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2 posts about DICOM. Every summary links to the original.
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A walkthrough of how a 3D CT model turns a voxel volume into a structured diagnostic report: isotropic resampling, 3D patch tokens, spatial encoding, and language-model reasoning.
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Medical imaging’s real bottleneck is not model architecture but data access. Hospitals, academia, medtech, and pharma all struggle to extract, de-identify, and query imaging data locked inside PACS and vendor archives. The approach proposed is a data lakehouse model on Databricks using a medallion architecture that ingests raw DICOM into Bronze, extracts de-identified metadata into Silver, and curates queryable Delta tables in Gold. This foundation unifies imaging with multi-omics, clinical-trial, and real-world data under a single governance model. The algorithms exist; the missing piece is a governed, queryable, and linkable data layer that makes the model the easy 10% again.
Multi-hospital networks face rising imaging volumes, siloed on-premises PACS, radiologist routing bottlenecks, and storage that must expand for 7 to 10 years of retention. The proposed pattern uses a hub-and-spoke hybrid architecture: local PACS instances preserve LAN-speed reading, while a centralized AWS archive provides cross-facility discovery, disaster recovery, and lifecycle-managed storage. Modalities send DICOM through C-STORE to local VNA servers, which normalize and compress objects; asynchronous replication sends images to Amazon S3, while Amazon Aurora PostgreSQL stores metadata and routing information. The AWS deployment spans two Availability Zones, places PACS services behind Network Load Balancers, and includes CloudFront and AWS WAF. Storage guidance distinguishes S3 Intelligent-Tiering, Glacier Instant Retrieval, and Glacier Deep Archive according to access frequency and retention needs.
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