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SU‐F‐R‐29: The Influence of Four Dimensional Diffusion‐Weighted MRI (4D‐DWI) On Feature Analysis of Time‐Resolved Apparent Diffusion Coefficient (ADC) Measurement: Initial Evaluation
Author(s) -
Liu Y,
Yin F,
Cai J
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4955801
Subject(s) - effective diffusion coefficient , nuclear medicine , imaging phantom , diffusion mri , kurtosis , magnetic resonance imaging , nuclear magnetic resonance , medicine , physics , mathematics , radiology , statistics
Purpose: Diffusion‐weighted imaging (DWI) has been shown to have superior tumor‐to‐tissue contrast for cancer detection compared to other MRI sequences and CT. This study aims at developing and evaluating the effects of the four dimensional DWI (4D‐DWI) technique on the feature analysis of time‐resolved Apparent Diffusion Coefficient (ADC) measurement. Methods: Image acquisition was performed by repeatedly imaging a volume of interest using a multi‐slice single‐shot 2D‐DWI sequence in the axial planes. Each 2D‐DWI image was acquired sequentially in the x, y, and z‐diffusion‐directions (b=500s/mm2). Respiratory motion was simultaneously recorded using bellows. Retrospective sorting was conducted to reconstruct 4D‐DWI. As a comparison, free breathing DWI (FB‐DWI) was also reconstructed using the same dataset. Subsequently, ADC maps were measured for 4D‐DWI and FB‐DWI data. The technique was implemented on a digital human phantom (XCAT). It was programmed to simulate regular motion. Motion trajectories of tumor were extracted from 4D‐DWI and compared with average input breathing curve. The mean amplitude difference(D) was calculated. To quantitatively analyze the effect of 4D‐DWI on time‐resolved ADC maps, feature analysis was conducted on tumor region for the time‐resolved ADC maps and the free breathing ADC maps. 3D XCAT images were served as the reference. The following features were calculated: mean tumor ADC value, entropy, energy, kurtosis, skewness, homogeneity, sphericity, tumor surface area and tumor contrast. Results: 4D‐DWI of XCAT digital phantom demonstrated the respiratory motion clearly. The values of D were 1.9mm, 1.7mm and 2.0mm, respectively. The feature analysis shows that the texture of tumor demonstrated on time‐resolved ADC maps was significantly closer to the reference than the free breathing ADC maps. Conclusion: The influence of 4D‐DWI technique on feature analysis of time‐resolved ADC measurement was initially evaluated on a digital human phantom. Comparing to free breathing DWI, 4D‐DWI can lead to more accurate measurement of ADC. NIH (1R21CA165384‐01A1)