Data · dataset · 2026
HAO-Former: A General Multi-Joint Musculoskeletal MRI Intelligent Denoising and Microstructure-Preserving Enhancement Platform (v1.0)
Listed in ScienceDB
Description
Musculoskeletal magnetic resonance imaging (MSK-MRI) often suffers from pronounced stochastic thermal noise during rapid routine scans, while conventional deep-learning denoising networks frequently introduce over-smoothing ("wax-like" artifacts), eroding crucial microstructures such as trabecular bone networks, articular cartilage interfaces, and tendon fascicles.HAO-Former is a specialized, open-source medical software platform designed for intelligent denoising and diagnostic image enhancement across multi-joint MSK-MRI examinations.
Powered by a Restormer backbone optimized via a composite hybrid loss (SSIM, L1, and Sobel gradient) through knowledge distillation, the platform integrates an adaptive high-frequency detail booster alongside multiplicative gain intensity alignment. This architecture delivers 2- to 3-fold objective gains in signal-to-noise ratio (Auto-SNR) and contrast-to-noise ratio (Auto-CNR) while rigorously preserving microstructural integrity and preventing signal dropout holes.Key Technical Features:Multi-Format Ingestion: Seamless batch parsing of volumetric NIfTI formats (.nii.gz) and standard multi-frame clinical DICOM archive packages (.zip) with complete spatial metadata inheritance.Automatic Sequence Routing & Zero-Shot Capability: Automated sequence identification and dynamic weight allocation for PD-FS and T1WI protocols, with verified zero-shot generalizability on non-finetuned T2-FS acquisitions.Non-Destructive Physical Calibration: Robust quantile mapping and morphological mask-guided multiplicative gain alignment, eliminating global baseline shift and negative-clamping boundary artifacts.Integrated ITHP Quality Benchmarking: Built-in Intra-Tissue Homogeneous Patch (ITHP) estimation engine providing slice-level automated quantification of Auto-SNR, Auto-CNR, Diff-PSNR, Removed Noise (%), and Diff-SSIM.Clinical PACS Storage Integration: Native implementation of standard DICOM network services (pynetdicom), enabling C-ECHO ping verification and C-STORE direct transmission to institutional PACS workstations.Cross-Platform Responsive GUI: Lightweight Streamlit-based interface featuring real-time paired slice visualization, multi-language support (English, Chinese, Japanese), and single-click compressed packaging.
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Note
The uploaded data is a packaged Streamlit program, and none of the uploaded data contains any sensitive information such as patient data.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.012dj ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Image 75% · Imaging 75% · Magnetic resonance imaging 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.012dj | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:imaging | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:mri | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |