Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(44)

Solar Industry(8)

User Experience(67)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

Medical Image Management: DICOM Images Sharing Process

By :
5 minutes, 29 seconds read

For modern healthcare organizations, extending better patient care across the service continuum involves new challenges that surround sharing information over a distributed network. Effectively sharing patient information remains a challenge. However, the inability to access these records in a time-sensitive manner results in re-imaging and re-testing the patients. It affects both — ‘time-to-treatment’ and the bottom line. Effective medical image management thus becomes crucial for every digital healthcare enterprise. 

The release process for medical images is altogether complicated — brimming with security related-risks. Images (such as X-Ray Scans, MRI scans, PET scans, etc.) are created and released across several departments and systems while being purposefully kept ‘out-of-reach’ from a host of unauthorized users.

Training & controls on release policies and procedures require ‘health information management’ expertise. It’s because image Handling (electronically) can become susceptible to data corruption, complex accessibility/sharing issues and high-security risks. All of these raise potential red flags for health information management (HIM) professionals.

So how does Medical Image sharing work in this environment? What, if any — are the safeguards surrounding the ‘release’ process?

Medical Image Management: Sharing DICOM Images across healthcare enterprises

Before we go further, let’s delve into the term ‘Medical Imaging’. According to the WHO, the technique embodies different imaging modalities and processes to image the human body (creating visual representations) for diagnostic and treatment purposes. — making it crucial for improving public health initiatives across all population groups.

First, the image is captured using a medical imaging device (routine imaging techniques like ultrasound, MRI, etc.). Then it is necessary to archive and store the images for future use and further processing. Unlike regular images (.png, .jpeg), medical images use DICOM format for storage. DICOM is Digital Imaging and Communication in Medicine standard. The medical practitioner responsible for acquiring and interpreting such medical images is a ‘Radiologist’. And the system they rely on for storing electronic image data is ‘PACS’ (Picture Archiving and Communication System).

If a healthcare organization or an outside consultant (physician, clinician) needs access to an individual patient’s medical images, then the access and retrieval will have to go through PACS. Typically, a Radiologist has authority to control and operate PACS.

Here is a simple process diagram of a medical imaging system —

medical imaging system process diagram

A Typical HIPAA-compliant Medical Imaging Management System places a request (for a specific file) to ‘PACS’ via an intermediary system known as ‘Edge Server’. The sole purpose of the Edge Server is to function as a request-node so that other hospitals or physicians can contact the particular radiologist (who possesses the images stored in PACS) and place a request to access a copy of the file in question.

[Related: Modern Medical Enterprises Absolutely Need Test Automation. Here’s Why.]

Medical image sharing use cases

Critical use cases arise for medical image sharing involving support for:

  • Remote image viewing (out of network)
  • Specialist consults
  • Telehealth (examples such as teleburn, telestroke)
  • Trauma transfers
  • Ambulatory image review

Typically, PACS store digital medical images locally for retrieval. A PACS consists of four major components: 

  1. The imaging modalities such as X-ray plain film (PF), CT and MRI 
  2. a secure network for the transmission of patient information
  3. workstations for interpreting and reviewing images
  4. archives for the storage and retrieval of images and reports. 

To communicate with the PACS server we use DICOM messages that are similar to DICOM image ‘headers”, but with different attributes. The Edge Server manages several functions that allow users to sort through hundreds of thousands of large-volume data and retrieve a specific file from a database either stored in ‘PACS’ or on the ‘MIMS’.

Each of the three highlighted sections (see diagram) can perform various functions, while communication is defined through specific rules and standards that are legally enforced and universally followed.

DICOM medical image sharing via PACS and MIMS

Through the ‘Edge Server’, we can access images stored in PACS. The ‘Management Services’ operation is the first and foremost feature. It means that a user can control & maintain the complete functionality of the server through this. Using ‘Remote Authentication’, users can obtain centralized authorization and authentication to request files from PACS. Please note, Remote Authentication is a networking protocol operating by way of specific ports.

To verify basic DICOM connectivity to the server — i.e, to check if the server is live or not, a C-Echo message is sent to ping the server, after which it will wait for its response. Once identifying the server as live, a user can perform querying and retrieval-based operations. Next, the user can begin the process of requesting DICOM images from the Medical Image Management System — known as ‘Ingestion’. DICOM Ingestion involves pre-assigned IP and port addresses (default ports are 2104-2111).

Basic DICOM Operations

Client: First, it’s important to check the location of the specific image(s) on a particular server. For this, a query-based C-FIND operation sends a request to the server. The user establishes a network connection to the PACS server and prepares a C-FIND request message (which is a list of DICOM attributes). The user then fills in the C-FIND request message with ‘keys’ that match. (E.g. to query for a patient ID, the user fills the patient ID attribute with the patient’s ID.) Then, the C-FIND request message is sent to the server.

Server: The server reverts a list of C-FIND response messages. Each of these messages contain a list of DICOM attributes with values for each match. It then initiates C-MOVE request using the DICOM network protocol to retrieve images from the PACS server. 

One can retrieve images at the Study, Series or Image (instance) level. The C-MOVE request specifies where the retrieved instances should be sent (using separate C-STORE messages). The C-STORE operation, also known as DICOM Push simply pushes (sends) the images to the PACS server (or P2P — Push to PACS). 

C-STORE message implements the DICOM storage service. The SCU sends a C-STORE-RQ (request) message to the server, which includes the actual dataset to transfer. The server answers by returning a C-STORE-RSP (response) message to the user, communicating success or failure of the storage request.

DICOM Images Benefits

Using DICOM images, health management professionals, physicians, and radiologists can utilize secure protocols in handling confidential medical image data. It extends the ability to view such images discreetly and instantly; avoiding duplication costs; and reducing unnecessary radiation exposure to patients.

Medical Image Sharing furthers the “Health 2.0” initiative by being able to instantly and electronically exchange medical information between physicians, as well as with patients — improving communication within the industry.

[Related: How AI is innovating healthcare sector?]

About the author: Rijin Raj is a Senior Software Engineer-QA at Mantra Labs, Bangalore. He is a seasoned tester and backbone of the organization with non-compromising attention to details.

Related:

DICOM FAQs

What is the DICOM Image format?

DICOM stands for — Digital Imaging and Communication. It is a medical standard for sharing a patient’s MRI, X-ray, and other image files over the internet.

How are DICOM Images stored?

Unlike regular images (png, jpg, etc.) DICOM is a secure format for storing confidential medical images. Usually, PACS (Picture Archiving and Communication System) and MIMS (Medical Image Management System) are used to store DICOM Images.

What is DICOM used for?

DICOM is used for securely storing and retrieving confidential images in distributed networks (internet).

Why is DICOM important?

Using DICOM images, health management professionals, physicians, and radiologists can securely handle confidential medical image data.

Cancel

Knowledge thats worth delivered in your inbox

Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

By :

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot