View Presentation Video

Chaoxing Huang^1,2, Yurui Qian¹, Jian Hou¹, Baiyan Jiang^1,3, Queenie Chan⁴, Vincent Wai-Sun Wong⁵, Winnie Chiu-Wing Chu^1,2, and Weitian Chen^1,2
1Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong, ²CUHK Lab of AI in Radiology (CLAIR), Shatin, Hong Kong, ³Illuminatio Medical Technology Limited, Hong Kong SAR, China, ⁴Philips Healthcare, Hong Kong SAR, China, ⁵Department of Medicine and and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong

Keywords: Quantitative Imaging, Liver

A self-supervised learning based multiparametric mapping method is proposed to map T1ρ and T2 simultaneously, by utilising the relaxation constraint in the learning process. The method was examined on a dataset of 52 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional fitting method, with reduced number of images, and reduced scan time.

View Presentation Video

Yongquan Ye¹, Jian Xu¹, Zhongqi Zhang¹, Yan Zhang², Qiang Zhao³, Jiajia Xu³, and Huishu Yuan^3
1United Imaging, Houston, TX, United States, ²Beijing United Imaging Intelligent Imaging Technology Research Institute, Beijing, China, ³Radiology, Peking University Third Hospital, Beijing, China

Keywords: Quantitative Imaging, Quantitative Imaging

A dual MDI strategy was developed and demonstrated to achieve T₂* and R₂* mapping that are both highly delineated between noise backgrounds and normal tissues as well as spikes free, thus eliminating the need for routine noise masking or manual segmentation. 

View Presentation Video

Zongye Li¹, Yijie Yang², Yue Zhang¹, Yanhong Lin², Xiao Wang¹, Yuchuan Zhuang³, Qinqin Yang², Eryuan Gao¹, Yanan Ren¹, Yong Zhang¹, Shuhui Cai², Zhong Chen², Congbo Cai², Jingliang Cheng¹, and Jianfeng Bao^1
1Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Department of Electronic Science, Xiamen University, Xiamen, China, ³Department of Imaging Sciences, University of Rochester, Rochester, NY, United States

Keywords: Quantitative Imaging, Neurography

We assess the ability of a novel single-shot quantitative T2 magnetic resonance imaging (MRI) sequence and apparent diffusion coefficient (ADC) maps in predicting the WHO grade in meningiomas. We used histogram analysis of T2 maps and ADC maps to compare different grades of meningioma. ADC P10 proved to be the best predictor among all histogram parameters of ADC. While histogram analysis of T2 maps did not receive satisfactory results, the combination of T2 kurtosis and ADC P10 proved to be the best predictor. It is worthy of consideration to try another method of ROI placement and increase the sample size.

View Presentation Video

Ronal Coronado^1,2, Carlos Castillo-Passi^3,4, Cecilia Besa^2,5, and Pablo Irarrazaval^2,6
1Biomedical Imaging Center-Universidad Catolica de Chile, Santiago, Chile, ²Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile, ³Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Santiago, Chile, ⁴King's College London, London, United Kingdom, ⁵Departamento de Radiologia-Universidad Católica de Chile, Santiago, Chile, ⁶Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Keywords: Quantitative Imaging, Prostate

Balanced Steady State Free Precession (bSSFP) has been successfully used to estimate T2 maps (DESPOT2), but bSSFP is prone to off-resonance artifacts. These artifacts affect the reconstruction of the T2 maps. We propose a dictionary-matching reconstruction using the signal model of bSSFP and spoiled gradient echo (SPGR). We tested this approach with simulation, 3D phantom and 3D in-vivo prostate acquisitions. In all of them, the off-resonance banding artifacts are greatly reduced, which is reflected in reduced NRMSE values. This method allows using a faster sequence with high SNR to quantify complicated body regions like the prostate. 
 

Jian Wu¹, Taishan Kang², Simin Li¹, Weikun Chen¹, Zhigang Wu³, Congbo Cai¹, and Shuhui Cai^1
1Department of Electronic Science, Xiamen University, Xiamen, China, ²Department of Radiology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China, ³Clinical & Technical Solutions, Philips Healthcare, Shenzhen, China

Keywords: Quantitative Imaging, Kidney

The overlapping-echo detachment (OLED) imaging can capture a T₂ map within about 150 ms. However, when the region of interest (ROI) is smaller than the object, it is inefficient to acquire the whole object because many unnecessary phase-encoding steps should be acquired. The zonal oblique multislice (ZOOM) is a method to reduce the field of view (FOV) by using two pulses to excite two slabs that have a specific angle between them. This study combined ZOOM with OLED to capture the T₂ map with reduced FOV. The ZOOM-OLED can reduce geometric distortion and improve image resolution.

