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Fast nonlinear regression method for CT brain perfusion analysis.

Edwin Bennink¹, Jaap Oosterbroek¹, Kohsuke Kudo²

¹University Medical Center Utrecht, Department of Radiology, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands; University Medical Center Utrecht, Image Sciences Institute, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands.

Journal of Medical Imaging (Bellingham, Wash.)

|July 15, 2016

Summary

A new nonlinear regression method (boxNLR) offers a faster, more robust approach to analyzing computed tomography perfusion (CTP) scans for ischemic stroke diagnosis and prognosis compared to existing techniques.

Keywords:

CT perfusion brain deconvolution model nonlinear regression stroke

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Area of Science:

Medical Imaging
Neurology
Biomedical Engineering

Background:

Computed tomography perfusion (CTP) imaging is crucial for diagnosing and determining the prognosis of ischemic stroke.
Current CTP analysis methods, including block-circulant singular value decomposition (bSVD), have limitations impacting accuracy and reliability.

Purpose of the Study:

To introduce and evaluate a novel fast nonlinear regression method with a box-shaped model (boxNLR) for CTP analysis.
To compare the performance of boxNLR against the state-of-the-art bSVD method and a commercial SVD-based method.

Main Methods:

Quantitative evaluation using a digital perfusion phantom (Kudo et al.).
Qualitative evaluation using 50 clinical CTP scans.
Comparison of boxNLR with bSVD and a commercial SVD-based method.

Main Results:

All methods showed high correlation with ground truth in the phantom.
boxNLR demonstrated improved robustness to noise, attenuation curve truncation, and tracer delay.
Perfusion maps generated by boxNLR showed higher correlation with bSVD than those from the commercial method in clinical scans.

Conclusions:

The boxNLR method provides a fast, reliable, and robust approach for estimating perfusion parameters from CTP scans.
boxNLR offers advantages in robustness, extensibility, and unified parameter estimation.
boxNLR presents a viable alternative to existing commercial and academic CTP analysis methods.