Experts believe compressive sensing could find wide application in medical laboratory and pathology testing, particularly where large amounts of data are generated
Pathologists and medical laboratory managers may soon be working with a new tool in their labs. It is called “Compressive Sensing” (CS) and it is an innovative mathematical approach that quickly and efficiently gets an answer by sampling large volumes of a data.
Currently compressive sensing is used in medical imaging technology. CS reduces radiation and speeds up imaging diagnostics. Some experts familiar with this technology believe that it can be used in those clinical laboratories that are working with new diagnostic technologies that generate large volumes of data. CS could dramatically reduce times to analyze results and lower the cost of expensive tests like whole-genome sequencing.
CS Microarrays an Improvement over DNA Microarrays
Medical scientists are already exploring uses for CS in medical laboratory diagnostics. Researchers, for example, have designed CS Microarrays (CSM) models that identify target organisms with fewer shared probes than a regular DNA microarray. CSM alleviates three problems faced by traditional microarrays, noted researchers in a paper on CSM design published in 2008 by EURASIP Journal on Bioinformatics and Systems Biology. The three problems include:
• Cross-hybridization events that lead to errors in array readout,
• Difficulty in sensing a large number of organisms, while simultaneously miniaturizing the array, and,
• An inefﬁcient utilization of the large number of array spots due to the sparsity of target type in a given sample.
Researchers have also designed a CS model for RNA interference (RNAi) cellular screening, an important genetic tool in studying the function of target genes that helps identify signaling pathways and cellular phase type. They noted in a paper published in 2012 by BMC Bioinformatics that traditional RNAi screening requires a huge library of small interfering RNAs (siRNAs) and, therefore, is time-consuming and expensive. Using the csiRNA model reduces the size of the siRNA library by one-third, saving time and lowering the cost for large-scale RNAi screening experiments.
CS Expected to Create Much Savings in Healthcare
Hailed as one of this century’s most significant mathematical discoveries by physicists at the University of Oslo, Norway, CS is poised to bring about enormous savings in the health sector, noted a report in Medical Design Technology (MDT).
“The idea is to solve a task by involving as few measurements as possible,” explained Physicist Anders Malthe-Sørenssen, Ph.D., a professor at the University of Oslo, in an article published by the university’s research magazine Apollon. “Whenever data capture is expensive, investment in this new mathematical approach may soon prove cost effective.”
Malthe-Sørenssen was introduced to the mathematical theory while attending a talk given by its creator, University of California Los Angeles Professor Terence Tao, Ph.D., an Australian-born child math prodigy who has been dubbed the “Mozart of Math.” At age 24, Tao was the youngest person ever to become a full professor.
CS Could Accelerate Imaging Scans, Reduce Radiation and Improve Mobile Devices
The sparse sampling technique provided by CS could reduce the number of measuring points for magnetic resonance imaging (MRI) to one-sixth of the present level, which would make MRI scans six times faster than today. This idea was tested at Stanford University’s American Lucile Packard Children’s Hospital in Palo Alto, California, a few years ago, noted the MDT report, which also noted that this was achieved with smart selection of sampling points for the MRI scan.
“Compressed sensing will enable you to calculate all the things you don’t measure,” said Andreas Solbrå a doctoral student and research fellow at the University of Oslo. “Every measurement provides much more information than you think, provided you are smart about the sampling.”
It could also accelerate CT scans by six times. “This means that hospitals may examine six times as many patients without having to buy more scanners and increase their staffing levels,” Solbrå pointed out. “There is also reason to believe that the new mathematical method can reduce the level of radiation from CT scans by five-sixths,” he added, noting that a CT scanner exposes patients to radiation equal to 10 years of natural radiation accumulation.
A minor problem of sparsity measurements, however, is more time must be spent calculating results. “Today, doctors are able to analyze the medical images straight away,” Solbrå continued, noting that it would mean extra time for computers to calculate results. Technicians who implement the CS method for medical diagnostics also must be highly skilled in mathematics, as well as calculation theory, he said.
CS may also improve wearable health monitoring technology, making it smaller, cheaper, and more energy efficient than today. Zhilin Zhang, Ph.D., a manager and staff research engineer at the Samsung Research America facility in Dallas, Texas, is working on signal processing solutions for Samsung projects involving wearable health monitoring devices, intelligent systems, smart-home monitoring, Big Data and communications.
He noted in a blog that CS could reduce power and energy use by computing resources in sensors used in wireless ambulatory devices, extend sensor lifespan and significantly simplify hardware design, reducing both the size and cost of devices.
CS is just one more innovative technology pathologists and medical laboratories can expect to see in future generations of diagnostic technologies that generate large volumes of data, such as genome sequencing analyzers. Using CS to analyze data would speed results processing and lower the cost of tests.