Hai-Yan Chen¹, Kai Li¹, Yi-Shi Wang², and Lei Shi^1
1The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China, ²Philips Healthcare, Beijing, China

Keywords: Quantitative Imaging, Quantitative Imaging

There was no related study focused on pancreatic cancer, and we have no idea whether T1 mapping and ECV fraction could be used in such patients. Our study was to evaluate whether the T1 relaxation time of the pancreas can detect parenchymal changes in pancreatic cancer (PC) patients and whether extracellular volume (ECV) fraction improves the diagnostic value.

We found that native T1 relaxation time of the pancreas in patients with PCs was significantly higher than patients with Non-PCs and volunteers, respectively. The pancreatic ECV was failed to show a significant difference among three groups.

Tong Fu¹, Rongrong Pan², Youyong Tian², Qing Gao², Xinying Wu ¹, Lindong Liu¹, Hai Lin³, and Yongming Dai^3
1Department of Radiology, Nanjing first hospital，Nanjing Medical University, Nanjing, China, ²Department of Neurology, Nanjing first hospital，Nanjing Medical University, Nanjing, China, ³Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Quantitative Imaging, Multi-Contrast

Quantitative MRI can measure a variety of physiological tissue parameters, such as longitudinal T1 value, transverse T2 value and proton density, iron content and fat content. We tried to apply a multi-parametric MR imaging technique of MULTIPLEX that provides the maps of T1, T2*, proton density and quantitative susceptibility mapping into the diagnosis of early Parkinson’s disease (PD). We found that MULTIPLEX could provide comprehensive and quantitative assessment of Parkinson’s disease-related subcortical nucleus and dopaminergic midbrain regions captured in multiple MR imaging parameters, which might assist in the diagnosis and better understanding of early Parkinson’s disease under tight clinical time-constraints.

View Presentation Video

Ryotaro Jingu¹, Keisuke Sato², Atsushi Nozaki³, Tetsuya Wakayama³, Ryuji Nakamuta¹, and Kengo Yoshimitsu^2
1Radiology center, Fukuoka University Hospital, Fukuoka city, Japan, ²Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka city, Japan, ³GE Healthcare, Hino city, Japan

Keywords: Quantitative Imaging, Quantitative Imaging, T1 mapping

  In phantom study, SPGR-MOLLI acquisition scheme was optimized for abdominal T₁ mapping in EOB-MRI, which was applied to clinical patients to measure ECV of the spleen and paraspinal muscle obtained 4 min and 25 min after the contrast administration. ECV obtained from contrast-enhanced CT within 3 months from EOB-MRI was used as reference standard. ECVs calculated from EOB-MRI showed significant agreements with those obtained from CT with ICCs of 0.9 and 0.74 for 4 min and 25 min, respectively (p<0.0001), which suggested that our T₁ mapping protocol is appropriate and applicable to ECV measurement in EOB-MRI similarly with contrast-enhanced CT.

View Presentation Video

Kiyoka Maeba¹, Akihiko Kanki¹, Yoshihiko Fukukura¹, Hidemitsu Sotozono¹, Akira Yamamoto¹, and Tsutomu Tamada^1
1Kawasaki Medical School Hospital, Kurashiki, Japan

Keywords: Quantitative Imaging, Pancreas

This study focused on the feasibility of quantitative MR evaluation of the pancreas parenchyma in patients with IPMN as a non-invasive tool for predicting the risk of malignancy. Our study showed that increased signal intensity on T2WI and longer T1 relaxation time were associated with a higher risk of malignant IPMN. These results suggested that quantitative MR evaluation of the underlying pancreas parenchyma in patients with IPMN can be a surrogate marker for predicting malignant IPMN.

View Presentation Video

Jyoti Mangal^1,2, Ayse Sila Dokumaci^1,2, David Leitao^1,2, Raphael Tomi-Tricot^1,2,3, Pip Bridgen^1,4, Joseph V Hajnal^2,5, Claudia Prieto¹, Shaihan Malik^1,2, and David W Carmichael^1,2
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²London Collaborative Ultra high field System (LoCUS), London, United Kingdom, ³MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom, ⁴London Collaborative Ultra high field System (LoCUS), Lodnon, United Kingdom, ⁵Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Keywords: Quantitative Imaging, Neuro, MPM, sequence optimization

