Data generated by 3D medical scans such as MRI, CT, CAT, ultrasound, tactile imaging, elastography, photoacoustic imaging and thermography, while technically volumetric data, is generally stored in a tomographic format (e.g., the industry-standard DICOM format) where the aggregated data is volumetric but it has broken up into a series of discrete slices, each a 2D image. Radiologists and other users of these data can view individual slices of the data set. With the application of considerable processor cycles, these images can be manipulated and modern systems can display the data as blocks of volumetric images.

However, if the data were stored in a true volumetric format and the Morsys algorithm applied, medical practitioners and CDS systems could realize significant benefits. Not only could pre-identified important structures be visualized in great detail much more efficiently and accurately, but both humans and systems could analyze the data relatively easily for possible points of interest.

Human-mediated Data Analysis

While advances in processing power have allowed big data to be manipulated faster than ever before, trade-offs remain between how much data will be manipulated, how much lag time the practitioner is willing to accept when manipulating the data, and how much the health system is willing to invest in the hardware that stores, processes and renders the images.

Using the Morsys algorithm with its multiresolution, progressive rendering and hyper-efficient capabilities, these trade-offs can be reconsidered with significant improvements to every part of the equation. Even with significantly reduced investments in hardware, and while dealing with full data sets and not subsets thereof, practitioners will be able to zoom, change viewing angles and otherwise manipulate the data in real time.

The Morsys algorithm is so efficient that, using a typical hardware configuration found in a modern MRI-viewing system, a full-body scan of a human could be loaded and a practitioner could “fly through” the data, pausing, zooming, panning and altering her path through the image at will, without experiencing any hitches or glitches in the images on her screen. Granted, the software for this has not been written yet, but the hardware environments are already more than capable providing the Morsys algorithm is used.

As mentioned earlier in this document, the efficiency and progressive-rendering capabilities of the Morsys algorithm will also allow more to be accomplished over telemedicine networks and via mobile computing. While these advantages will allow huge data sets to be manipulated in real time in the hardware environments that currently support major medical imaging installations, they will also allow previously impossible things to be done at a distance on more modest hardware.

Whether this is a practitioner watching a progressively rendered image slowly resolving on her screen (such as we experienced in 2D when JPEGs slowly resolved on our computer screens during the days of dial-up Internet) as massive chunks of data are pulled from a remote system during a telemedicine session, or a radiologist with an iPad pulling raw data from a server and manipulating it using the iPad’s internal processing power, the Morsys algorithm will allow significant strides to be made in decoupling volumetric data manipulation from massively parallel computer systems.

Clinical Decision Support

While the concept of CDS has been around for a few decades, advances in system interoperability, computing power and bandwidth for transmitting data have resulted in significant interest and breakthroughs in CDS during the past decade. The amount of data available has far exceeded the ability of humans to manually evaluate even the smallest fraction of it. Humans only focus on those pieces of known interest, often referred to as “important structures,” and lack the time to plow through mounds of data looking for something that might be important.

While medical judgments and decisions must continue to be made by humans, CDS systems play a significant role in improving healthcare, lowering costs and lowering liability. These systems are already being integrated with EHRs to prompt best practices during medical visits (e.g., immunization forecasting or lab test recommendations), to run reminder/recall processes, and, outside of EHRs, to evaluate large swathes of medical data for known patterns or deviations from certain specified limits.

Given the current methods for storing, decomposing, analyzing and manipulating volumetric data, CDS with these data borders on the impossible. The Morsys algorithm will change that. Given its neighbor-finding attributes, coupled with it enormous improvements in efficiency, volumetric medical scans will be able to be analyzed quickly for areas of interest or concern and then flagged and tagged for evaluation and decisions by specialists.


Volumetric data processing at the speed of light