AI Article Synopsis
- The study aimed to evaluate how effective deep learning (DL) image reconstruction is compared to traditional parallel imaging (PI) in head and neck diffusion-weighted imaging (DWI).
- Researchers analyzed data from 41 patients, focusing on qualitative aspects like image quality and artifacts, as well as quantitative metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).
- Results showed that DL-based reconstructions significantly outperformed PI-based reconstructions in both qualitative and quantitative assessments, indicating DL's potential to improve image quality in head and neck imaging.
Article Abstract
Objectives: To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI).
Materials And Methods: We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle.
Results: Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001).
Discussion: DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
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| http://dx.doi.org/10.1007/s10334-023-01129-4 | DOI Listing |
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