Quantitative MRI at 7T may have important clinical applications owing to the potential for enhanced resolution and uniform tissue characterisation. Current protocols, often for pragmatic reasons, use short TRs(~20ms) and a handful of echo times but this might not be optimal for T2* estimation. We simulated the optimal TR and echo number for dual flip angle mapping of T1, PD, and T2* for realistic 7T tissue and sequence parameters. The optimal efficiency was found to be at 65ms with 20 echoes. The simulations suggested that longer TRs of 40-60ms would be optimal. Preliminary in-vivo data only partially supported these findings

View Presentation Video

Qingdang Qin¹, Jiechao Wang¹, Zhigang Wu², Shuhui Cai¹, and Congbo Cai^1
1Department of Electronic Science, Xiamen University, Xiamen, China, ²MSC Clinical & Technical Solutions, Philips Healthcare, ShenZhen, China

Keywords: Quantitative Imaging, Quantitative Imaging

Compressed sensing (CS) relies on random under-sampling measurements and has been the primary technique to accelerate quantitative parametric mapping (QPM). The multiple overlapping-echo detachment (MOLED) technique proposed recently is a novel technique to realize quantitative parametric mapping in a single shot within around 100 milliseconds. However, the two techniques still lack a systematic comparison of acceleration performance in QPM. For the first time, we analyze and compare the performance of these two techniques under the same conditions via numerical experiments. The results show that MOLED outerperforms one-dimensional under-sampling CS acceleration technique in T₂ quantification.

View Presentation Video

Nadège Corbin^1,2, Aurélien J. Trotier¹, Sylvain Miraux¹, and Emeline J. Ribot^1
1Centre de Résonance Magnétique et Systèmes Biologiques UMR5536 CNRS/University of Bordeaux, Bordeaux, France, ²Wellcome Centre for Human Neuroimagin, UCL Queen Square, Institute of Neurology, University College of London, London, United Kingdom

Keywords: Quantitative Imaging, Relaxometry, radial sampling

Quantitative MRI protocols can benefit from radial trajectory sampling to reduce the acquisition time. However we identified a bias in T2 relaxometry studies conducted with radial imaging. After in-depth analysis aiming at further our understanding of the underlying mechanisms, we propose some guidelines to mitigate this bias. The error depends on the range of T2 present in the object of interest and its geometry, the number of spokes and the trajectory of the sequence. Parallel imaging and compressed sensing help reducing the bias.

View Presentation Video

Martin Schilling¹, Moritz Blumenthal², Xiaoqing Wang^3,4, and Martin Uecker^2
1University Medical Center Göttingen, Göttingen, Germany, ²Institute for Biomedical Imaging, TU Graz, Graz, Austria, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Department of Radiology, Harvard Medical School, Boston, MA, United States

Keywords: Quantitative Imaging, Myocardium

Linear subspace-based models are a powerful method for reconstruction in quantitative magnetic resonance imaging (qMRI). Compared to nonlinear model-based reconstructions, they are computationally more efficient and avoid partial volume effects. Both methods have not yet been compared with respect to myocardial T₁ mapping using single-shot inversion-recovery radial FLASH. This work quantitatively compares the two methods for different regularization parameters. Reconstruction quality and T₁ maps of both methods are comparable.

View Presentation Video

Amir Heydari¹, Tae Hyung Kim^2,3,4, Abbas Ahmadi¹, and Berkin Bilgic^3,4
1Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran (Islamic Republic of), ²Department of Computer Engineering, Hongik University, Seoul, Korea, Republic of, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Radiology, Harvard Medical School, Boston, MA, United States

Keywords: Quantitative Imaging, Quantitative Imaging, Image Reconstruction

We introduce a new method for joint T₁ and T₂^*  MR parameter mapping in MAPLE framework which takes advantage of multi-echo multi-flip angle (MEMFA) gradient echo data. It combines joint reconstruction of MEMFA data with a joint relaxation signal model to improve parameter estimation. The proposed method estimates T₁ and T₂* parameters jointly with enough flexibility to incorporate different standard and state-of-the-art reconstruction methods, including zero-shot self-supervised learning (ZS-SSL) reconstruction. It improves the reconstruction performance of the methods with learnable parameters by offering a physics-based additional regularization into their optimization process and exploiting spatio-temporal correlations.

