Sector-Wise Golden Angle (SWIG) is a novel approach that was developed to address the limitations associated with Golden Angle radial imaging, commonly used for high temporal resolution flow measurements. Golden angle radial imaging is a time-efficient method that effectively reduces motion sensitivity. However, binned or retrospectively gated imaging where multiple heartbeats are utilized to acquire a single time series may lead to uneven coverage of k- space, ultimately resulting in poor image quality. In contrast, SWIG restricts the radial profiles to a sector of k-space per heartbeat, ensuring even distributions of spokes during retrospectively gated acquisitions. One drawback of SWIG is the loss of ability to reconstruct real-time images. The combination of sorted and unsorted acquisition simultaneously holds significant potential and could be applied in various domains. The goal of the thesis work was to design a trajectory that combines radial and spiral k-space sampling, enabling hybrid real-time and retrospectively gated imaging. The objective was to obtain an image series with comparable quality to a SWIG readout while retaining the ability to reconstruct a low-resolution real-time image series from the same data. To evaluate the hybrid trajectory, the numerical phantom XCAT was used to generate synthetic MRI images. Binned images were sampled using a hybrid-SWIG method, yielding similar image quality to a conventional SWIG image series, with the added benefit of being able to reconstruct a low-resolution real-time image series. Although the current method was only evaluated in a numerical phantom and may require additional adjustment to be suitable for a real MRI scanner, the results show that it is possible to combine radial and spiral imaging in a single readout.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-329393 |
| Date | January 2023 |
| Creators | Mineur, Sara |
| Publisher | KTH, Skolan för kemi, bioteknologi och hälsa (CBH) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-CBH-GRU ; 2023:066 |
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