Related Experiment Video
Updated: Jan 11, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Quantitative assessment of benign and malignant bone tumours using synthetic magnetic resonance imaging and diffusion
Haitian Liu1, Yinghua Zhang1, Xiaohui Ma1
1Department of Medical Imaging, HEBEI MEDICAL UNIVERSITY THIRD HOSPITAL, Shijiazhuang, Hebei Province, China.
Abstract:
Our study aimed to evaluate the feasibility of quantitative magnetic resonance imaging (MRI) for characterising benign and malignant bone tumours. We analysed forty-seven patients with bone tumours who underwent Synthetic MRI with magnetic resonance image complication (MAGIC) and intravoxel incoherent motion (IVIM) sequence imaging performed at 3 T MRI. Based on different pathological results, the patients were divided into benign and malignant bone tumour groups. Two radiologists performed statistical analysis to analyse whether the parameters T1 relaxation time (T1), T2 relaxation time (T2), proton density relaxation time from MAGIC and diffusion-coefficient (D), perfusion-coefficient (D*), perfusion fraction (f) from IVIM to differentiate malignant and benign bone tumours effectively. T1 and D for malignant tumours were significantly lower than benign bone tumours. The combined T1 and D parameters had the best performance for differentiating malignant from benign bone tumours with receiver operating characteristic curves of 0.860. The MAGIC and IVIM techniques can provide quantitative parameters for identifying benign and malignant bone tumours, with the parameters T1 and D demonstrating high diagnostic efficiency. This approach may serve as an effective strategy for improving bone tumour differentiation.
More Related Videos
12:23Multi-modal Imaging of Angiogenesis in a Nude Rat Model of Breast Cancer Bone Metastasis Using Magnetic Resonance Imaging, Volumetric Computed Tomography and Ultrasound
Published on: August 14, 2012
09:30Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
Published on: December 18, 2016