Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Yannick Bliesener; Sajan G Lingala; Justin P Haldar; Krishna S Nayak

doi:10.1002/mrm.28024

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Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Journal article

Open access

Peer reviewed

Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects

Yannick Bliesener, Sajan G Lingala, Justin P Haldar and Krishna S Nayak

Magnetic resonance in medicine, Vol.83(5), pp.1625-1639

05/2020

DOI: 10.1002/mrm.28024

PMCID: PMC6982604

PMID: 31605556

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Abstract

To evaluate the impact of (k,t) data sampling on the variance of tracer-kinetic parameter (TK) estimation in high-resolution whole-brain dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Three anatomically and physiologically realistic brain-tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone-based, lattice, pseudo-random, and pseudo-radial; with 50-time frames and 4-fold to 25-fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image-time-series reconstruction followed by model fitting), and direct estimation from the under-sampled data. We evaluated methods based on the Cramér-Rao bound and Monte-Carlo simulations, over the range of signal-to-noise ratio (SNR) seen in clinical brain DCE-MRI. Lattice-based sampling provided the lowest SDs, followed by pseudo-random, pseudo-radial, and zone-based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo-random sampling resulted in 19% higher averaged SD compared to lattice-based sampling. Zone-based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice-based and pseudo-random sampling up to undersampling factors of 25. Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice-based and pseudo-random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25-fold undersampling.

Algorithms

Brain Neoplasms

Contrast Media

Humans

Image Processing, Computer-Assisted

Magnetic Resonance Imaging

Details

Title: Subtitle: Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects
Creators: Yannick Bliesener - University of Southern California
Sajan G Lingala - University of Southern California
Justin P Haldar - University of Southern California
Krishna S Nayak - University of Southern California
Resource Type: Journal article
Publication Details: Magnetic resonance in medicine, Vol.83(5), pp.1625-1639
DOI: 10.1002/mrm.28024
PMID: 31605556
PMCID: PMC6982604
NLM abbreviation: Magn Reson Med
ISSN: 0740-3194
eISSN: 1522-2594
Grant note: R33 CA225400 / NCI NIH HHS
Language: English
Date published: 05/2020
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984196996902771

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