scholarly journals Metabolic and Vascular Features of Dynamic Contrast-enhanced Breast Magnetic Resonance Imaging and 15O-Water Positron Emission Tomography Blood Flow in Breast Cancer

2008 ◽  
Vol 15 (10) ◽  
pp. 1246-1254 ◽  
Author(s):  
Peter R. Eby ◽  
Savannah C. Partridge ◽  
Steven W. White ◽  
Robert K. Doot ◽  
Lisa K. Dunnwald ◽  
...  
2021 ◽  
Vol 1 ◽  
Author(s):  
David E. Frankhouser ◽  
Eric Dietze ◽  
Ashish Mahabal ◽  
Victoria L. Seewaldt

Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.


2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jacob U. Fluckiger ◽  
Xia Li ◽  
Jennifer G. Whisenant ◽  
Todd E. Peterson ◽  
John C. Gore ◽  
...  

We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies.


Sign in / Sign up

Export Citation Format

Share Document