This webinar provides an overview of Advanced Acceleration Technology (AAT) in MRI, focusing on compressed sensing, simultaneous multi-slice (SMS), and AI-based techniques. It discusses the underlying science, implementation guidance, image quality considerations, and shares user experiences and case studies from various manufacturers.
Accelerated MRI sequences offer several potential benefits across different groups within a radiology department:
For Patients:
For Radiology Managers:
For Radiologists:
For Radiographers:
The fundamental difference lies in how they achieve acceleration and reconstruct the image:
Traditional Acceleration (e.g., Parallel Imaging): This method works by acquiring fewer lines of k-space (data collected during an MRI scan) than a fully sampled scan. To reconstruct the image, it relies on information about the sensitivities of the different receive coil elements used in the scanner. While it speeds up acquisition, it typically leads to a compromise in signal-to-noise ratio (SNR) or spatial resolution.
Compressed Sensing (CS): CS also involves acquiring a subset of k-space data (sub-sampling). However, instead of relying solely on coil sensitivity information, CS leverages the fact that many MR images are "sparse" in certain mathematical domains (like wavelets). It uses an incoherent sub-sampling pattern in k-space, which, when reconstructed using iterative algorithms, allows for significant denoising and the recovery of image quality. This means CS can achieve higher acceleration factors than traditional methods without the usual penalty in SNR or spatial resolution, and it specifically aims to reduce noise and artifacts introduced by the sub-sampling.
AI-based reconstruction techniques aim to improve MRI scans in several ways:
A key characteristic of their clinical deployment is that the software is not learning in real-time. Once the neural network or AI algorithm is trained by the manufacturer and its parameters are set, this forms the fixed clinical product. The software does not continuously improve or adapt based on the images it processes in the clinic; it performs as it was designed and validated.
AI-based reconstruction techniques aim to improve MRI scans in several ways:
A key characteristic of their clinical deployment is that the software is not learning in real-time. Once the neural network or AI algorithm is trained by the manufacturer and its parameters are set, this forms the fixed clinical product. The software does not continuously improve or adapt based on the images it processes in the clinic; it performs as it was designed and validated.
The implementation of Simultaneous Multi-Slice (SMS) and Compressed Sensing (CS) technologies can present certain challenges and potential artifacts:
Challenges:
Potential Artifacts: