ArticleJournal of magnetic resonance imaging : JMRI2023
A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Characterizing chronic obstructive pulmonary disease using quantitative MRI biomarkers.The British journal of radiology · 2026Review
- Benchmarking Hybrid CNN-Transformer Versus Pure Transformer Architectures for Accelerated HyperpolarizedJournal of magnetic resonance imaging : JMRI · 2026Article
- Magnetic Resonance Imaging in the Clinical Evaluation of Lung Disorders: Current Status and Future Prospects.Journal of magnetic resonance imaging : JMRI · 2025Review
- Functional Pulmonary Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence.Academic radiology · 2025Article
- Comparative Analysis of Foundational, Advanced, and Traditional Deep Learning Models for Hyperpolarized Gas MRI Lung Segmentation: Robust Performance in Data-Constrained Scenarios.Bioengineering (Basel, Switzerland) · 2025Article
- Robust Segmentation of Lung Proton and Hyperpolarized Gas MRI with Vision Transformers and CNNs: A Comparative Analysis of Performance Under Artificial Noise.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Combining neural networks and image synthesis to enable automatic thoracic cavity segmentation of hyperpolarizedMagnetic resonance imaging · 2023Article
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundHyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton ( PURPOSE: We hypothesized that a DL-based dual-channel approach, leveraging both STUDY TYPE: Retrospective. POPULATION: A total of 480 corresponding FIELD STRENGTH/SEQUENCE: 1.5-T, three-dimensional (3D) spoiled gradient-recalled ASSESSMENT: We developed a multimodal DL approach, integrating STATISTICAL TESTS: Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland-Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant.
resultsThe dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867-0.978), 1.68 mm (37.0-0.778), and 0.066 (0.246-0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). DATA
conclusionOur dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.