Dynamic Contrast-Enhanced Imaging

Dynamic contrast-enhanced (DCE) MRI is a valuable albeit still evolving technique for mapping the spatial distribution of vascular parameters such as perfusion, permeability, transit time, and blood volume. It employs serial T1 weighted imaging, during a bolus injection of a Gadolinium-based contrast agent. Changes in signal intensity are mapped to changes in contrast agent concentration then regressed to quantify physiological parameters related to vascular permeability (Ktrans, Kep) and cellular compartment volumes, including the fractional plasma volume and the extravascular-extracellular volume fraction. DCE is a promising tool for tumor assessment as it enables quantification of vascular and cellular irregularities. Histologically, abnormal blood-brain barrier permeability is associated with tumor progression; this is observed with DCE imaging, where Ktrans correlates with tumor grade. It has been used for monitoring therapy response and holds vast potential for drug trials. Despite unequivocal benefits, clinical and research adoption has been limited. This is due, in part, to suboptimal image acquisition. Acquisition of imaging data for DCE is challenging since a new image volume must be obtained every 1-30 s to detect signal intensity changes resulting from diffusion of the agent from the intravascular space to the extravascular-extracellular space. As a result, spatial resolution and volume coverage are severely restricted. Our research has been focusing on accelerating DCE imaging acquisition, using techniques such as parallel imaging and compressed sensing.

The video below shows the endothelial permeability (K^trans) of a Multiple sclerosis patient, using a highly accelerated protocol (36x). The voxel volume is 1.6 mm³, slice converge is 119 cm, and temporal resolution is 4.1 s. The corresponding parameters for the current clinical protocol are 7.2 mm³, 3.6 cm and 5.0 s respectively.

Selected References:

1. Y Bliesener, J Acharya, KS Nayak. Efficient DCE-MRI parameter and uncertainty estimation using a Neural Network. IEEE Transactions on Medical Imaging. in press.
2. SG Lingala, Y Guo, Y Bliesener, Y Zhu, RM Lebel, M Law, KS Nayak. Tracer kinetic models as temporal constraints during brain tumor DCE-MRI reconstruction. Medical Physics. in press.
3. Y Bliesener, SG Lingala, JP Haldar, KS Nayak. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magnetic Resonance in Medicine. Early View. PDF JRNL
4. Y Guo, SG Lingala, Y Bliesener, RM Lebel, Y Zhu, KS Nayak. Joint arterial input function and tracker kinetic parameter estimation from under-sampled DCE-MRI using a model consistency constraint. Magnetic Resonance in Medicine. 79(5):2804-2815. May 2018. PDF JRNL GitHub
5. Y Guo, SG Lingala, Y Zhu, RM Lebel, KS Nayak. Direct Estimation of Tracer-Kinetic Parameter Maps from Highly Undersampled Brain DCE-MRI. Magnetic Resonance in Medicine. 78(4):1566-1578. October 2017. PDF JRNL GitHub
6. Y Guo, RM Lebel, Y Zhu, SG Lingala, MS Shiroishi, M Law, KS Nayak. High-resolution Whole-brain DCE-MRI Using Constrained Reconstruction: Prospective Clinical Evaluation in Brain Tumor Patients. Medical Physics 43:2013. April 2016.. PDF JRNL

Collaborators:

• Jay Acharya, MD
  
• Meng Law, MD
  
• Thomas Chen, MD, PhD
  
• Eric Chang, MD
  
• Gino In, MD