A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 197

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 317
Function: require_once

Optimal saturation recovery time for minimizing the underestimation of arterial input function in quantitative cardiac perfusion MRI. | LitMetric

Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Purpose: The purpose of this study was to determine an optimal saturation-recovery time (TS) for minimizing the underestimation of arterial input function (AIF) in quantitative cardiac perfusion MRI without multiple gadolinium injections per subject.

Methods: We scanned 18 subjects (mean age = 59 ± 14 years, 9/9 males/females) to acquire resting perfusion data and 1 additional subject (age = 38 years, male) to obtain stress-rest perfusion data using a 5-fold accelerated pulse sequence with radial k-space sampling and applied k-space weighted image contrast (KWIC) filters on the same k-space data to retrospectively reconstruct five AIF images with effective TS ranging from 10 to 21.2 ms (2.8 ms steps). Undersampled images were reconstructed using a compressed sensing framework with temporal-total-variation and temporal-principal-component as 2 orthogonal sparsifying transforms. The image processing steps included, same motion correction across five different AIF images, signal normalization by the proton-density-weighted-image, signal-to-T conversion using a Bloch equation, T -to-gadolinium-concentration conversion assuming fast water exchange, T * correction to the AIF, and gadolinium-concentration to myocardial blood flow (MBF) conversion based on a Fermi model.

Results: Among five TS values, the shortest TS (10 ms) produced significantly (P < 0.05) higher peak AIF and lower resting MBF (13.73 mM, 0.73 mL g  min ) than 12.8 ms (11.24 mM, 0.89 mL g  min ), 15.6 ms (9.56 mM, 1.05 mL g  min ), 18.4 ms (8.55 mM, 1.17 mL g  min ), and 21.2 ms (7.95 mM, 1.27 mL g  min ). Similarly, shorter TS reduced underestimation of AIF (or overestimation of MBF) for both during stress and at rest, but this effect was canceled in myocardial-perfusion-reserve (MPR).

Conclusion: This study demonstrates that TS of 10 ms reduces the underestimation of AIF and, hence, the overestimation of MBF compared with longer TS values (12.8-21.2 ms).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321550PMC
http://dx.doi.org/10.1002/mrm.29240DOI Listing

Publication Analysis

Top Keywords

time minimizing
8
minimizing underestimation
8
underestimation arterial
8
arterial input
8
input function
8
quantitative cardiac
8
cardiac perfusion
8
perfusion mri
8
perfusion data
8
aif images
8

Similar Publications