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Updated: Jul 27, 2025

Generation of Human 3D Lung Tissue Cultures 3D-LTCs for Disease Modeling
Published on: February 12, 2019
Lung T
Vadim Malis1, Yoshimori Kassai2, Diana Vucevic1
1Department of Radiology, University of California, San Diego, La Jolla, California, USA.
Purpose:
To develop 3D ultrashort-TE (UTE) sequences with tight TE intervals (δTE), allowing for accurate T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping of lungs under free breathing.
Methods:
We have implemented a four-echo UTE sequence with δTE (< 0.5 ms). A Monte-Carlo simulation was performed to identify an optimal number of echoes that would result in a significant improvement in the accuracy of the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ fit within an acceptable scan time. A validation study was conducted on a phantom with known short T 2 * $$ {\mathrm{T}}_2^{\ast } $$ values (< 5 ms). The scanning protocol included a combination of a standard multi-echo UTE with six echoes (2.2-ms intervals) and a new four-echo UTE (TE < 2 ms) with tight TE intervals δTE. The human imaging was performed at 3 T on 6 adult volunteers. T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping was performed with mono-exponential and bi-exponential models.
Results:
The simulation for the proposed 10-echo acquisition predicted over 2-fold improvement in the accuracy of estimating the short T 2 * $$ {\mathrm{T}}_2^{\ast } $$ compared with the regular six-echo acquisition. In the phantom study, the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ was measured up to three times more accurately compared with standard six-echo UTE. In human lungs, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ maps were successfully obtained from 10 echoes, yielding average values T 2 * $$ {\mathrm{T}}_2^{\ast } $$ = 1.62 ± 0.48 ms for mono-exponential and T 2 s * $$ {\mathrm{T}}_{2s}^{\ast } $$ = 1.00 ± 0.53 ms for bi-exponential models.
Conclusion:
A UTE sequence using δTE was implemented and validated on short T 2 * $$ {\mathrm{T}}_2^{\ast } $$ phantoms. The sequence was successfully applied for lung imaging; the bi-exponential signal model fit for human lung imaging may provide valuable insights into the diseased human lungs.
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