NIRViz: 3D visualization software for multimodality optical imaging using visualization toolkit (VTK) and insight segmentation toolkit (ITK).

Optical imaging using near-infrared light is used for noninvasive probing of tissues to recover vascular and molecular status of healthy and diseased tissues using hemoglobin contrast arising due to absorption of light. While multimodality optical techniques exist, visualization techniques in this area are limited. Addressing this issue, we present a simple framework for image overlay of optical and magnetic resonance (MRI) or computerized tomographic images which is intuitive and easily usable, called NIRViz. NIRViz is a multimodality software platform for the display and navigation of Digital Imaging and Communications in Medicine (DICOM) MRI datasets and 3D optical image solutions geared toward visualization and coregistration of optical contrast in diseased tissues such as cancer. We present the design decisions undertaken during the design of the software, the libraries used in the implementation, and other implementation details as well as preliminary results from the software package. Our implementation uses the Visualization Toolkit library to do most of the work, with a Qt graphical user interface for the front end. Challenges encountered include reslicing DICOM image data and coregistration of image space and mesh space. The resulting software provides a simple and customized platform to display surface and volume meshes with optical parameters such as hemoglobin concentration, overlay them on magnetic resonance images, allow the user to interactively change transparency of different image sets, rotate geometries, clip through the resulting datasets, obtain mesh and optical solution information, and successfully interact with both functional and structural medical image information.

3-D Image-guided diffuse optical tomography using boundary element method and MPI implementation.

Boundary elements provide an attractive method for image-guided multi-modality near infrared spectroscopy in three dimensions using only surface discretization. This method operates under the assumption that the underlying tissue contains piece-wise constant domains whose boundaries are known a priori from an alternative imaging modality such as MRI or microCT. This significantly simplifies the meshing process providing both speed-up and accuracy in the forward solution. Challenges with this method are in solving dense matrices, and working with complex heterogeneous domains. Solutions to these problems are presented here, with applications in breast cancer imaging and small - animal molecular imaging.

Rapid magnetic resonance-guided near-infrared mapping to image pulsatile hemoglobin in the breast.

A near-IR (NIR) tomography system with spectral-encoded sources was built to quantify the temporal contrast in human breast tissue using guidance from magnetic resonance imaging. The systems were integrated with a custom breast coil interface to provide simultaneous acquisition. The NIR signal was synchronized to simultaneous finger pulse oximeter plethysmogram, which offered a frequency reference. A 0.1 s temporal delay of the absorption pulse within adipose tissue relative to fibroglandular tissue was found, in an initial human study, showing the potential for novel contrast imaging of fast flow signals in deep tissue.

A coupled finite element-boundary element method for modeling Diffusion equation in 3D multi-modality optical imaging.

Three dimensional image reconstruction for multi-modality optical spectroscopy systems needs computationally efficient forward solvers with minimum meshing complexity, while allowing the flexibility to apply spatial constraints. Existing models based on the finite element method (FEM) require full 3D volume meshing to incorporate constraints related to anatomical structure via techniques such as regularization. Alternate approaches such as the boundary element method (BEM) require only surface discretization but assume homogeneous or piece-wise constant domains that can be limiting. Here, a coupled finite element-boundary element method (coupled FE-BEM) approach is demonstrated for modeling light diffusion in 3D, which uses surfaces to model exterior tissues with BEM and a small number of volume nodes to model interior tissues with FEM. Such a coupled FE-BEM technique combines strengths of FEM and BEM by assuming homogeneous outer tissue regions and heterogeneous inner tissue regions. Results with FE-BEM show agreement with existing numerical models, having RMS differences of less than 0.5 for the logarithm of intensity and 2.5 degrees for phase of frequency domain boundary data. The coupled FE-BEM approach can model heterogeneity using a fraction of the volume nodes (4-22%) required by conventional FEM techniques. Comparisons of computational times showed that the coupled FE-BEM was faster than stand-alone FEM when the ratio of the number of surface to volume nodes in the mesh (N(s)/N(v)) was less than 20% and was comparable to stand-alone BEM ( ± 10%).

Characterizing accuracy of total hemoglobin recovery using contrast-detail analysis in 3D image-guided near infrared spectroscopy with the boundary element method.

