Light and Sound
By Rebecca Montz, EdD, MBA, CNMT, PET, RT(N)(CT), NMTCB RS
Radiology Today   
Vol. 24 No. 8 P. 21   

Biomedical imaging continues to make strides with new and advanced technologies. Being mostly noninvasive, biomedical imaging is a powerful component of diagnostic imaging and an essential tool in the biological sciences. It is an important tool for investigating anatomy, physiology, and function at multiple levels, from cellular to the whole body. The technology combines anatomical structure with functional data, providing more depth and detail to clinicians, including real-time cellular-level data regarding anatomical locations, organs, tissues, and biological indicators.   

Some biomedical imaging modalities offer precise tracking of metabolites that can be used as biomarkers for identifying disease, tracking progress, and determining treatment response. One of these modalities is photoacoustic tomography (PAT), also referred to as optoacoustic tomography, which integrates the molecular contrast of optical imaging with the high spatial resolution of ultrasound imaging in deep tissue. PAT imaging is a hybrid imaging technique that combines the spectroscopic specificity of optics with the tissue penetration of ultrasound.

PAT provides cross-sectional or 3D imaging of an object based on the photoacoustic effect, which converts absorbed light to sound. Light is absorbed by biological tissue and converted to transient heating, which is successively converted into an ultrasonic wave due to thermoelastic expansion. These generated ultrasonic waves are detected by ultrasonic transducers and analyzed to produce images. The magnitude of the ultrasonic emission, which is proportional to the local energy deposition, reveals physiologically specific optical absorption contrast.

By linking the rich optical contrast and ultrasonic resolution, PAT is an imaging modality capable of providing multiscale, high-resolution structural, functional, and molecular imaging. Functional imaging, also known as physiological imaging, can detect or measure changes in metabolism, blood flow, chemical composition, and absorption. Molecular imaging enables the visualization, characterization, and quantification of biologic processes taking place at the cellular and subcellular levels. Over the years, biomedical imaging has played a significant role in diagnosing diseases, monitoring therapy, and providing biological insights into lives. However, these discoveries have only just begun.

A multidisciplinary team from the University of Texas at Arlington, led by principal investigator and mathematician Souvik Roy, PhD, along with statistician and coprincipal investigator Suvra Pal, PhD, in collaboration with radiologists for experimental validation, are on a mission to improve PAT using a new technique called quantitative PAT (QPAT). Their work is supported by three-year funding from the National Science Foundation- Computational Mathematics Division.

QPAT is a quantitative version of the PAT mechanism, where the combination of optical tomography and ultrasound allows additional information about optical parameters such as absorption, diffusion, and scattering coefficients to be obtained. In optical tomography, laser light or infrared light is transmitted inside the medium of interest, which is usually a human body. Once the light is absorbed by the body, the intensity of the light is detected and measured, providing the optical properties of the biological components.

When the light interacts with tissues, blood, and other biological characteristics, all, some, or none of the light is absorbed. Some biological components will absorb light, while others will cause the light to scatter without being absorbed. The difference in the intensities of the light provides clinicians with a prediction of the optical parameters of the tissue.

However, optical tomography is known to provide only high-contrast reconstructions of the optical parameters. It does not produce high resolution and, thus, may miss the location of an abnormal tissue. On the other hand, ultrasound has high resolution but not high contrast, making it difficult to differentiate between varying intensities. The goal of QPAT is to combine these two modalities as hybrid imaging to take advantage of each modality’s most useful features. Differentiating between the varying optical parameters in multiple objects is what QPAT aims at, Roy says.

In QPAT, once the light is absorbed by the body, the generated ultrasound waves are measured via detectors placed on the surface of the body. With these acoustic measurements, the initial sound pressure distribution is obtained, which is the classical PAT computational method. In the second step, based on the sound pressure distribution and the physics of photon propagation inside the tissues, the distribution of the optical parameters is obtained to provide precise information about the structural and physiological properties of the tissues. With these combined steps, the profiles of the optical parameters can pinpoint the location of a tissue and distinguish whether it is cancerous or benign.

Roy explains that cancerous tissues contain a wide variety of abnormal cells, causing them to absorb a great deal of light energy compared with normal and healthy tissues. Accurate profiles of optical properties in different tissues allow clinicians to recognize that the absorption property of abnormal tissue is quite high compared with surrounding normal tissues. This indicates that there may be cancerous tissue in that location. This prediction is achieved through an accurate and robust QPAT reconstruction algorithm.

