Spectral derivative based image reconstruction provides inherent insensitivity to coupling and geometric errors

Evaluation of a polarization-enhanced laparoscopy prototype for improved intra-operative visualization of peritoneal metastases

Despite careful staging, the accuracy for preoperative detection of small distant metastases remains poor, creating a clinical need for enhanced operative staging to detect occult peritoneal metastases. This study evaluates a polarization-enhanced laparoscopy (PEL) prototype and assesses its potential for label-free contrast enhancement of peritoneal metastases. This is a first-in-human feasibility study, including 10 adult patients who underwent standard staging laparoscopy (SSL) for gastrointestinal malignancy along with PEL. Image frames of all detectable peritoneal lesions underwent analysis. Using Monte Carlo simulations, contrast enhancement based on the color dependence of PEL (mPEL) was assessed. The prototype performed safely, yet with limitations in illumination, fogging of the distal window, and image co-registration. Sixty-five lesions (56 presumed benign and 9 presumed malignant) from 3 patients represented the study sample. While most lesions were visible under human examination of both SSL and PEL videos, more lesions were apparent using SSL. However, this was likely due to reduced illumination under PEL. When controlling for such effects through direct comparisons of integrated (WLL) vs differential (PEL) polarization laparoscopy images, we found that PEL imaging yielded an over twofold Weber contrast enhancement over WLL. Further, enhancements in the discrimination between malignant and benign lesions were achieved by exploiting the PEL color contrast to enhance sensitivity to tissue scattering, influenced primarily by collagen. In conclusion, PEL appears safe and easy to integrate into the operating room. When controlling for the degree of illumination, image analysis suggested a potential for mPEL to provide improved visualization of metastases.

An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis

The ultrasound-guided diffuse optical tomography is a noninvasive imaging technique for breast cancer diagnosis and treatment monitoring. The technique uses a handheld probe capable of providing measurements of multiple wavelengths in a few seconds. These measurements are used to estimate optical absorptions of lesions and calculate the total hemoglobin concentration. Any measurement errors caused by low signal to noise ratio data and/or movements during data acquisition would reduce the accuracy of reconstructed total hemoglobin concentration. In this article, we introduce an automated preprocessing method that combines data collected from multiple sets of lesion measurements of 4 optical wavelengths to detect and correct outliers in the perturbation. Two new measures of correlation between each pair of wavelength measurements and a wavelength consistency index of all reconstructed absorption maps are introduced. For phantom and patients' data without evidence of measurement errors, the correlation coefficient between each pair of wavelength measurements was above 0.6. However, for patients with measurement errors, the correlation coefficient was much lower. After applying the correction method to 18 patients' data with measurement errors, the correlation has improved and the wavelength consistency index is in the same range as the cases without wavelength-dependent measurement errors. The results show an improvement in classification of malignant and benign lesions.

Quantitative bioluminescence tomography using spectral derivative data

Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new treatments and therapies. For many applications, the quantitative assessment of accurate cellular activity and spatial distribution is desirable as it would enable direct monitoring for prognostic evaluation. This requires quantitative spatially-resolved measurements of bioluminescence source strength inside the animal to be obtained from BLI images. This is the goal of bioluminescence tomography (BLT) in which a model of light propagation through tissue is combined with an optimization algorithm to reconstruct a map of the underlying source distribution. As most models consider only the propagation of light from internal sources to the animal skin surface, an additional challenge is accounting for the light propagation from the skin to the optical detector (e.g. camera). Existing approaches typically use a model of the imaging system optics (e.g. ray-tracing, analytical optical models) or approximate corrections derived from calibration measurements. However, these approaches are typically computationally intensive or of limited accuracy. In this work, a new approach is presented in which, rather than directly using BLI images acquired at several wavelengths, the spectral derivative of that data (difference of BLI images at adjacent wavelengths) is used in BLT. As light at similar wavelengths encounters a near-identical system response (path through the optics etc.) this eliminates the need for additional corrections or system models. This approach is applied to BLT with simulated and experimental phantom data and shown that the error in reconstructed source intensity is reduced from 49% to 4%. Qualitatively, the accuracy of source localization is improved in both simulated and experimental data, as compared to reconstruction using the standard approach. The outlined algorithm can widely be adapted to all commercial systems without any further technological modifications.

Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Diffuse optical tomography is highly sensitive to measurement and modeling errors. Errors in the source and detector coupling and positions can cause significant artifacts in the reconstructed images. Recently the approximation error theory has been proposed to handle modeling errors. In this article, we investigate the feasibility of the approximation error approach to compensate for modeling errors due to inaccurately known optode locations and coupling coefficients. The approach is evaluated with simulations. The results show that the approximation error method can be used to recover from artifacts in reconstructed images due to optode coupling and position errors.

Compensation of optode sensitivity and position errors in diffuse optical tomography using the approximation error approach

Diffuse optical tomography is highly sensitive to measurement and modeling errors. Errors in the source and detector coupling and positions can cause significant artifacts in the reconstructed images. Recently the approximation error theory has been proposed to handle modeling errors. In this article, we investigate the feasibility of the approximation error approach to compensate for modeling errors due to inaccurately known optode locations and coupling coefficients. The approach is evaluated with simulations. The results show that the approximation error method can be used to recover from artifacts in reconstructed images due to optode coupling and position errors.

Application of spectral derivative data in visible and near-infrared spectroscopy

The use of the spectral derivative method in visible and near-infrared optical spectroscopy is presented, whereby instead of using discrete measurements around several wavelengths, the difference between nearest neighbouring spectral measurements is utilized. The proposed technique is shown to be insensitive to the unknown tissue and fibre contact coupling coefficients providing substantially increased accuracy as compared to more conventional techniques. The self-calibrating nature of the spectral derivative techniques increases its robustness for both clinical and industrial applications, as is demonstrated based on simulated results as well as experimental data.

Quantitative imaging of scattering changes associated with epithelial proliferation, necrosis, and fibrosis in tumors using microsampling reflectance spectroscopy

Highly localized reflectance measurements can be used to directly quantify scatter changes in tissues. We present a microsampling approach that is used to raster scan tumors to extract parameters believed to be related to the tissue ultrastructure. A confocal reflectance imager was developed to examine scatter changes across pathologically distinct regions within tumor tissues. Tissue sections from two murine tumors, AsPC-1 pancreas tumor and the Mat-LyLu Dunning prostate tumor, were imaged. After imaging, histopathology-guided region-of-interest studies of the images allowed analysis of the variations in scattering resulting from differences in tissue ultra-structure. On average, the median scatter power of tumor cells with high proliferation index (HPI) was about 26% less compared to tumor cells with low proliferation index (LPI). Necrosis exhibited the lowest scatter power signature across all the tissue types considered, with about 55% lower median scatter power than LPI tumor cells. Additionally, the level and maturity of the tumor's fibroplastic response was found to influence the scatter signal. This approach to scatter visualization of tissue ultrastructure in situ could provide a unique tool for guiding surgical resection, but this kind of interpretation into what the signal means relative to the pathology is required before proceeding to clinical studies.

Developments in quantitative oxygen-saturation imaging of breast tissue in vivo using multispectral near-infrared tomography

Imaging of oxygen saturation provides a spatial map of the tissue metabolic activity and has potential in diagnosis and treatment monitoring of breast cancer. Oxygen-saturation imaging is possible through near-infrared (NIR) tomography, but has low signal-to-noise ratio (SNR). This can be augmented by using NIR tomography as an add-on to MRI. Presented are results from a free-standing NIR system and a hybrid MR-guided system for breast imaging. In results from imaging 60 healthy volunteers in the initial NIR system, oxygen saturation was a significant discriminator between the BIRADS classifications of adipose tissue, heterogeneously dense, and extremely dense tissue. By using the MR-guided NIR system, more accurate tissue-specific data were obtained on adipose and fibroglandular volumes, with 11 healthy volunteers. In these data, oxygen saturation in the adipose tissue correlated with percentage of adipose tissue. In two case studies of infiltrating ductal carcinomas, oxygen saturation was reduced at the site of the tumor, as compared with the surrounding healthy tissue, agreeing with conventional thought that hypoxia exists in larger solid tumors. The MRI-guided NIR images of oxygen saturation provide higher resolution and superior SNR and will likely be used in the future to study and characterize specific tissue volumes.

Numerical modelling errors in electrical impedance tomography

Electrical impedance tomography (EIT) is a non-invasive technique that aims to reconstruct images of internal impedance values of a volume of interest, based on measurements taken on the external boundary. Since most reconstruction algorithms rely on model-based approximations, it is important to ensure numerical accuracy for the model being used. This work demonstrates and highlights the importance of accurate modelling in terms of model discretization (meshing) and shows that although the predicted boundary data from a forward model may be within an accepted error, the calculated internal field, which is often used for image reconstruction, may contain errors, based on the mesh quality that will result in image artefacts.