The quantification of total hemoglobin concentration (HbT) obtained from multi-modality image-guided near infrared spectroscopy (IG-NIRS) was characterized using the boundary element method (BEM) for 3D image reconstruction. Multi-modality IG-NIRS systems use a priori information to guide the reconstruction process. While this has been shown to improve resolution, the e(R)ect on quantitative accuracy is unclear. Here, through systematic contrast-detail analysis, the fidelity of IG-NIRS in quantifying HbT was examined using 3D simulations. These simulations show that HbT could be recovered for medium sized (20mm in 100mm total diameter) spherical inclusions with an average error of 15%, for the physiologically relevant situation of 2:1 or higher contrast between background and inclusion. Using partial 3D volume meshes to reduce the ill-posed nature of the image reconstruction, inclusions as small as 14 mm could be accurately quantified with less than 15% error, for contrasts of 1.5 or higher. This suggests that 3D IG-NIRS provides quantitatively accurate results for sizes seen early in treatment cycle of patients undergoing neoadjuvant chemotherapy when the tumors are larger than 30 mm.

Imaging targeted-agent binding in vivo with two probes.

An approach to quantitatively image targeted-agent binding rate in vivo is demonstrated with dual-probe injection of both targeted and nontargeted fluorescent dyes. Images of a binding rate constant are created that reveal lower than expected uptake of epidermal growth factor in an orthotopic xenograft pancreas tumor (2.3 x 10(-5) s(-1)), as compared to the normal pancreas (3.4 x 10(-5) s(-1)). This approach allows noninvasive assessment of tumor receptor targeting in vivo to determine the expected contrast, spatial localization, and efficacy in therapeutic agent delivery.

Image guided near-infrared spectroscopy of breast tissue in vivo using boundary element method.

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.

Sensitivity of hemoglobin concentration on optical probe positioning in image-guided near infrared spectroscopy.

Multi-modality image-guided near infrared spectroscopy provides volume-based quantification of total hemoglobin, oxygen saturation, water and scatter in various tissue types in-vivo. The accuracy of these parameters depends on the location of the imaging probe and its distance from the tumor. In a numerical study, we have performed simulation to analyze this effect in a breast-specific imaging domain. Results show that the accuracy of total hemoglobin decreases by 25% for every centimeter away from the tumor center, for a typical size of 35mm cancer in the breast. Image guidance is necessary for accurate positioning, and multi-plane acquisition can improve this accuracy.

Imaging of glioma tumor with endogenous fluorescence tomography.

Tomographic imaging of a glioma tumor with endogenous fluorescence is demonstrated using a noncontact single-photon counting fan-beam acquisition system interfaced with microCT imaging. The fluorescence from protoporphyrin IX (PpIX) was found to be detectable, and allowed imaging of the tumor from within the cranium, even though the tumor presence was not visible in the microCT image. The combination of single-photon counting detection and normalized fluorescence to transmission detection at each channel allowed robust imaging of the signal. This demonstrated use of endogenous fluorescence stimulation from aminolevulinic acid (ALA) and provides the first in vivo demonstration of deep tissue tomographic imaging with protoporphyrin IX.

Video-rate near infrared tomography to image pulsatile absorption properties in thick tissue.

A high frame-rate near-infrared (NIR) tomography system was created to allow transmission imaging of thick tissues with spectral encoding for parallel source implementation. The design was created to maximize tissue penetration through up to 10 cm of tissue, allowing eventual use in human imaging. Eight temperature-controlled laser diodes (LD) are used in parallel with 1.5 nm shifts in their lasing wavelengths. Simultaneous detection is achieved with eight high-resolution, CCD-based spectrometers that were synchronized to detect the intensities and decode their source locations from the spectrum. Static and dynamic imaging is demonstrated through a 64 mm tissue-equivalent phantom, with acquisition rates up to 20 frames per second. Imaging of pulsatile absorption changes through a 72 mm phantom was demonstrated with a 0.5 Hz varying object having only 1% effect upon the transmitted signal. This subtle signal change was used to show that while reconstructing the signal changes in a tissue may not be possible, image-guided recovery of the pulsatile change in broad regions of tissue was possible. The ability to image thick tissue and the capacity to image periodic changes in absorption makes this design well suited for tracking thick tissue hemodynamics in vivo during MR or CT imaging.

Numerical modelling and image reconstruction in diffuse optical tomography.

The development of diffuse optical tomography as a functional imaging modality has relied largely on the use of model-based image reconstruction. The recovery of optical parameters from boundary measurements of light propagation within tissue is inherently a difficult one, because the problem is nonlinear, ill-posed and ill-conditioned. Additionally, although the measured near-infrared signals of light transmission through tissue provide high imaging contrast, the reconstructed images suffer from poor spatial resolution due to the diffuse propagation of light in biological tissue. The application of model-based image reconstruction is reviewed in this paper, together with a numerical modelling approach to light propagation in tissue as well as generalized image reconstruction using boundary data. A comprehensive review and details of the basis for using spatial and structural prior information are also discussed, whereby the use of spectral and dual-modality systems can improve contrast and spatial resolution.

