Abstract
Background Myocardial T1 mapping has been proposed for characterizing a wide range of diffuse and focal fibrotic pathologies. Due to the demands of requiring multiple sample points along the signal relaxation curve in a breath-held ECG-gated scan, current T1 mapping methods have focused on single-shot acquisitions. Collecting all data in a quiescent time window has limited T1 mapping to a reduced number of k-space lines, often with parallel imaging. These compromises lead to low spatial resolution and/or low signal-to-noise (SNR) images. This work explores the initial feasibility of accelerating the recently proposed SMART1Map method[1] using Compressed Sensing with temporal Principle Component Analysis (CS-tPCA)[2] to obtain higher spatial resolution and higher SNR T1 maps.
Highlights
Myocardial T1 mapping has been proposed for characterizing a wide range of diffuse and focal fibrotic pathologies
A phantom consisting of 20 samples with different T1s (200-1600 ms) was imaged using SMART1Map with a matrix size of 256 × 256 over 36 cm, acquiring 6 time samples (TS) of the T1 recovery curve at 100, 315, 1315, 2315, 3315, and 4315 ms
Images were acquired with a single-channel body coil resulting in low SNR data
Summary
Myocardial T1 mapping has been proposed for characterizing a wide range of diffuse and focal fibrotic pathologies. Due to the demands of requiring multiple sample points along the signal relaxation curve in a breath-held ECG-gated scan, current T1 mapping methods have focused on single-shot acquisitions. Collecting all data in a quiescent time window has limited T1 mapping to a reduced number of k-space lines, often with parallel imaging. These compromises lead to low spatial resolution and/or low signal-to-noise (SNR) images. This work explores the initial feasibility of accelerating the recently proposed SMART1Map method[1] using Compressed Sensing with temporal Principle Component Analysis (CS-tPCA)[2] to obtain higher spatial resolution and higher SNR T1 maps
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