Abstract #0079

Simultaneous T2 and ADC Mapping via MQMOLED MRI for Brain Tumor Differentiation

Jianfeng Bao¹, Zongye Li¹, Qinqin Yang², Xiao Wang³, Yuchuan Zhuang⁴, Yanbo Dong⁵, Liangjie Lin⁶, Andrey Tulupov⁷, Yong Zhang⁸, Shuihui Cai², Zhong Chen², Congbo Cai², and Jingliang Cheng³

¹Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, zhengzhou, China, ²Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China, ³Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China, ⁴Department of Imaging Sciences, University of Rochester Medical Center, ROCHESTER, NY, United States, ⁵Faculty of Teacher Education, Pingdingshan University, Pingdingshan, China, ⁶Philips Healthcare, Beijing, China, ⁷Laboratory of MRT technologies, INTERNATIONAL TOMOGRAPHY CENTER Siberian Branch of Russian Academy of Sciences, Novosibirsk, China, ⁸Functional Magnetic Resonance and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China

Synopsis

Keywords: Tumors (Pre-Treatment), Quantitative Imaging, simultaneous multiparametric quantitative MRI, meningioma, glioma, schwannoma

Motivation: To improve the preoperative differentiation of brain tumors such as meningiomas, gliomas, and schwannomas, crucial for treatment planning and prognosis.

Goal(s): The objective is to assess the efficacy of simultaneous T2 and ADC mapping using a novel MQMOLED MRI technique for distinguishing among these tumors.

Approach: The study utilized the MQMOLED technique on 117 patients to simultaneously capture whole-brain T2 and ADC maps within 110 seconds, followed by a deep learning-enhanced analysis of these images.

Results: Significant differences in T2 and ADC values were noted among the tumor types, with multivariate logistic regression models achieving high diagnostic accuracy, evidenced by superior AUC values.

Impact: The MQMOLED's rapid, reliable tumor differentiation capability could transform preoperative diagnostics, enabling tailored treatment approaches and potentially improving patient outcomes by precisely identifying tumor types, thus guiding more effective therapeutic interventions.

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