The GENAT4/GAMOS Monte Carlo (MC) plug-in is now available for free download. The Optics in Medicine Laboratory has also released a user’s manual for the software, example simulation files, and a MATLAB file of the program.

The technique developed by Glaser and his Dartmouth colleagues has been verified through a series of experiments using a clinical linac from Varian Medical Systems. The first step in the process is to fill a water tank with tap water and dissolve the fluorophore (quinine sulphate) to a concentration of 1.0 g/l. Then, a standard commercial CMOS camera is positioned at a given distance from the water tank, perpendicular to the incident beam, and focused to the beam’s isocentre.

When the beam is turned on, a 2D projection image is captured using a 10 s exposure time, and an equivalent image with the beam off is recorded and subtracted to isolate the Cerenkov-excited fluorescence for direct correlation to the deposited dose.

“Each image is immediately downloaded from the camera to a computer and can be viewed in real time,” explained Glaser. “Our experiments in this proof-of-concept study show that the strength of the fluorescence signal equates near-linearly to the dose imparted in the water. We believe this is the first demonstration of using Cerenkov light to indirectly determine the spatial distribution of a charged particle’s energy deposition within a medium.”

For more on Glaser’s research, read the article on medicalphysicsweb.com.

Kristian’s projects are in the field of biomedical technologies, and his research project is part of a larger Dartmouth initiative titled “Fluorescence guided neurosurgery.”  Sexton’s work explores the use of fluorescence to help surgeons and medical practitioners differentiate between diseased and healthy tissue. In medicine, fluorescence is used to identify healthy and cancerous tissue during surgical procedures.

To read the full post, visit the Machine Shop’s blog.

Over a decade ago, a group of German doctors discovered that if a patient is given an oral dose of a 5-aminolevulinic acid solution before brain surgery, a chemical reaction will cause certain cells, including cancer cells, to appear fluorescent, allowing them to identify tumors for removal during surgery.

But it was Dartmouth engineering professor Keith Paulsen and his team, along with doctors from the Ontario Cancer Institute in Toronto, who took even more of the guesswork out of fluorescence-guided brain surgery by creating a fiber optic probe that when placed on early-stage, low-grade tumors detects fluorescence not visible to the naked eye.

For more, read the full article published by the Thayer School of Engineering on 2/4/2013.

Researchers from Dartmouth-Hitchcock Medical Center and the Thayer School of Engineering have developed a quantitative imaging system to detect low-grade brain cancer cells and make tumor removal more precise, according to Thayer School professor and research group co-leader Keith Paulsen.

The technology consists of a drug, taken pre-operatively, which is broken down, processed and moved into brain tumor tissue.

The fluorescent compound accumulates most intensely in high-grade brain tumor cells, which are not curable by surgery, according to Paulsen. Low-grade tumor cells that are potentially curable, however, accumulate a lower percentage of the compound.

To learn more about this collaborative research project, read Elizabeth Mc Nally’s article in The Dartmouth. 

In the segment, graduate student Kolbein Kolste explains how the imaging technique that he developed with PhD/MD candidate Pablo Valdes, Dr. David Roberts, and the directors of the Optics in Medicine Lab work in practice. The research project is being funded by a grant from the National Science Foundation, and the optical probe is being developed through a research collaboration with the University of Michigan.

WCAX.COM Local Vermont News, Weather and Sports-

To learn more about this research collaboration, visit the press release published on the WCAX website.

Hamid Ghadyani

On February 7^th, 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.