Dynamic contrast-enhanced (DCE)-MRI is becoming an increasingly important tool for evaluating tumor vascularity and assessing the effectiveness of emerging antiangiogenic and antivascular agents. In chest and abdominal regions, however, respiratory motion can seriously degrade the achievable image quality in DCE-MRI studies. The purpose of this work is to develop a respiratory motion-compensated DCE-MRI technique that combines the self-gating properties of radial imaging with the reconstruction flexibility afforded by the golden-angle view-order strategy. Following radial data acquisition, the signal at k-space center is first used to determine the respiratory cycle, and consecutive views during the expiratory phase of each respiratory period (34-55 views, depending on the breathing rate) are grouped into individual segments. Residual intra-segment translation of lesion is subsequently compensated for by an autofocusing technique that optimizes image entropy, while intersegment translation (among different respiratory cycles) is corrected using 3D image correlation. The resulting motion-compensated, undersampled dynamic image series is then processed to reduce image streaking and to enhance the signal-to-noise ratio (SNR) prior to perfusion analysis, using either the k-space-weighted image contrast (KWIC) radial filtering technique or principal component analysis (PCA). The proposed data acquisition scheme also allows for high frame-rate arterial input function (AIF) sampling and free-breathing baseline T1 mapping. The performance of the proposed radial DCE-MRI technique is evaluated in subjects with lung and liver lesions, and results demonstrate that excellent pixelwise perfusion maps can be obtained with the proposed methodology.
- Principal component analysis
- Respiratory motion
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging