Optimization of topological complexity for one-dimensional arterial blood flow models

Area of Science:

• Computational biology
• Cardiovascular physiology

Background:

• Computational models of the cardiovascular system are vital for personalized medicine.
• High numbers of arterial segments in models increase realism but also parameter uncertainty.

Purpose of the Study:

• To present a method for optimizing/reducing arterial segments in one-dimensional blood flow models.
• To preserve key flow and pressure waveform features during model reduction.

Main Methods:

• Quantified preservation of flow features using metrics like average relative error, pulse pressure, and augmentation pressure.
• Compared an optimized network against baseline models (96-artery and patient-specific coronary).
• Evaluated various physiological and pathological states.

Main Results:

• A minimalistic network (excluding limb and cerebral arteries) sufficiently captures aortic root and systemic artery pressure waveforms.
• Key features like pressure augmentation and pulse pressure are well-preserved.
• Discrepancies in carotid and middle cerebral artery flow are minor compared to ultrasound measurement uncertainties.

Conclusions:

• Model input certainty is maximized by optimizing arterial segment number.
• Reduced arterial segment models can accurately represent essential cardiovascular dynamics.
• This approach supports more robust personalized cardiovascular medicine.