We have developed a novel parallel-plate diffuse optical tomography (DOT) system for three-dimensional in vivo imaging of human breast tumor based on large optical data sets. Images of oxy-, deoxy-, and total hemoglobin concentration as well as blood oxygen saturation and tissue scattering were reconstructed. Tumor margins were derived using the optical data with guidance from radiology reports and magnetic resonance imaging. Tumor-to-normal ratios of these endogenous physiological parameters and an optical index were computed for 51 biopsy-proven lesions from 47 subjects. Malignant cancers (N=41) showed statistically significant higher total hemoglobin, oxy-hemoglobin concentration, and scattering compared to normal tissue. Furthermore, malignant lesions exhibited a twofold average increase in optical index. The influence of core biopsy on DOT results was also explored; the difference between the malignant group measured before core biopsy and the group measured more than 1 week after core biopsy was not significant. Benign tumors (N=10) did not exhibit statistical significance in the tumor-to-normal ratios of any parameter. Optical index and tumor-to-normal ratios of total hemoglobin, oxy-hemoglobin concentration, and scattering exhibited high area under the receiver operating characteristic curve values from 0.90 to 0.99, suggesting good discriminatory power. The data demonstrate that benign and malignant lesions can be distinguished by quantitative three-dimensional DOT.
We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients.
In this paper, we describe a novel clinical breast diffuse optical tomography (DOT) instrument for CW and RF data acquisition in transmission geometry. It is designed to be able to acquire a massive amount of data in a short amount of time available for patient measurement by using a 209-channel galvo-based fast optical switch
and a fast electron-multiplying CCD. In addition to CW measurements, RF measurements were made by using an electro-optic modulator for source modulation and a gain-modulated image intensifier for detection. The patient bed has many clinically-oriented features as well as improved data acquisition rate and transmission RF
measurement capability. A series of preliminary results will be shown, including a heterodyne RF experiment
for bulk property measurement and a CW experiment for 3D imaging. In order to deal with large data size, a
linear reconstruction algorithm that exploits separability of the inverse problem in Fourier domain is used for
fast and memory-load-free reconstruction.
We have developed a novel method for combining non-concurrent MR and DOT data, which integrates advanced
multimodal registration and segmentation algorithms within a well-defined workflow. The method requires little user
interaction, is computationally efficient for practical applications, and enables joint MR/DOT analysis. The method
presents additional advantages: More flexibility than integrated MR/DOT imaging systems, The ability to independently
develop a standalone DOT system without the stringent limitations imposed by the MRI device environment, Enhancement
of sensitivity and specificity for breast tumor detection, Combined analysis of structural and functional data,
Enhancement of DOT data reconstruction through the use of MR-derived a priori structural information. We have
conducted an initial patient study which asks an important question: how can functional information on a tumor
obtained from DOT data be combined with the anatomy of that tumor derived from MRI data? The study confirms that
tumor areas in the patient breasts exhibit significantly higher total hemoglobin concentration (THC) than their
surroundings. The results show significance in intra-patient THC variations, and justify the use of our normalized
difference measure defined as the distance from the average THC inside the breast, to the average THC inside the tumor
volume in terms of the THC standard deviation inside the breast. This method contributes to the long-term goal of
enabling standardized direct comparison of MRI and DOT and facilitating validation of DOT imaging methods in
clinical studies.
We have developed a software platform for multimodal integration and visualization of diffuse optical tomography and magnetic resonance imaging. Novel registration and segmentation algorithms have been integrated into the platform. The multimodal registration technique enables the alignment of non-concurrently acquired MR and DOT breast data. The non-rigid registration algorithm uses two-dimensional signatures (2D digitally reconstructed radiographs) of the reference and moving volumes in order to register them. Multiple two-dimensional signatures can robustly represent the volume depending on the way signatures are generated. An easy way to conceptualize the idea is to understand the motion of an object by tracking three perpendicular shadows of the object. The breast MR image segmentation
technique enables a priori structural information derived from MRI to be incorporated into the reconstruction of DOT data. The segmentation algorithm is based on "Random walkers". Both registration and segmentation algorithms were tested and have shown promising results. The average Target Registration Error (TRE) for phantom models simulating the large breast compression differences was always below 5%. Tests on patient datasets also showed satisfying visual results. Several tests were also conducted for segmentation assessment and results have shown high quality MR breast image segmentation.
We have developed a software platform for multimodal integration and visualization of diffuse optical tomography (DOT) and magnetic resonance imaging (MRI) of breast cancer. The image visualization platform allows multimodality 3D image visualization and manipulation of datasets, such as a variety of 3D rendering technique, and the ability to simultaneously control multiple fields of view. This platform enables quantitative and qualitative analysis of structural and functional diagnostic data, using both conventional & molecular imaging. The functional parameters, together with morphological parameters from MR can be suitably combined and correlated to the absolute diagnosis from histopathology. Fusion of the multimodal datasets will eventually lead to a significant improvement in the sensitivity and specificity of breast cancer detection. Fusion may also allow a priori structural information derived from MRI to be incorporated into the reconstruction of diffuse optical tomography images. We will present the early results of image visualization and registration on multimodal breast cancer data, DOT and MRI.
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