Brian W. Pogue

Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results

Three-dimensional (3D), multiwavelength near-infrared tomography has the potential to provide new physiological information about biological tissue function and pathological transformation. Fast and reliable measurements of multiwavelength data from multiple planes over a region of interest, together with adequate model-based nonlinear image reconstruction, form the major components of successful estimation of internal optical properties of the region. These images can then be used to examine the concentration of chromophores such as hemoglobin, deoxyhemoglobin, water, and lipids that in turn can serve to identify and characterize abnormalities located deep within the domain. We introduce and discuss a 3D modeling method and image reconstruction algorithm that is currently in place. Reconstructed images of optical properties are presented from simulated data, measured phantoms, and clinical data acquired from a breast cancer patient. It is shown that, with a relatively fast 3D inversion algorithm, useful images of optical absorption and scatter can be calculated with good separation and localization in all cases. It is also shown that, by use of the calculated optical absorption over a range of wavelengths, the oxygen saturation distribution of a tissue under investigation can be deduced from oxygenated and deoxygenated hemoglobin maps. With this method the reconstructed tumor from the breast cancer patient was found to have a higher oxy-deoxy hemoglobin concentration and also a higher oxygen saturation level than the background, indicating a ductal carcinoma that corresponds well to histology findings.

Automated region detection based on the contrast-to-noise ratio in near-infrared tomography

The contrast-to-noise ratio (CNR) was used to determine the detectability of objects within reconstructed images from diffuse near-infrared tomography. It was concluded that there was a maximal value of CNR near the location of an object within the image and that the size of the true region could be estimated from the CNR. Experimental and simulation studies led to the conclusion that objects can be automatically detected with CNR analysis and that our current system has a spatial resolution limit near 4 mm and a contrast resolution limit near 1.4. A new linear convolution method of CNR calculation was developed for automated region of interest (ROI) detection.

Spectroscopic diffuse optical tomography for the quantitative assessment of hemoglobin concentration and oxygen saturation in breast tissue

Near-infrared (NIR) spectroscopic diffuse tomography has been used to map the hemoglobin concentration and the hemoglobin oxygen saturation quantitatively in tissuelike phantoms and to determine average values in vivo. A series of phantom calibrations were performed to achieve quantitatively accurate images of the absorption and the reduced scattering coefficients at multiple optical wavelengths. A least-squares fit was applied to absorption-coefficient images at multiple NIR wavelengths to obtain hemoglobin images of the concentration and the hemoglobin oxygen saturation. Objects of varying hemoglobin concentration and oxygen saturation within highly scattering media were localized and imaged to within 15% of their actual values. The average hemoglobin concentration and oxygen saturation of breast tissue was measured in vivo for two women volunteers. The potential application for the diagnosis of breast tumors is discussed.

Superficial dosimetry imaging based on Čerenkov emission for external beam radiotherapy with megavoltage x-ray beam

PURPOSEnČerenkov radiation emission occurs in all tissue, when charged particles (either primary or secondary) travel at velocity above the threshold for the Čerenkov effect (about 220 KeV in tissue for electrons). This study presents the first examination of optical Čerenkov emission as a surrogate for the absorbed superficial dose for MV x-ray beams.nnnMETHODSnIn this study, Monte Carlo simulations of flat and curved surfaces were studied to analyze the energy spectra of charged particles produced in different regions near the surfaces when irradiated by MV x-ray beams. Čerenkov emission intensity and radiation dose were directly simulated in voxelized flat and cylindrical phantoms. The sampling region of superficial dosimetry based on Čerenkov radiation was simulated in layered skin models. Angular distributions of optical emission from the surfaces were investigated. Tissue mimicking phantoms with flat and curved surfaces were imaged with a time domain gating system. The beam field sizes (50 × 50-200 × 200 mm(2)), incident angles (0°-70°) and imaging regions were all varied.nnnRESULTSnThe entrance or exit region of the tissue has nearly homogeneous energy spectra across the beam, such that their Čerenkov emission is proportional to dose. Directly simulated local intensity of Čerenkov and radiation dose in voxelized flat and cylindrical phantoms further validate that this signal is proportional to radiation dose with absolute average discrepancy within 2%, and the largest within 5% typically at the beam edges. The effective sampling depth could be tuned from near 0 up to 6 mm by spectral filtering. The angular profiles near the theoretical Lambertian emission distribution for a perfect diffusive medium, suggesting that angular correction of Čerenkov images may not be required even for curved surface. The acquisition speed and signal to noise ratio of the time domain gating system were investigated for different acquisition procedures, and the results show there is good potential for real-time superficial dose monitoring. Dose imaging under normal ambient room lighting was validated, using gated detection and a breast phantom.nnnCONCLUSIONSnThis study indicates that Čerenkov emission imaging might provide a valuable way to superficial dosimetry imaging in real time for external beam radiotherapy with megavoltage x-ray beams.

