Multimodal approaches that combine near-infrared (NIR) and conventional imaging modalities have been shown to improve optical parameter estimation dramatically and thus represent a prevailing trend in NIR imaging. These approaches typically involve applying anatomical templates from magnetic resonance imaging/computed tomography/ultrasound images to guide the recovery of optical parameters. However, merging these data sets using current technology requires multiple software packages, substantial expertise, significant time-commitment, and often results in unacceptably poor mesh quality for optical image reconstruction, a reality that represents a significant roadblock for translational research of multimodal NIR imaging. This work addresses these challenges directly by introducing automated digital imaging and communications in medicine image stack segmentation and a new one-click three-dimensional mesh generator optimized for multimodal NIR imaging, and combining these capabilities into a single software package (available for free download) with a streamlined workflow. Image processing time and mesh quality benchmarks were examined for four common multimodal NIR use-cases (breast, brain, pancreas, and small animal) and were compared to a commercial image processing package. Applying these tools resulted in a fivefold decrease in image processing time and 62% improvement in minimum mesh quality, in the absence of extra mesh postprocessing. These capabilities represent a significant step toward enabling translational multimodal NIR research for both expert and nonexpert users in an open-source platform.
NIRFAST is open source software for near infrared (NIR) imaging using finite element method for modeling light
diffusion tissue. Recently, we integrated an add-on to NIRFAST based on boundary-element method (BEM) solution to
the diffusion equation. This toolbox requires only surface discretization of the imaging domain as opposed to volume
meshing, geared towards 3D NIR spectroscopy. The software is
Matlab-based and provides a framework for surface
meshing, forward model, reconstruction and data and solution visualization capabilities as well as ability to run in
parallel environments using OpenMP standard. This was validated in simulations, experiments and applied to in-vivo
clinical data and was made open-source for the near infrared imaging community.
We demonstrate quantitative functional imaging using image-guided near-infrared spectroscopy (IG-NIRS) implemented with the boundary element method (BEM) for reconstructing 3-D optical property estimates in breast tissue in vivo. A multimodality MRI-NIR system was used to collect measurements of light reflectance from breast tissue. The BEM was used to model light propagation in 3-D based only on surface discretization in order to reconstruct quantitative values of total hemoglobin (HbT), oxygen saturation, water, and scatter. The technique was validated in experimental measurements from heterogeneous breast-shaped phantoms with known values and applied to a total of seven subjects comprising six healthy individuals and one participant with cancer imaged at two time points during neoadjuvant chemotherapy. Using experimental measurements from a heterogeneous breast phantom, BEM for IG-NIRS produced accurate values for HbT in the inclusion with a <3% error. Healthy breast tissues showed higher HbT and water in fibroglandular tissue than in adipose tissue. In a subject with cancer, the tumor showed higher HbT compared to the background. HbT in the tumor was reduced by 9 µM during treatment. We conclude that 3-D MRI-NIRS with BEM provides quantitative and functional characterization of breast tissue in vivo through measurement of hemoglobin content. The method provides potentially complementary information to DCE-MRI for tumor characterization.
A high frequency ultrasound-coupled fluorescence tomography system, primarily designed for imaging of protoporphyrin IX production in skin tumors <i>in vivo</i>, is demonstrated for the first time. The design couples fiber-based spectral sampling of the protoporphyrin IX fluorescence emission with high frequency ultrasound imaging, allowing thin-layer fluorescence intensities to be quantified. The system measurements are obtained by serial illumination of four linear source locations, with parallel detection at each of five interspersed detection locations, providing 20 overlapping measures of subsurface fluorescence from both superficial and deep locations in the ultrasound field. Tissue layers are defined from the segmented ultrasound images and diffusion theory used to estimate the fluorescence in these layers. The system calibration is presented with simulation and phantom validation of the system in multilayer regions. Pilot <i>in-vivo</i> data are also presented, showing recovery of subcutaneous tumor tissue values of protoporphyrin IX in a subcutaneous U251 tumor, which has less fluorescence than the skin.
Mesh quality is an important factor for stable, repeatable numerical simulations. The Delaunay method is
widely used for creation of 3D tetrahedral meshes. Two-dimensional triangulation via Delaunay exhibits the
mathematical property of maximizing the minimum interior angle. This feature provides excellent quality meshes for a
given node deployment. However, the 3D equivalent of this property, i.e. to maximize the minimum solid angle, is not
assured with 3D Delaunay. The tetrahedron's interior solid angle is directly related to mesh quality, but it is
independent of the Delaunay process. Consequently, sliver elements and poor quality meshes can be created via
Delaunay tetrahedral formation. In this paper, we describe a method for maximizing the minimum solid angle of
tetrahedral meshes by changing the locations of non-boundary nodes. The displacement of nodes uses a gradient-based
approach. The process is iterative and terminates when the mesh quality exceeds a user specified quality or convergence
criterion. The technique is robust. The relocation of vertices is local which avoids significant deformation of the mesh.
The results show considerable improvements in mesh quality. Using a 3D human brain mesh (27,000+ elements), our
algorithm reduced the number of ill-formed elements three fold. We are extending this approach to allow tangential
motion along the boundary surfaces. Currently all boundary nodes are fixed which constrains some of the element
In contrast to traditional 'video conferencing' the Access Grid (AG), developed by Argonne National Laboratory, is a collaboration of audio, video and shared application tools which provide the 'persistent presence' of each participant. Among the shared application tools are the ability to share viewing and control of presentations, browsers, images and movies. When used in conjunction with Virtual Network Computing (VNC) software, an investigator can interact with colleagues at a remote site, and control remote systems via local keyboard and mouse commands. This combination allows for effective viewing and discussion of information, i.e. data, images, and results. It is clear that such an approach when applied to the medical sciences will provide a means by which a team of experts can not only access, but interact and control medical devices for the purpose of experimentation, diagnosis, surgery and therapy. We present the development of an application node at our 4.7 Tesla MR magnet facility, and a demonstration of remote investigator control of the magnet. A local magnet operator performs manual tasks such as loading the test subject into the magnet and administering the stimulus associated with the functional MRI study. The remote investigator has complete control of the magnet console. S/he can adjust the gradient coil settings, the pulse sequence, image capture frequency, etc. A geographically distributed audience views and interacts with the remote investigator and local MR operator. This AG demonstration of MR magnet control illuminates the potential of untethered medical experiments, procedures and training.