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Medical Image Management: DICOM Images Sharing Process

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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.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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