Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(151)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

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

What’s Next in Cloud Optimization? Can We Optimize Costs Without Sacrificing Performance?

Not too long ago, storing data meant dedicating an entire room to massive CPUs. Then came the era of personal computers, followed by external hard drives and USB sticks. Now, storage has become practically invisible, floating somewhere between data centers and, well, the clouds—probably the ones in the sky. Cloud computing continues to evolve, As cloud computing evolves, optimizing costs without sacrificing performance has become a real concern.  How can organizations truly future-proof their cloud strategy while reducing costs? Let’s explore new-age cloud optimization strategies in 2025 designed for maximum performance and cost efficiency.

Smarter Cloud Strategies: Cutting Costs While Boosting Performance

1. AI-Driven Cost Prediction and Auto-Optimization

When AI is doing everything else, why not let it take charge of cloud cost optimization too? Predictive analytics powered by AI can analyze usage trends and automatically scale resources before traffic spikes, preventing unnecessary over-provisioning. Cloud optimization tools like AWS Compute Optimizer and Google’s Active Assist are early versions of this trend.

  • How it Works: AI tools analyze real-time workload data and predict future cloud resource needs, automating provisioning and scaling decisions to minimize waste while maintaining performance.
  • Use case: Netflix optimizes cloud costs by using AI-driven auto-scaling to dynamically allocate resources based on streaming demand, reducing unnecessary expenditure while ensuring a smooth user experience.

2. Serverless and Function-as-a-Service (FaaS) Evolution

That seamless experience where everything just works the moment you need it—serverless computing is making cloud management feel exactly like that. Serverless computing eliminates idle resources, cutting down costs while boosting cloud performance. You only pay for the execution time of functions, making it a cost-effective cloud optimization technique.

  • How it works: Serverless computing platforms like AWS Lambda, Google Cloud Functions, and Azure Functions execute event-driven workloads, ensuring efficient cloud resource utilization while eliminating the need for constant infrastructure management.
  • Use case: Coca-Cola leveraged AWS Lambda for its vending machines, reducing backend infrastructure costs and improving operational efficiency by scaling automatically with demand. 

3. Decentralized Cloud Computing: Edge Computing for Cost Reduction

Why send all your data to the cloud when it can be processed right where it’s generated? Edge computing reduces data transfer costs and latency by handling workloads closer to the source. By distributing computing power across multiple edge nodes, companies can avoid expensive, centralized cloud processing and minimize data egress fees.

  • How it works: Companies deploy micro data centers and AI-powered edge devices to analyze data closer to the source, reducing dependency on cloud bandwidth and lowering operational costs.
  • Use case: Retail giant Walmart leverages edge computing to process in-store data locally, reducing latency in inventory management and enhancing customer experience while cutting cloud expenses.

4. Cloud Optimization with FinOps Culture

FinOps (Cloud Financial Operations) is a cloud cost management practice that enables organizations to optimize cloud costs while maintaining operational efficiency. By fostering collaboration between finance, operations, and engineering teams, FinOps ensures cloud investments align with business goals, improving ROI and reducing unnecessary expenses.

  • How it works: Companies implement FinOps platforms like Apptio Cloudability and CloudHealth to gain real-time insights, automate cost optimization, and enforce financial accountability across cloud operations.
  • Use case: Early adopters of FinOps were Adobe, which leveraged it to analyze cloud spending patterns and dynamically allocate resources, leading to significant cost savings while maintaining application performance. 

5. Storage Tiering with Intelligent Data Lifecycle Management

Not all data needs a VIP seat in high-performance storage. Intelligent data lifecycle management ensures frequently accessed data stays hot, while infrequently used data moves to cost-effective storage. Cloud-adjacent storage, where data is stored closer to compute resources but outside the primary cloud, is gaining traction as a cost-efficient alternative. By reducing egress fees and optimizing storage tiers, businesses can significantly cut expenses while maintaining performance.

  • How it’s being done: Companies use intelligent storage optimization tools like AWS S3 Intelligent-Tiering, Google Cloud Storage’s Autoclass, and cloud-adjacent storage solutions from providers like Equinix and Wasabi to reduce storage and data transfer costs.
  • Use case: Dropbox optimizes cloud storage costs by using multi-tiered storage systems, moving less-accessed files to cost-efficient storage while keeping frequently accessed data on high-speed servers. 

6. Quantum Cloud Computing: The Future-Proof Cost Gamechanger

Quantum computing sounds like sci-fi, but cloud providers like AWS Braket and Google Quantum AI are already offering early-stage access. While still evolving, quantum cloud computing has the potential to process vast datasets at lightning speed, dramatically cutting costs for complex computations. By solving problems that traditional computers take days or weeks to process, quantum computing reduces the need for excessive computing resources, slashing operational costs.

  • How it works: Cloud providers integrate quantum computing services with existing cloud infrastructure, allowing businesses to test and run quantum algorithms for complex problem-solving without massive upfront investments.
  • Use case: Daimler AG leverages quantum computing to optimize battery materials research, reducing R&D costs and accelerating EV development.

7. Sustainable Cloud Optimization: Green Computing Meets Cost Efficiency

Running workloads when renewable energy is at its peak isn’t just good for the planet—it’s good for your budget too. Sustainable cloud computing aligns operations with renewable energy cycles, reducing reliance on non-renewable sources and lowering overall operational costs.

  • How it works: Companies use carbon-aware cloud scheduling tools like Microsoft’s Emissions Impact Dashboard to track energy consumption and optimize workload placement based on sustainability goals.
  • Use case: Google Cloud shifts workloads to data centers powered by renewable energy during peak production hours, reducing carbon footprint and lowering energy expenses. 

The Next Frontier: Where Cloud Optimization is Headed

Cloud optimization in 2025 isn’t just about playing by the old rules. It’s about reimagining the game entirely. With AI-driven automation, serverless computing, edge computing, FinOps, quantum advancements, and sustainable cloud practices, businesses can achieve cost savings and high cloud performance like never before.

Organizations that embrace these innovations will not only optimize their cloud spend but also gain a competitive edge through improved efficiency, agility, and sustainability. The future of cloud computing in 2025 isn’t just about cost-cutting—it’s about making smarter, more strategic cloud investments.

At Mantra Labs, we specialize in AI-driven cloud solutions, helping businesses optimize cloud costs, improve performance, and stay ahead in an ever-evolving digital landscape. Let’s build a smarter, more cost-efficient cloud strategy together. Get in touch with us today!

Are you ready to make your cloud strategy smarter, cost-efficient, and future-ready with AI-driven, serverless, and sustainable innovations?

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot