Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI.

Matthew R Orton¹, James A d'Arcy, Simon Walker-Samuel

¹Cancer Research UK Clinical MR Research Group, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT, UK.

Physics in Medicine and Biology

|February 26, 2008

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Apparent diffusion coefficient and kurtosis parameters show early response to anti-angiogenic therapy in patients with colorectal liver metastases.

Cancer imaging : the official publication of the International Cancer Imaging Society·2026

Same author

Acoustofluidic monitoring of stimuli-responsive microgels for drug delivery.

Ultrasonics·2026

Same author

Efficacy of continuous UV-C<sub>222</sub> exposure of <i>Candida auris,</i> methicillin-resistant <i>Staphylococcus aureus,</i> and T1 bacteriophage at two soil levels in hospital patient rooms.

Infection control and hospital epidemiology·2026

Same author

Characterizing the Diffusion Properties of Prostate Tissue Using Paired MR Microscopy and Multidimensional Diffusion MRI.

Magnetic resonance in medicine·2026

Same author

Genome-wide classification of tumor-derived reads from bulk long-read sequencing.

bioRxiv : the preprint server for biology·2026

Same author

Non-destructive surface acoustic wave sensing for real-time material characterisation of GelMA hydrogels.

Biosensors & bioelectronics·2026

Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026

Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026

Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026

Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·2026

Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026

Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026

See all related articles

Accurate vascular input function modeling is crucial for dynamic contrast-enhanced MRI (DCE-MRI) parameter estimation. The cosine model offers the most accurate and efficient method for DCE-MRI analysis, especially with plasma fractions.

Area of Science:

Medical Imaging
Biophysics
Pharmacokinetics

Background:

Accurate tissue kinetic parameter estimation in dynamic contrast-enhanced MRI (DCE-MRI) relies on a precise description of the vascular input function.
Existing methods for modeling the vascular input function can be computationally intensive, potentially limiting their clinical applicability.

Purpose of the Study:

To introduce a general modeling framework for compact functional forms of vascular input functions in DCE-MRI.
To develop and evaluate realistic models that allow for analytical calculation of tissue concentration curves, enhancing computational efficiency.
To compare the performance and accuracy of different vascular input function models, including exponential, gamma-variate, and cosine functions.

Main Methods:

Development of a general modeling framework for vascular input functions.

Related Experiment Videos

Implementation and simulation of three specific models: exponential, gamma-variate, and cosine.

Investigation of model performance under varying plasma fraction conditions and comparison with a fast-Fourier-transform-based numerical approach.

Main Results:

The cosine model demonstrated minimal bias (less than 5%) in parameter estimation, even with larger plasma fractions, and was indistinguishable from the data-generating model.
Exponential and gamma-variate models showed significant bias (up to 50% and 10% respectively) with increasing plasma fractions.
Analytic calculation methods were 4-10 times faster than numerical convolution approaches, significantly improving computational efficiency for parameter estimation.

Conclusions:

The proposed modeling framework provides efficient and accurate vascular input function descriptions for DCE-MRI.
The cosine model is recommended for its robustness and accuracy, particularly in the presence of plasma fractions.
The computational efficiency of analytic methods makes them highly suitable for clinical application of DCE-MRI parameter estimation.