Confidence maps and confidence intervals for near infrared images in breast cancer

Statistical Hypothesis Testing for Postreconstructed and Postregistered Medical Images

Postreconstructed and postregistered medical images are typically treated as the raw data, implicitly assuming that those operations are error free. We question this assumption and explore how the precision of reconstruction and affine registration can be assessed by the image covariance matrix and confidence interval, called the confidence eigenimage, using a statistical model-based approach. Various hypotheses may be tested after image reconstruction and registration using classical statistical hypothesis testing vehicles: Is there a statistically significant difference between images? Does the intensity at a specific location or area of interest belong to the "normal" range? Is there a tumor? Does the image require rigid registration? We illustrate statistical hypothesis testing with three examples: breast computed tomography, breast near infrared linear reconstruction, and brain magnetic resonance imaging.

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.

Quantitative analysis of near-infrared tomography: sensitivity to the tissue-simulating precalibration phantom

A near-infrared (NIR) imaging system is evaluated as a diagnostic clinical tool to image total hemoglobin concentration and oxygen saturation within tissue. Calibration of this type of system requires measurement of the response at each detector and source location from a homogeneous tissue-simulating phantom. The effect of using calibration phantoms of varying composition, size, and optical properties is examined to determine how it affects the overall image accuracy. All of the calibration phantoms investigated result in accurate reconstruction of absorbing heterogeneities due to increased blood concentration with less than 4% standard deviation. Images from a patient with a biopsy-confirmed ductal carcinoma are also evaluated and found to be insensitive to the choice of calibration object, with only 1% variation between images generated with different calibration objects. The tumor total hemoglobin contrast is approximately 240% higher than the average total hemoglobin concentration in contralateral breast. Soft calibration phantoms, which mimic the elastic properties of human breast tissue, are also considered and found to diminish positioning errors in the fibers relative to the actual breast exam, thereby reducing the artifacts in the periphery of the reconstructed image.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.