1. Advantages of higher resolution by using PIQE
In order to generate high SNR and resolution images from low SNR and resolution images, PIQE employs two deep learning reconstruction (DLR) technologies: denoising and up-sampling. PIQE, like AiCE, can compensate for the reduction in SNR when the resolution is increased. In addition, PIQE has the following features beyond those of AiCE: 1) the matrix can be increased by a factor of 3×3 to improve resolution, 2) contrast can be maintained because the resolution of the original image can be reduced, and 3) the reduction in contrast caused by an excessively high resolution can be minimized because scanning is performed at a lower resolution, thus improving tissue contrast.
Figure 3 shows 1-mm 2D FS-PDWI acquired with PIQE in a patient with a lunate fracture and TFCC injury. Bone marrow edema is seen in the lunate bone, and a sagittal image (a) shows a free bone fragment (orange arrow) on the dorsal aspect of the lunate bone. In a coronal image (b) and a sagittal image (c), fluid (blue arrows) is seen in the articular disk of the TFCC, indicating the presence of injury. The lesion can be observed from both coronal and sagittal directions in 1-mm 2D images.
Figure 4 shows T2-weighted images (T2WI) of a patient with cervical disc bulging and herniation. Compared to the original image (a), structural details are more precisely depicted with PIQE (b). The cervical cord is also clearly visualized, and abnormal findings such as cervical edema and myelomalacia can easily be identified.
Figure 3: 1-mm 2D FS-PDWI with PIQE in a patient with a lunate fracture and TFCC injury
Figure 4: Comparison of images with and without PIQE for evaluating cervical disc bulging and herniation
2. Application of PIQE to reductions in acquisition time
AiCE eliminates noise caused by the decrease in SNR due to increasing the acceleration rate of SPEEDER or CS and reducing the number of acquisitions to achieve reductions in acquisition time. With PIQE, on the other hand, the resolution can be increased at the time of image reconstruction, allowing the acquisition time to be shortened by reducing the matrix in the phase encoding direction during acquisition. This also means that the acceleration rate of SPEEDER or CS can be reduced, resulting in more stable image quality.
This article is a translation of the INNERVISION magazine, Vol.38, No.6, 2023.
Disclaimer
The contents of this report include the personal opinions of the author based on his clinical experience and knowledge. Deep learning technology is used in the design stage of the image reconstruction processing for AiCE and PIQE. The system itself does not have self-learning capabilities.
The content reflects information available at the time of publication and may differ from current information.