Category Archives: Research

Cherenkov research in ‘Focus’

A radiation beam treatment is visualized here in the first in human use of the technique.

A scientific breakthrough may give the field of radiation oncology new tools to increase the precision and safety of radiation treatment in cancer patients by helping doctors “see” the powerful beams of a linear accelerator as they enter or exit the body. Twelve patients are participating in a pilot study, which is being conducted by Thayer professor Brian Pogue, Geisel professors Lesley Jarvis and David J. Gladstone,  graduate students Adam Glaser and Rongxiao Zhang, and medical student Whitney Hitchcock. While the Optics in Medicine Laboratory has been researching potential clinical applications of the Cherenkov effect for over four years, in July of 2013 the team first examined the fluorescent radiation in a female patient undergoing treatment for breast cancer. By integrating Cherenkov imaging into routine clinical care, the team believes that the technology could be used to verify that the proper dose is being delivered to patients, helping to avoid misadministration of radiation therapy, a rare, but dangerous occurrence.

To learn more about Cherenkov imaging, read the full article published in Focus by Norris Cotton Cancer Center.

BEM-based Meshing

Hamid Ghadyani

On February 7th, 2013, Professor Brian Pogue, research scientist Scott Davis, and former Optics in Medicine post doc Hamid Dehghani are teaching an introductory workshop on Near-Infrared Fluorescence and Spectral Tomography (NIRFAST) at the Society of Photo-Optical Instrumentation Engineers (SPIE) Photonics West conference in San Francisco. Developed by the Optics in Medicine Laboratory, Nirfast is used in hospitals and research institutions across the US to model Near-Infrared light transport in tissue. Nirfast is an open source software package that can be downloaded for free, and customized to work with a laboratory’s imaging equipment.

For the duration of his post doctoral position in Dartmouth’s Optics in Medicine Laboratory, research fellow Hamid Ghadyani has improved Nirfast’s meshing capabilities, and added a number of much needed functions to the software package. Hamid received his Bachelors of Science in Mechanical Engineering from Sharif University of Technology in Tehran, Iran, started his masters degree at Temple University, PA, and completed both his masters and doctoral degrees in Mechanical Engineering at Worcester Polytechnic Institute (WPI). In 2010, Hamid presented the Boundary Element Method (BEM) computational work he was doing on Nirfast at the SPIE Optics + Photonics conference in San Diego, and later published the research as “Characterizing accuracy of total hemoglobin recovery using contrast-detail analysis in 3D image-guided near infrared spectroscopy with the boundary element method” in Optics express, 07/2010; 18(15): 15917-35.

While the Finite Element Method (FEM) approximates a partial differential equation  (PDE) over many smaller regions of the entire domain, BEM solves partial differential equations that Green’s function—a function used to solve differential equations with boundary conditions—can be calculated for. In conjunction with Green’s function, boundary conditions are used to solve PDE on the boundary of the domain. The integration of this computational method into Nirfast enables the software bundle to quickly solve a light transport equation without a full-blown step of solid mesh generation.

“The real advantage of BEM is that it simplifies the meshing step. In FEM, mesh creation can account for up to 75 percent of a model’s computational efforts,” explains Hamid. “At the SPIE Optics + Photonics 2010 conference in San Diego, I explained how the Optics in Medicine Laboratory was using BEM-based imaging to model smaller cancer tumors that, in some situations, FEM meshing was unable to detect.”

Through a collaboration with the University of Texas at Austin (UT Austin), Hamid used Dartmouth’s Discovery cluster—a 1704 processor RedHat 5.8 super computer with AMD, Intel, and Nvidia CPUs—to research how small of a tumor Nirfast was able to image. With a software package developed at UT Austin, Hamid ran a simulation on the Discovery cluster that used an impressive 127 GB of Random-access memory (RAM). This research was presented at last year’s Optics Society of America’s (OSA) Miami conference.

A 3D solid mesh is generated using two-dimensional segmented MR images. The cross section shows the location of the tumor in the model.

“My main research interest is developing computational tools that help researchers in biomedical fields harness the power of the FEM method. This involves developing mesh generation algorithms, and high-performance parallel numerical methods,” explains Hamid. “FEM is based on the idea of simplifying PDEs over much smaller sub-regions. The creation of these sub-regions is an important step in any Finite Element analysis as it can affect the accuracy and reliability of solutions. I have streamlined and added advanced features to mesh generation capabilities of Nirfast so that users with almost no background can use meshing tools in their research. This cuts down both the computational time of mesh generation, and the time spent preparing the model significantly.”

Ghadyani has conducted his mesh generation study with current graduate students Kelly Michalsen and Michael Mastanduno, as well as former lab members Amir Golnabi and Xiahxayo Fan. The tools developed by Ghadyani and his collaborators is used by engineers, as well as other professionals who utilize mesh-generation in imaging on a regular basis, including those involved with Electro-Imdepdence tomography, Magnetic Resonance Elastography, and Microwave Imaging Spectroscopy.

Microwave Imaging

Collaborative project of Optics in Medicine Director Keith Paulsen, Dartmouth Engineering Professor Paul Meaney, and researchers at both Dartmouth-Hitchcock Medical Center and the Geisel School of Medicine featured in Focus.

Researchers at the Cancer Imaging and Radiobiology Research Program (CIR) at Dartmouth-Hitchcock’s Norris Cotton Cancer Center study and test new ways to get good images using techniques that exploit different properties of tissue. This research program includes a collaborative team of engineers, family physicians, oncologists, and radiologists.

A semi-transparent CT view of one a study participant’s heel and ankle. The horizontal line overlays indicate where scientists will set the microwave imaging planes.

Microwave imaging has been shown reliable in detecting breast tumors

One area we are exploring is microwave technology: the same basic technology used in microwave ovens can be used to create an image of breast tissue. By sending very low levels (1,000 times less than a cell phone) of microwave energy through tissue, researchers can form a three-dimensional image. These images capture the dielectric properties — electrical conductivity and permittivity (electrical resistance) — of the tissue, which translates into detecting anomalies, such as tumors or other aberrations.

Paul Meaney, a professor at Dartmouth’s Thayer School of Engineering, has been working on microwave engineering for more than 15 years, primarily with Keith Paulsen, the co-director of the CIR, and also the Robert A. Pritzker Professor of Biomedical Engineering at Dartmouth’s Thayer School of Engineering; professor of radiology at the Geisel School of Medicine at Dartmouth; and director of the Dartmouth Advanced Imaging Center at Dartmouth-Hitchcock Medical Center.

For full article, please visit Focus by the Norris Cotton Cancer Center (NCCC).

Epithelial and Stromal Imaging

Scattered light measured from tissue can be uniquely correlated to tissue substructure, function and progression of disease. The ultrastructural information provided by scatter may render optical techniques valuable to diagnosis.Epithelial and Stromal Imaging

Many recent studies have demonstrated that scattered light measured from tissue can be uniquely correlated to tissue substructure, function and progression of disease, if the wavelength dependence of the light is obtained at each pixel. This is because the morphologic changes associated with cancer progression cause organelle and structural matrix alteration, which can be observed macroscopically as local fluctuations in the optical refractive index (RI). These changes include hyper-proliferation of epithelium, nuclear crowding and enlargement, as well as intracellular organelle changes and sub-cellular stromal matrix alteration. Therefore, it is quite reasonable to assume that the ultrastructural information provided by scatter may render optical techniques valuable to diagnosis; the limiting factor being our lack of knowledge about how light scatters through heterogeneous tissues.

Extracting information from scatter spectra requires an ability to reasonably model the behavior of light as it passes through a tissue. This is a rather convoluted problem because it is difficult to separate light that has weakly scattered from that which has multiply scattered, in addition the effects of absorption and scatter are intermingled. To circumvent this complication, optical constraints are applied to limit detected photons to those primarily experiencing a single elastic collision. A raster scanning reflectance spectroscopy imaging system is used to characterize fresh, excised tumors and normal specimens with 100 micron spatial resolution (approximately one mean free scattering path length in tissue). This system was designed to sample the scatter directly, allowing empirical separation of the absorption and scattering effects. Scatter measures are then elucidated with pathology so that diagnostic categories of breast tissue may be optically characterized for a classification algorithm.

To enhance the diagnostic utility of our system, we are also using electron microscopy to visualize individual, sub-wavelength scatterers to better understand how the distribution of small scatterers in the extra-cellular matrix influences optical signals. The focus of this analysis is on collagen fibers because scattering from epithelial cells is well approximated by Mie theory and little is known about collagen, a dominant, non-spherical scatterer. Understanding light-tissue interactions at the microscopic level will improve models of light propagation through breast tissue and consequently data parameterization.


Image-guided NIR Spectroscopy

IG-NIRS provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical NIR technologies, giving information directly relating to the vascular and metabolic status of tissue in-vivo.Image-guided NIR Spectroscopy

Image-guided near infrared spectroscopy (IG-NIRS) provides deep tissue functional characterization at high resolution. This approach combines conventional imaging techniques such as MRI and CT with optical near infrared technologies, giving information directly relating to the vascular and metabolic status of tissue in-vivo. The resultant estimates of total hemoglobin, oxygen saturation, water, lipids and scatter provide a window towards understanding the mechanisms of cancer in terms of angiogenesis, hypoxia, changes in the interstitium and cell organelle structural changes. This type of spectroscopy has been applied for breast cancer diagnosis and treatment monitoring, as well as image-guided fluorescence in small-animals.

Optimization of these systems is essential to provide quantitative and accurate spectroscopy. This optimization encompasses system design for simultaneous multi-modality image acquisition, methods for intelligently combining spatial anatomical structure from MRI/CT into optical recovery, image segmentation, visualization and interpretation of novel combined optical and MRI/CT parameters.

The focus of this project is optimization to go from image segmentation of MRI volume to recovery of spectroscopic parameters in 3-D in a seamless automated manner, in 3-D. Funded by NIHNIBIB, computationally efficient models have been developed using finite element (FE) and boundary element methods (BEM). BEM is specific to IG-NIRS using only surface discretization whereas FEM allows for NIR tomography using volume discretization. Both models solve for light propagation in tissue using diffusion equation. Software packages have been developed using FEM (NIRFAST) and BEM (BEMFAST) to run in multi-processing cluster. Segmentation tools and visualization based on ITK and VTK have been developed specific to this multi-modality setting.

Our goals are to study breast tissue in-vivo and monitoring different tissue responses to neoadjuvant chemotherapy using this multi-modality approach. Cancer shows higher total hemoglobin compared to benign lesions. In addition, total hemoglobin levels appear to be sensitive to how the patient is responding to neoadjuvant chemotherapy. We are also studying image-guided fluorescence in small animals as a precursor to clinical work in fluorescence.