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Updated: Jul 14, 2026

Spatial Measurements of Perfusion, Interstitial Fluid Pressure and Liposomes Accumulation in Solid Tumors
Published on: August 18, 2016
Quantification of perfusion and permeability in breast tumors with a deconvolution-based analysis of second-bolus
S Makkat1, R Luypaert, S Sourbron
1Department of Radiology, Academisch Ziekenhuis Vrije Universiteit Brussel/Medische Beeldvorming en Fysische Wetenschappen (BEFY), Laarbeeklaan 101, 1090 Brussels, Belgium. Smitha.Makkat@vub.ac.be
Purpose:
To test the feasibility of using a second-bolus injection, added to a routine breast MRI examination, to measure regional perfusion and permeability in human breast tumors.
Materials And Methods:
In 30 patients with breast tumors, first a routine whole-breast T1-DCE sequence was applied, and the slice where the lesion enhanced maximally was located. At that slice position, T1-weighted MR images were acquired at 0.3-second resolution using a second-bolus dynamic inversion recovery (IR)-prepared turbo field echo (TFE) sequence. A pixel-by-pixel model-independent deconvolution of the relative signal enhancement was performed to estimate the tumor blood flow (TBF), tumor volume of distribution (TVD), mean transit time (MTT), extraction flow product (EF), and extraction fraction (E). In addition to this pilot study, a first appraisal of its sensitivity to tissue type was made on the basis of a small patient cohort.
Results:
In the malignant tumors, the parametric maps clearly delineated tumors from the breast tissue and enabled visualization of the heterogeneity. The deconvolution analysis provided objective parametric maps of tumor perfusion with a mean TBF (84 +/- 48 mL/100 mL/minute) in malignant tumors in the high range of literature values.
Conclusion:
In terms of these perfusion values, our method appears promising to quantitatively characterize tumor pathophysiology. However, the number of patients was limited, and the separation between malignant and benign groups was not clear-cut. Additional parameters generated through compartment modeling may improve the tumor differentiation.
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