Speaker
Description
Magnetic resonance cholangiopancreatography (MRCP) is a non-invasive imaging technique to visualize the hepatobiliary system. However, acquiring MRCP using a triggered 3-D T2-weighted turbo spin echo sequence causes prolonged scan time and often provides undiagnostic image quality. Therefore, we aimed to accelerate MRCP acquisition using deep learning (DL)-based reconstruction.
We acquired conventional two-fold accelerated MRCP on 3T scanners (Siemens Healthineers, Erlangen) as used in clinical routine. Then, we trained a variational network (VN) [1] with two-fold GRAPPA reconstruction as ground truth and retrospective six-fold undersampling as input. We compared our method with parallel imaging [2], compressed sensing [3], and a self-supervised learning method, SSDU (self-supervised learning via data undersampling) [4], designed for situations lacking fully sampled ground truth. We evaluated reconstructions based on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Furthermore, we tested our method with prospective six-fold undersampling to reflect real-world clinical applications and applied this approach to 0.55T MRCP to assess its adaptability.
In summary, our method demonstrated superior performance in reconstructing both prospectively and retrospectively undersampled data, yielding higher metric scores and improved image quality. It also effectively reduced background noise in images acquired at 0.55T without compromising detail.
Reference
1. K. Hammernik et al., "Learning a variational network for reconstruction of accelerated MRI data," MRM, 2018.
2. K. P. Pruessmann et al., "Advances in sensitivity encoding with arbitrary k-space trajectories," MRM, 2001.
3. M. Lustig et al., "Sparse MRI: The application of compressed sensing for rapid MR imaging," MRM, 2007.
4. B. Yaman et al., "Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data," MRM, 2020.
