Chrome Extension
WeChat Mini Program
Use on ChatGLM

The influence of diffusion gradient direction on diffusion-weighted imaging of breast mass-like lesions at 3.0T.

Acta radiologica (Stockholm, Sweden : 1987)(2017)

Cited 0|Views2
No score
Abstract
Background It has been challenging to achieve ideal breast diffusion-weighted imaging (DWI). The optimization of diffusion gradient direction is of great importance. Purpose To evaluate the effect of diffusion gradient direction on the apparent diffusion coefficient (ADC) values of breast mass-like lesions and the visual grades of image quality, lesion visibility, and sharpness of breast contour at 3.0T. Material and Methods Sixty consecutive patients with mass-like lesions were enrolled in this study. In addition to typical breast magnetic resonance imaging (MRI) protocols, the breasts were scanned with conventional orthogonal DWI (c-DWI), tetrahedral DWI (t-DWI), and 3in1 DWI (3in1-DWI) sequences. The DW images were observed and visually graded by two radiologists independently. For ADC measurement, one radiographer manually selected the region of interest (ROI). Results For both readers, t-DWI had better image quality and sharpness of breast contour than c-DWI. Regarding lesion visibility, no significant differences were observed among three sequences. The mean ADC values were 1.462 × 10, 1.490 × 10, and 1.446 × 10 mm s for c-DWI, t-DWI, and 3in1-DWI, respectively. The ADC values extracted from both t-DWI and 3in1-DWI were not statistically different compared with those from c-DWI. In all DWI sequences, the ADC of malignant lesions was significantly reduced compared with benign lesions. Conclusion DWI with tetrahedral or 3in1 diffusion gradients is a more useful technique in clinical breast MRI than c-DWI because the image quality and sharpness of breast contour are improved. ADC is comparable to c-DWI.
More
Translated text
Key words
Diffusion gradient,apparent diffusion coefficient (ADC),breast,diffusion-weighted imaging (DWI),tetrahedral
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined