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Updated: Apr 28, 2026

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
Published on: October 2, 2020
SWIFT MRI enhances detection of breast cancer metastasis to the lung
Naoharu Kobayashi1, Djaudat Idiyatullin, Curt Corum
1Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
Purpose:
To evaluate the capability of longitudinal MR scans using sweep imaging with Fourier transformation (SWIFT) to detect breast cancer metastasis to the lung in mice.
Methods:
Mice with breast cancer metastatic to the lung were generated by tail vein injection of MDA-MB-231-LM2 cells. Thereafter, MR imaging was performed every week using three different pulse sequences: SWIFT [echo time (TE) ∼3 μs], concurrent dephasing and excitation (CODE; TE ∼300 μs), and three-dimensional (3D) gradient echo (GRE; TE = 2.2 ms). Motion during the long SWIFT MR scans was compensated for by rigid-body motion correction. Maximum intensity projection (MIP) images were generated to visualize changes in lung vascular structures during the development and growth of metastases.
Results:
SWIFT MRI was more sensitive to signals from the lung parenchyma than CODE or 3D GRE MRI. Metastatic tumor growth in the lungs induced a progressive increase in intensity of parenchymal signals in SWIFT images. MIP images from SWIFT clearly visualized lung vascular structures and their disruption due to progression of breast cancer metastases in the lung.
Conclusion:
SWIFT MRI's sensitivity to fast-decaying signals and tolerance of magnetic susceptibility enhances its effectiveness at detecting structural changes in lung parenchyma and vasculature due to breast cancer metastases in the lung.
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