Improving the Discrimination of Benign and Malignant Breast MRI Lesions Using the Apparent Diffusion Coefficient

This paper presents an investigation of the apparent diffusion coefficient (ADC) for improving the discrimination of benign and malignant lesions in breast magnetic resonance imaging (MRI). In particular a method is presented for automatically selecting hyper intense tumour voxels in dynamic contrast enhanced (DCE) MRI data and evaluating their average ADC in the corresponding diffusion-weighted (DW) MRI data. The method was applied to ten breast MRI datasets obtained from routine clinical practice. The results demonstrate that the combination of the relative signal increase (DCE-MRI) with the apparent diffusion coefficient (DW-MRI) leads to better discrimination than with either feature alone. The results also suggest that it is important to acquire the DWMRI data in a consistent fashion, i.e. either before or after the acquisition of the DCE-MRI data.

[1]  Wendy B DeMartini,et al.  Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. , 2009, AJR. American journal of roentgenology.

[2]  Alan Jackson,et al.  Dynamic contrast-enhanced magnetic resonance imaging in oncology , 2005 .

[3]  Fernanda Philadelpho Arantes Pereira,et al.  Assessment of breast lesions with diffusion-weighted MRI: comparing the use of different b values. , 2009, AJR. American journal of roentgenology.

[4]  A. Cilotti,et al.  Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion , 2007, European Radiology.

[5]  J. Tsuruda,et al.  Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. , 1990, Radiology.

[6]  Usha Sinha,et al.  Functional magnetic resonance of human breast tumors: diffusion and perfusion imaging. , 2002, Annals of the New York Academy of Sciences.

[7]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[8]  W. Marsden I and J , 2012 .

[9]  Alan Coulthard,et al.  BREAST MRI IN PRACTICE , 2002 .

[10]  Thoralf Niendorf,et al.  Highly parallel volumetric imaging with a 32‐element RF coil array , 2004, Magnetic resonance in medicine.

[11]  M. Van Cauteren,et al.  Effect of Intravenous Gadolinium-DTPA on Diffusion-Weighted Images: Evaluation of Normal Brain and Infarcts , 2002, Stroke.

[12]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[13]  M. Giger,et al.  Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. , 2006, Medical physics.

[14]  Yoshito Tsushima,et al.  Magnetic resonance (MR) differential diagnosis of breast tumors using apparent diffusion coefficient (ADC) on 1.5‐T , 2009, Journal of magnetic resonance imaging : JMRI.

[15]  Rachel Brem,et al.  Diffusion imaging of human breast , 1997, NMR in biomedicine.

[16]  A Heinig,et al.  Contrast-enhanced MRI of the breast: accuracy, value, controversies, solutions. , 1997, European journal of radiology.

[17]  M V Knopp,et al.  Dynamic contrast-enhanced magnetic resonance imaging in oncology. , 2001, Topics in magnetic resonance imaging : TMRI.

[18]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[19]  Thierry Metens,et al.  Quantitative diffusion imaging in breast cancer: A clinical prospective study , 2006, Journal of magnetic resonance imaging : JMRI.

[20]  Usha Sinha,et al.  Functional Magnetic Resonance of Human Breast Tumors , 2002 .