Blind Estimation Versus Direct Measurement of the Arterial Input Function in Dynamic Contrast-Enhanced MRI of the Breast

Blind Estimation Versus Direct Measurement of the Arterial Input Function in Dynamic Contrast-Enhanced MRI of the Breast

Článek WoS

Purpose Accurate arterial input functions (AIFs) are essential for quantitative dynamic contrast-enhanced (DCE) MRI, yet direct measurement is challenging and population-averaged AIFs neglect patient-specific variability. Blind deconvolution provides an alternative by estimating patient-specific AIFs directly from tissue data, up to a scale factor. This study compared blindly estimated AIFs with carefully measured aortic AIFs in breast DCE-MRI.Theory and Methods Data from 25 patients with breast cancer were analyzed, each with a carefully measured AIF. Blind AIF estimates were obtained using model-constrained deconvolution in the Perflab toolkit with both Tofts-Kety (TK) and two-compartment exchange (2CXM) tissue models. To help isolate AIF shape blind AIFs were scaled using cardiac output. These blind AIFs were compared with measured AIFs using scale-invariant and scale-dependent metrics which assess the similarity of the AIF shape.Results Blind estimates were obtained for all 25 patients with both tissue models. Compared with measured AIFs, blind estimates derived using the 2CXM showed stronger agreement across all metrics than those derived using the TK model. However, the weak correlation in scale-dependent metrics suggests limitations of cardiac output scaling.Conclusion Blind AIF estimation using the 2CXM provides more reliable recovery of AIF shape and dispersion than the TK model in breast DCE-MRI. While blind deconvolution shows promise for estimating local patient-specific AIFs other scaling strategies may need to be employed in practice.

Purpose Accurate arterial input functions (AIFs) are essential for quantitative dynamic contrast-enhanced (DCE) MRI, yet direct measurement is challenging and population-averaged AIFs neglect patient-specific variability. Blind deconvolution provides an alternative by estimating patient-specific AIFs directly from tissue data, up to a scale factor. This study compared blindly estimated AIFs with carefully measured aortic AIFs in breast DCE-MRI.Theory and Methods Data from 25 patients with breast cancer were analyzed, each with a carefully measured AIF. Blind AIF estimates were obtained using model-constrained deconvolution in the Perflab toolkit with both Tofts-Kety (TK) and two-compartment exchange (2CXM) tissue models. To help isolate AIF shape blind AIFs were scaled using cardiac output. These blind AIFs were compared with measured AIFs using scale-invariant and scale-dependent metrics which assess the similarity of the AIF shape.Results Blind estimates were obtained for all 25 patients with both tissue models. Compared with measured AIFs, blind estimates derived using the 2CXM showed stronger agreement across all metrics than those derived using the TK model. However, the weak correlation in scale-dependent metrics suggests limitations of cardiac output scaling.Conclusion Blind AIF estimation using the 2CXM provides more reliable recovery of AIF shape and dispersion than the TK model in breast DCE-MRI. While blind deconvolution shows promise for estimating local patient-specific AIFs other scaling strategies may need to be employed in practice.

arterial input function, breast cancer, dynamic contrast-enhanced MRI, perfusion

arterial input function, breast cancer, dynamic contrast-enhanced MRI, perfusion

CRAY, J.; VITOUŠ, J.; JIŘÍK, R.; TITARENKO, S.; BUCKLEY, D.

04.07.2026

Wiley

Magnetic Resonance in Medicine

96

October 2026

Spojené státy americké

1521

2036

9

https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.70454