1. Estimation of Dual-Input Blood Volumes Using Dynamic Contrast-Enhanced MRI
Michael H. Rosenthal, MD, PhD
Hiroto Hatabu, MD, PhD
Francine Jacobson, MD, MPH

2. Traditional Perfusion Analysis
• Exponential kinetic models
• Homogeneous compartments and voxels
• Limited emphasis on dual-input vascular supplies
Challenges
Subvoxel characterization
Measurement and modeling of dual inflows
Temporal resolution

3. Composite Voxels
Consider tissue voxels as a mixture of reference vascular signals:
Estimate weighting factors αi using least squares
Subtracted signal used to isolate enhancement

4. Monte Carlo Simulation
Numerical phantom
Six vascular compartments
Eleven ‘mixed’ targets
Gaussian white noise to test SNR from 0.1 to 5.0
ROIs from 1 to 900 pixels
Sampling from 1/s to 0.1/s
206,250 iterations

5. Results Standard error in vessel contents ≤ 3%:
SNR ≥ 0.2 and ROI ≥ 100 pixels at ≥ 0.1 Hz
SNR ≥ 1 and ROI ≥ 25 pixels at ≥ 0.1 Hz
Standard error in vessel contents ≤ 1%:
SNR ≥ 1 and ROI ≥ 81 pixels at ≥ 0.1 Hz

6. Clinical DCE-MRI Example

7. Characterization of Pulmonary Tissues in Clinical DCE-MRI Cases
Tissue of Interest
Pulmonary Arterial Fraction Volume
Systemic Arterial Fraction Volume
Pleural Mesothelioma
0.04
0.29
Normal Lung
0.11
0.02
Chronic Atelectasis
0.06
0.17

8. Conclusions
• Viewing voxels as mixtures of reference signals allows subvoxel estimation in a simple numerical phantom
• Early anecdotal promise in clinical applications
• Prospective clinical evaluation in progress

9. This work was supported in part by RSNA Research and Education Foundation Resident Research Grant RR0826.
Questions?