Next Article in Journal
Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps
Previous Article in Journal
Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tomography
Volume 5
Issue 1
10.18383/j.tom.2019.00001
tomography-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements

by
Warren Foltz
1,2,
Brandon Driscoll
1,
Sangjune Laurence Lee
2,
Krishna Nayak
3,
Naren Nallapareddy
3,
Ali Fatemi
2,
Cynthia Ménard
4,6,
Catherine Coolens
1,2,4,6,* and
Caroline Chung
5,7
1
Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON M5G 1Z5, Canada; [email protected]
2
Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
3
Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
4
Department of Radiation Oncology, Centre Hospitalier Universite de Montreal, Montreal, QC, Canada
5
TECHNA Institute, University Health Network, Toronto, ON, Canada
6
Department of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
7
Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 77-89; https://doi.org/10.18383/j.tom.2019.00001
Submission received: 9 December 2018 / Revised: 8 January 2019 / Accepted: 6 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIFMAGN) and phase (AIFPHA) signals, and compared them against computed tomography (CT) (AIFCT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3–7.5 mL/s), and peak bolus concentrations (0.5–10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIFCT via Pearson correlation analysis. AIFMAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIFCT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIFMAGN accuracy; yet, AIFMAGN metrics remained variable and significantly reduced from AIFCT at concentrations above 2.5 mM. AIFPHA performed equivalently within 1 mM to AIFCT across all tested conditions. AIFPHA, but not AIFMAGN, reported equivalent measurements to AIFCT across the range of tested conditions. AIFPHA showed superior robustness.
Keywords: dynamic contrast-enhanced MRI (DCE-MRI); permeability; arterial input function (AIF); quantification; MRI phase; phantom dynamic contrast-enhanced MRI (DCE-MRI); permeability; arterial input function (AIF); quantification; MRI phase; phantom

Share and Cite

MDPI and ACS Style

Foltz, W.; Driscoll, B.; Lee, S.L.; Nayak, K.; Nallapareddy, N.; Fatemi, A.; Ménard, C.; Coolens, C.; Chung, C. Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements. Tomography 2019, 5, 77-89. https://doi.org/10.18383/j.tom.2019.00001

AMA Style

Foltz W, Driscoll B, Lee SL, Nayak K, Nallapareddy N, Fatemi A, Ménard C, Coolens C, Chung C. Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements. Tomography. 2019; 5(1):77-89. https://doi.org/10.18383/j.tom.2019.00001

Chicago/Turabian Style

Foltz, Warren, Brandon Driscoll, Sangjune Laurence Lee, Krishna Nayak, Naren Nallapareddy, Ali Fatemi, Cynthia Ménard, Catherine Coolens, and Caroline Chung. 2019. "Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements" Tomography 5, no. 1: 77-89. https://doi.org/10.18383/j.tom.2019.00001

APA Style

Foltz, W., Driscoll, B., Lee, S. L., Nayak, K., Nallapareddy, N., Fatemi, A., Ménard, C., Coolens, C., & Chung, C. (2019). Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements. Tomography, 5(1), 77-89. https://doi.org/10.18383/j.tom.2019.00001

Article Metrics

Back to TopTop