Sampling of time- and frequency-domain signals in Monte Carlo simulations of photon migration

We compare two fundamentally different ways to evaluate the time dependence in Monte Carlo simulations of photon migration: estimating the pulse response in time versus evaluating the transfer function at discrete points in the frequency domain. We show that these two methods differ in accuracy owing to quantization and sampling errors, whereas the statistical error is essentially the same for both methods. From our analysis we also derive alternative methods to sample the time-domain pulse response with reduced quantization and sampling error. Simulation results are included to illustrate our theoretical analysis.

Verteporfin heterogeneity in pancreatic adenocarcinoma and the relationship to tumor vasculature and collagen distribution

Photodynamic therapy (PDT) has emerged as one promising treatment regimen for several cancer types, with a clinical trial ongoing in pancreatic adenocarcinoma (PDAC). PDT treatment efficacy mainly depends on the combination of light delivery, oxygen availability and photosensitizer uptake, each of which can be limited in pancreas cancer. Therefore, increasing drug uptake in the tumor would make an important impact on treatment outcome. This study was conducted to focus on the issue with drug resistance by examining the relationship between photosensitizer verteporfin and tissue parameters such as collagen and vascular patency. Verteporfin uptake in the tumors was assessed by fluorescence imaging while collagen content and patent vessel area fraction were quantified by evaluating Masson’s Trichrome and Lectin pathology staining images. Two tumor cell lines – AsPC-1 and BxPC-3 – were modeled in nude mice to investigate the impact of different tumor microenvironments. Experimental results highlighted the correlation between vascular patency and verteporfin uptake. Collagen content was found to be an independent factor within each tumor line, but a comparison across two tumor types suggested that collagen area of greater than 10% of tumor cross section reflected a lower verteporfin uptake. It was observed that whole-slice tumor quantifications have showcased some interesting trends which could be greatly enhanced and further supported by regional analysis.

Cherenkov imaging for Total Skin Electron Therapy (TSET)

Total Skin Electron Therapy (TSET) utilizes high-energy electrons to treat cancers on the entire body surface. The otherwise invisible radiation beam can be observed via the optical Cherenkov photons emitted from interaction between the high-energy electron beam and tissue. Using a specialized camera-system, the Cherenkov emission can thus be used to evaluate the dose uniformity on the surface of the patient in real-time. Each patient was also monitored during TSET via in-vivo detectors (IVD) in nine locations. Patients undergoing TSET in various conditions (whole body and half body) were imaged and analyzed, and the viability of the system to provide clinical feedback was established.

Structural Cherenkov luminescence imaging with Hadamard-patterned field illumination (Conference Presentation)

Cherenkov-excited luminescence scanned imaging (CELSI) has been proposed for radiation-dose determination in medical physics due to its high spatial-resolution over centimeters of tissue. However, dense line-scanning illumination in typical CELSI is time-cost owing to the mechanical movement of the leaves in multi leaf collimator (MLC), resulting into increased radiation exposure. As a result, a scanningless Cherenkov luminescence imaging modality is herein proposed through structuring epi-illumination with MLC-based Hadamard-patterns, which utilizes a reduced radiation does by limiting illumination patterns, extremely shortening the sampling process. In order to effectively reconstruct unknowns from the resultant underdetermined linear system with sparse samplings, a compressed sensing-based reconstruction methodology with l1-norm regularization is adopted. Numerical and phantom experiments show that the proposed methodology achieves the same image quality as the traditional CELSI does.

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.