE-MRI proton density fat fraction (PDFF) measurements are often biased by an asymmetric noise distribution present in PDFF maps acquired with low/moderate signal-to-noise ratio (SNR). This work investigates the use of deep learning de-noising to mitigate this bias in phantoms and in vivo. Results demonstrate that deep learning reconstruction removes or reduces noise-related PDFF estimation bias while maintaining the expected noise distribution characteristic of PDFF.

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Nikolaos Panagiotopoulos^1,2, David Rutkowski³, Alexandra Anagnostopoulos¹, Thekla H Oechtering^1,2, Diego Hernando^1,3,4,5,6, Jean Brittain³, and Scott B Reeder^{1,3,4,5,7,8
1}Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, ²Department of Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany, ³Calimetrix, Madison, WI, United States, ⁴Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Department of Departments of Electrical & Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁷Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States, ⁸Department of Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Keywords: Quantitative Imaging, Phantoms

The purpose of this work was to determine the spatial dependence of PDFF measurements at both 1.5T and 3.0T in a phantom setting. We showed that bias in CSE-MRI-based PDFF measurements is low when performed in areas not more than 20 cm from the isocenter in any direction. These results have immediate practical relevance for clinical routine, e.g., in prescribing MRI acquisitions for clinical PDFF measurements and for the correct placement of pocket phantoms in the MRI bore when used as quality assurance tools.

Chu Wang¹, Chuyu Liu¹, Benqi Zhao², Zhuozhao Zheng², and Xiaolei Song^1
1center for biomedical imaging research, school of medicine, Tsinghua Univisity, Beijing, China, ²Beijing Tsinghua Changgung Hospital, Beijing, China

Keywords: Quantitative Imaging, CEST & MT

CEST MRI is a promising molecular imaging technique, but the in vivo quantification inherently faces challenges because of multiple types of signal contaminations. Herein we introduced a QUEST-like 10-point fitting method for simultaneous NOE and amide quantification at 3T with reduced T1 contamination. The quantification method was evaluated by simulations and phantom experiments. Furthermore, for glioblastoma patients, the highlighted regions on fitted amide images are consistent with the hyperintensities on Gd-T1W maps, which is a smaller enhanced area than those on conventional APTw images. The initial results on tumor patients indicate its potential for clinical use.

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Adam Carscadden¹, Nathaniel Bly¹, Anthony G. Tessier^1,2, Catherine J. Field³, and Atiyah Yahya^1,2
1Department of Oncology, University of Alberta, Edmonton, AB, Canada, ²Department of Medical Physics, Cross Cancer Institute, Edmonton, AB, Canada, ³Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada

Keywords: Quantitative Imaging, Spectroscopy, fat quantification

In this work at 9.4 T, it is demonstrated that the apparent T₂ (includes losses due to J-coupling) relaxation times of the allylic fat protons (≈ 2.1 ppm) is correlated with higher ω-3 content.  J-coupling evolution appears to refocus with a PRESS TE of 270 ms rendering it a suitable TE for obtaining T₂­ estimates.  Experiments were performed on oils of varying ω-3 content and preliminary in-vivo experiments were performed on two mice, each fed a different fat diet.

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Michael C Langham¹, Jing Xu¹, and Felix Wehrli^1
1Radiology, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Quantitative Imaging, Quantitative Imaging, Obstructive sleep apnea

Obstructive sleep apnea is associated with changes in upper airway morphology and sleep disturbances from transient nocturnal hypoxemia and hypoxia. The latter adversely affects brain metabolism and neurologic function. MRI is the only non-invasive modality cable of concurrently visualize airway architecture and quantify neurometabolism during sleep. We present an interleaved pulse sequence and demonstrate its ability to detect metabolic response and airway anatomic alteration from a swallowing apnea (SA). Although airway anatomy during SA is distinctively different from a breath-hold apnea both result in similar physiological responses in terms of cerebral blood flow and venous blood oxygenation.

Jingcheng Huang¹, Qingqing Wen², Weiqiang Dou², and Xianfu Luo^1
1Clinical Medical School of Yangzhou University, Northern Jiangsu People’s Hospital, Yangzhou, China, Yangzhou City, China, ²GE Healthcare, MR Research China, Beijing, P.R. China, Beijing City, China

Keywords: Quantitative Imaging, CEST & MT, APTw

Conventional MRI methods are difficult to reveal histologic and molecular characteristic heterogeneity of HCC. In this study, we aimed to investigate the relationship between APTw and Ki-67 labeling index (LI) in hepatocellular carcinoma (HCC) to evaluate whether APTw can reflect the pathological information of HCC at the molecular level. It was found that APTw values were significantly different between low and high Ki-67 LI in HCC, and the AUC to distinguish high and low Ki-67 LI was 0.794, indicating APTw imaging might be a potential molecular technique for predicting the malignancy grade of HCC

Yu Zhao¹ and Zhongliang Zu^1
1Vanderbilt University Medical Center, Nashville, TN, United States

Keywords: Quantitative Imaging, CEST & MT

In this study, we proposed a new data-postprocessing method to specifically quantify the APT and NOE effects based on two canonical CEST MRI acquisitions with double saturation powers (DSP). First, Numerical simulations based on Bloch equations is used to demonstrate that the proposed method can detect the APT and NOE effects with a removal of background signals from the direct water saturation (DS) and the semi-solid magnetization transfer (MT) and the CEST signals that originate from the fast-exchange pools. Then, an in vivo validation of the proposed method is conducted using an animal tumor model at a 4.7-T scanner.