Diagnostic detection of diffuse glioma tumors in vive with molecular fluorescent probe-based transmission spectroscopy.

The diffuse spread of glioma tumors leads to the inability to image and properly treat this disease. The optical spectral signature of targeted fluorescent probes provides molecular signals from the diffuse morphologies of glioma tumors, which can be a more effective diagnostic probe than standard morphology-based magnetic resonance imaging (MRI) sequences. Three orthotopic xenograft glioma models were used to examine the potential for transmitted optical fluorescence signal detection in vivo, using endogenously produced protoporphyrin IX (PpIX) and exogenously administered fluorescently labeled epidermal growth factor (EGF). Accurate quantification of the fluorescent signals required spectral filtering and signal normalization, and when optimized, it was possible to improve detection of sparse diffuse glioma tumor morphologies. The signal of endogenously produced PpIX provided similar sensitivity and specificity to MRI, while detection with fluorescently labeled EGF provided maximal specificity for tumors with high EGF receptor activity. Optical transmitted fluorescent signal may add significant benefit for clinical cases of diffuse infiltrative growth pattern glioma tumors given sufficient optimization of the signal acquisition for each patient.

OPTIMIZATION OF 3-D IMAGE-GUIDED NEAR INFRARED SPECTROSCOPY USING BOUNDARY ELEMENT METHOD.

Multimodality imaging systems combining optical techniques with MRI/CT provide high-resolution functional characterization of tissue by imaging molecular and vascular biomarkers. To optimize these hybrid systems for clinical use, faster and automatable algorithms are required for 3-D imaging. Towards this end, a boundary element model was used to incorporate tissue boundaries from MRI/CT into image formation process. This method uses surface rendering to describe light propagation in 3-D using diffusion equation. Parallel computing provided speedup of up to 54% in time of computation. Simulations showed that location of NIRS probe was crucial for quantitatively accurate estimation of tumor response. A change of up to 61% was seen between cycles 1 and 3 in monitoring tissue response to neoadjuvant chemotherapy.

Image-guided near infrared spectroscopy using boundary element method: phantom validation.

Image-guided near infrared spectroscopy (IG-NIRS) can provide high-resolution vascular, metabolic and molecular characterization of localized tissue volumes in-vivo. The approach for IG-NIRS uses hybrid systems where the spatial anatomical structure of tissue obtained from standard imaging modalities (such as MRI) is combined with tissue information from diffuse optical imaging spectroscopy. There is need to optimize these hybrid systems for large-scale clinical trials anticipated in the near future in order to evaluate the feasibility of this technology across a larger population. However, existing computational methods such as the finite element method mesh arbitrary image volumes, which inhibit automation, especially with large numbers of datasets. Circumventing this issue, a boundary element method (BEM) for IG-NIRS systems in 3-D is presented here using only surface rendering and discretization. The process of surface creation and meshing is faster, more reliable, and is easily generated automatically as compared to full volume meshing. The proposed method has been implemented here for multi-spectral non-invasive characterization of tissue. In phantom experiments, 3-D spectral BEM-based spectroscopy recovered the oxygen dissociation curve with mean error of 6.6% and tracked variation in total hemoglobin linearly.

Spectral tomography with diffuse near-infrared light: inclusion of broadband frequency domain spectral data.

Near-infrared (NIR) region-based spectroscopy is examined for accuracy with spectral recovery using frequency domain data at a discrete number of wavelengths, as compared to that with broadband continuous wave data. Data with more wavelengths in the frequency domain always produce superior quantitative spectroscopy results with reduced noise and error in the chromophore concentrations. Performance of the algorithm in the situation of doing region-guided spectroscopy within the MRI is also considered, and the issue of false positive prior regions being identified is examined to see the effect of added wavelengths. The results indicate that broadband frequency domain data are required for maximal accuracy. A broadband frequency domain experimental system was used to validate the predictions, using a mode-locked Ti:sapphire laser for the source between 690- and 850-nm wavelengths. The 80-MHz pulsed signal is heterodyned with photomultiplier tube detection, to lower frequency for data acquisition. Tissue-phantom experiments with known hemoglobin absorption and tissue-like scatter values are used to validate the system, using measurements every 10 nm. More wavelengths clearly provide superior quantification of total hemoglobin values. The system and algorithms developed here should provide an optimal way to quantify regions with the goal of image-guided breast tissue spectroscopy within the MRI.