Yawen Liu¹, Pengling Ren², Hongxia Yin³, Yi Zhu⁴, Rong Wei⁵, Linkun Cai¹, Haijun Niu¹, and Zhenchang Wang^1,2
1School of Biological Science and Medical Engineering, Beihang University, Beijing, China, ²Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China, ³Department of Medical Engineering, Beijing Friendship Hospital, Capital Medical University, Beijing, China, ⁴Philips Healthcare, Beijing, China, ⁵Peking university Academy for Advanced Interdisciplinary Studies, Beijing, China

Keywords: Quantitative Imaging, Machine Learning/Artificial Intelligence

Rapid quantitative magnetic resonance imaging (qMRI) is the trend of MR development and has essential diagnostic value. However, reducing acquisition time will come at the expense of image quality and will also affect the accuracy of quantitative values. Here we propose a method for reconstructing fast low-resolution qMRI images using deep learning, aiming to improve image quality while reducing bias in quantitative values. The research results show that after deep learning, the image quality is comparable to that of conventional high-resolution scanning images, and quantitative values are also more stable.

View Presentation Video

Sebastian Flassbeck¹, Andrew Mao¹, and Jakob Assländer^1
1Dept. of Radiology, NYU Langone, New York, NY, United States

Keywords: Quantitative Imaging, CEST & MT, MR Fingerprinting, qMT, Low field, B0-field dependance

We demonstrate an approach to quantify unconstrained MT parameter mapping and utilized this approach to study the magnetic field dependence of the relaxation and MT parameters. The largest field dependence was observed in the longitudinal relaxation rate of the semi-solid spin pool.

View Presentation Video

Tristhal Parasram¹ and Dan Xiao^1
1University of Windsor, Windsor, ON, Canada

Keywords: Quantitative Imaging, Relaxometry

Quantitative analysis of T₁ spectra could reveal microscopic properties and have been used to study biological tissues such as the heart, brain and related disease. It is challenging to determine the relaxation times from magnetic resonance signals particularly with multicomponent continuous spectra as it is an inherently ill-posed exponential analysis problem even with a large number of input data points. Artificial neural networks have been trained to determine T₁ spectra with 8 to 4 logarithmically spaced data points. Improved performance over a larger range of T₁ values and faster processing time compared to traditional methods have been achieved.

View Presentation Video

Zaheer Abbas¹, Markus Zimmermann¹, Dominik Ridder¹, Ana-Maria Oros-Peusquens¹, and N. Jon Shah^1,2,3,4
1Institute of Neuroscience and Medicine 4, Jülich, Germany, ²Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany, ³JARA - BRAIN - Translational Medicine, Aachen, Germany, ⁴Department of Neurology, RWTH Aachen University, Aachen, Aachen, Germany

Keywords: Quantitative Imaging, Brain

Measurement of quantitative, tissue-specific MR properties such as water content or relaxation times using quantitative-MRI at clinical field strength (1.5T and 3T) is a well-explored topic. Established methods for water content mapping are based on the variable flip-angle (VFA) approach. However, transition to ultrahigh field strength remains challenging due to significantly increased RF field inhomogeneity. Here, we demonstrate a novel VFA method to acquire quantitative water content and relaxation times at 7T with full brain coverage and 1x1x1.5 mm resolution within 7 min. Accuracy and precision of the parametric maps is demonstrated by comparison to already reported results.

View Presentation Video

Neil Wilson¹, Sophia Swago², Mark Elliott¹, Ravi Prakash Reddy Nanga¹, Ravinder Reddy¹, and Walter Witschey^1
1Radiology, University of Pennsylvania, Philadelphia, PA, United States, ²Bioengineering, University of Pennsylvania, Philadelphia, PA, United States

Keywords: Quantitative Imaging, Spectroscopy

Here we present a method based on spectrally selective saturation recovery 1H spectroscopy to measure the apparent T1 values of NAD+ in the downfield at 7T for the first time. With spectrally selective saturation and excitation, NAD+ exhibits enhanced T1 recovery do to cross relaxation with water, and we report values in three subjects between 200 and 300 ms. Measuring apparent T1 is crucial as a correction factor for quantitative spectroscopy of NAD+. 

View Presentation Video

Maik Rothe¹, Selina Riedel¹, Andreas Deistung¹, Brill Richard¹, Walter Alexander Wohlgemuth¹, and Alexander Gussew^1
1University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Halle (Saale), Germany

Keywords: Quantitative Imaging, Relaxometry

In this study, we used a method for fast in vivo T₁ and T₂^* mapping of fast relaxing tissues of the knee using a high resolution 3D spoiled gradient ultrashort echo time research application sequence with spiral read out to quantitatively describe different tissues. We manually segmented 10 different tissues in five healthy young volunteers and found good agreement with literature values for fast relaxing tissues. The bivariate histograms showed tissue specific clusters, which can be used for tissue differentiation. Additional information, such as phase images, could further improve bivariate histogram-based tissue differentiation and should be explored in future studies.