ArticleQuantitative imaging in medicine and surgery2026
Impact of deep learning-based MRI reconstruction on image quality and apparent diffusion coefficient measurements in patients with intracranial tumors: a prospective intra-individual study.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Deep learning-based magnetic resonance imaging (MRI) reconstruction has shown promise in improving image quality and reducing scan time. However, its impact on diffusion-weighted imaging (DWI) and quantitative parameters such as apparent diffusion coefficient (ADC) in patients with intracranial tumors remains unclear. This study aims to investigate the impact of deep learning-based MRI reconstruction on the quality of diffusion-weighted images and the measurements of lesion ADC in patients with intracranial tumors. Additionally, it assessed the potential influence of this reconstruction method on the consistency of ADC-based quantitative analysis. Methods: This single-center, prospective intra-individual study involved 58 consecutive adults diagnosed with intracranial space-occupying lesions through imaging and/or pathology. These patients underwent brain MRI between January and May 2025, and all received standard single-b-value DWI (b=1,000 s/mm Results: Compared with conventional reconstruction, DLR significantly reduced background SI (29.40±5.05 Conclusions: Under a conventional single-b-value DWI protocol (b=1,000 s/mm
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