Methodology development for three-dimensional MR-guided near infrared spectroscopy of breast tumors.

Combined Magnetic Resonance (MR) and Near Infrared Spectroscopy (NIRS) has been proposed as a unique method to quantify hemodynamics, water content, and cellular size and packing density of breast tumors, as these tissue constituents can be quantified with increased resolution and overlaid on the structural features identified by the MR. However, the choices in how to reconstruct and visualize this information can have a dramatic impact on the feasibility of implementing this modality in the clinic. This is especially true in 3 dimensions, as there is often limited optical sampling of the breast tissue, and methods need to accurately reflect the tissue composition. In this paper, the implementation and display of fully 3D MR image-guided NIRS is outlined and demonstrated using in vivo data from three healthy women and a volunteer undergoing neoadjuvant chemotherapy. Additionally, a display feature presented here scales the transparency of the optical images to the sensitivity of the measurements, providing a logical way to incorporate partial volume sets of optical images onto the MR volume. These concepts are demonstrated with 3D data sets using Volview software online.

Image-guided Raman spectroscopic recovery of canine cortical bone contrast in situ.

Raman scattering provides valuable biochemical and molecular markers for studying bone tissue composition with use in predicting fracture risk in osteoporosis. Raman tomography can image through a few centimeters of tissue but is limited by low spatial resolution. X-ray computed tomography (CT) imaging can provide high-resolution image-guidance of the Raman spectroscopic characterization, which enhances the quantitative recovery of the Raman signals, and this technique provides additional information to standard imaging methods. This hypothesis was tested in data measured from Teflon tissue phantoms and from a canine limb. Image-guided Raman spectroscopy (IG-RS) of the canine limb using CT images of the tissue to guide the recovery recovered a contrast of 145:1 between the cortical bone and background. Considerably less contrast was found without the CT image to guide recovery. This study presents the first known IG-RS results from tissue and indicates that intrinsically high contrasts (on the order of a hundred fold) are available.

Noninvasive Raman tomographic imaging of canine bone tissue.

Raman spectroscopic diffuse tomographic imaging has been demonstrated for the first time. It provides a noninvasive, label-free modality to image the chemical composition of human and animal tissue and other turbid media. This technique has been applied to image the composition of bone tissue within an intact section of a canine limb. Spatially distributed 785-nm laser excitation was employed to prevent thermal damage to the tissue. Diffuse emission tomography reconstruction was used, and the location that was recovered has been confirmed by micro-computed tomography (micro-CT) images.

3D Multi-spectral Image-guided Near-infrared Spectroscopy using Boundary Element Method.

Image guided (IG) Near-Infrared spectroscopy (NIRS) has the ability to provide high-resolution metabolic and vascular characterization of tissue, with clinical applications in diagnosis of breast cancer. This method is specific to multimodality imaging where tissue boundaries obtained from alternate modalities such as MRI/CT, are used for NIRS recovery. IG-NIRS is severely limited in 3D by challenges such as volumetric meshing of arbitrary anatomical shapes and computational burden encountered by existing models which use finite element method (FEM). We present an efficient and feasible alternative to FEM using boundary element method (BEM). The main advantage is the use of surface discretization which is reliable and more easily generated than volume grids in 3D and enables automation for large number of clinical data-sets. The BEM has been implemented for the diffusion equation to model light propagation in tissue. Image reconstruction based on BEM has been tested in a multi-threading environment using four processors which provides 60% improvement in computational time compared to a single processor. Spectral priors have been implemented in this framework and applied to a three-region problem with mean error of 6% in recovery of NIRS parameters.

Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction.

Diffuse optical tomography, also known as near infrared tomography, has been under investigation, for non-invasive functional imaging of tissue, specifically for the detection and characterization of breast cancer or other soft tissue lesions. Much work has been carried out for accurate modeling and image reconstruction from clinical data. NIRFAST, a modeling and image reconstruction package has been developed, which is capable of single wavelength and multi-wavelength optical or functional imaging from measured data. The theory behind the modeling techniques as well as the image reconstruction algorithms is presented here, and 2D and 3D examples are presented to demonstrate its capabilities. The results show that 3D modeling can be combined with measured data from multiple wavelengths to reconstruct chromophore concentrations within the tissue. Additionally it is possible to recover scattering spectra, resulting from the dominant Mie-type scatter present in tissue. Overall, this paper gives a comprehensive over view of the modeling techniques used in diffuse optical tomographic imaging, in the context of NIRFAST software package.