Algorithmically Speaking
Roy says QPAT is a multiphysics imaging technique that involves the process of sending near-infrared light into the body; the tissues absorb light in various capacities. This leads to heating and spontaneous cooling of the tissues, known as the photoelastic effect, which generates sound waves. The intensity of the waves is measured through detectors placed on the surface of the body, and the waves are processed through a mathematical reconstruction algorithm to help obtain various optical properties of the tissues, such as diffusion, absorption, and scattering abilities. Since cancerous tissues absorb more light than normal tissues, profiles of the optical properties of various tissues help identify cancerous tissues from healthy ones. They also help accurately stage the cancer. The mathematical reconstruction process is known as the QPAT reconstruction algorithm.

However, for high-resolution and high-contrast reconstructions, Roy says traditional QPAT reconstruction algorithms require a sufficient amount of sound wave data across the entire circumference of the detectors. Unfortunately, there are several practical scenarios in which such large amounts of data are not available. For example, injury to one part of the brain or spinal cord may prevent the placement of detectors along those regions.

Lack of data corrupts images and renders them inaccurate. Roy and Pal’s QPAT reconstruction algorithm addresses this underlying issue and has the potential to deliver highquality images with a lesser amount of measured data. The team will use a novel combination of game theory, statistical sensitivity analysis, and gradient-free optimal control methods to help complete the missing acoustic wave measurements, stabilizing and recalibrating computational algorithms to provide high-contrast and high-resolution images, even with low signal-to-noise ratio measurements.

Opportunities and Challenges
One advantage of utilizing the QPAT reconstruction algorithm is that it can handle the absence of multidirectional and noisy data and still provide fast and accurate images of various optical profiles, which is sometimes not feasible with current algorithms. “With foundations based on the novel combination of several mathematical methods, our QPAT algorithm will eventually lead to cost effective and timely diagnosis of cancer, leading to the improvement of patient survival rates,” Roy says. He also expects that this will reduce anxiety for patients and decrease health care costs by reducing the need for repeated scans.

The team’s algorithm also has a significant advantage over state-of-art machine learning algorithms because there is no need to train and validate the QPAT algorithm using large data samples—which are unavailable. It also provides accurate personalized reconstructions for varying classes of patients, Roy explains.

Some primary challenges, according to Roy, are accurately filling in missing data using a game theory setup since there could be a potential issue of nonuniqueness of the missing data. Another challenge is properly calibrating the reconstruction algorithm so that it can work dynamically for individual patients rather than having to redevelop the entire algorithm from scratch for each patient.

Although there are challenges, Roy is confident that a QPAT reconstruction algorithm will be beneficial in the clinical setting for accurate and fast monitoring of the brain and hemodynamic activities, as well as for cancer diagnosis. Currently, the proposed QPAT reconstruction algorithm will help with accurate and fast detection and monitoring of cancers without the need for repeated imaging procedures. This helps save time and money for patients as well as hospitals, Roy notes.

“In the mission to combat cancer, there arises the need for fast, accurate, and robust diagnostic imaging techniques, and QPAT is one such multiphysics imaging modality,” Roy says.

Future Possibilities
Biomedical imaging is a dynamic, innovative imaging technology that plays a vital role in health care. As the capability to perform real-time imaging of in vivo molecular and cellular events increases, clinicians will be better equipped to treat illnesses at early stages. The continued pursuit of improvement in health care will likely produce many innovative diagnostic imaging techniques, such as QPAT.

Currently, the team’s algorithm is in its developmental stage, and once it is ready to be carried forward to the clinical trial stage, they will need to develop a global PAT detection machine that can be used for imaging different parts of the body, much like a mobile ultrasound scanner. Roy is aiming for his team’s research to create an overall global impact but knows there is still a lot of work to be done in the development of QPAT and global PAT detection.

Roy says science is about coming up with new ideas to address challenges and implement solutions. In the long run, he believes patients and health care providers will benefit immensely from accurate images by significantly improving inference times and cost of procedures. The focus of the team’s QPAT research is to assist in the early diagnosis of cancer in the most accurate way so clinicians are equipped to provide the best possible treatment.

“Providing doctors with a mechanism to obtain high-quality scans to significantly improve patient diagnostics is the ultimate goal of our QPAT algorithm,” Roy says.

— Rebecca Montz, EdD, MBA, CNMT, PET, RT(N)(CT), NMTCB RS, has worked at the Mayo Clinic Jacksonville and University of Texas MD Anderson Cancer Center in Houston as a nuclear medicine and PET technologist.