Advances in imaging and early cancer detection have increased the interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. For certain cancers, MRgFUS treatment could reduce surgical risks, preserve organ tissue/function, and improve patient quality of life. However, surgical resection and histological analysis remain the gold standard to assess cancer treatment response. For non-invasive ablation therapies such as MRgFUS, the treatment response must be determined through MR imaging biomarkers. However, the accuracy of current MR biomarkers is inconclusive and these biomarkers have not been rigorously evaluated against histology via accurate registration. Previous MR to histology registration methods require direct MR and histology correlation and are not sufficient for many MRgFUS applications. New registration methods are needed that are independent of direct MR and histology correlation to facilitate evaluating MRgFUS imaging biomarkers.  We present a novel, multi-step MR to histology registration workflow that utilizes correlations between intermediate imaging steps to perform MR to histology registration for validating biomarkers. The developed registration pipeline is used to evaluate a common MRgFUS treatment assessment biomarker against histology. Evaluating MR biomarkers against histopathology using this registration pipeline will facilitate validating novel MRgFUS biomarkers to improve treatment assessment without surgical intervention. 

Matched filter techniques have been used for retrieval of greenhouse gas concentration from imaging spectroscopy datasets. While multiple algorithmic techniques and refinements have been proposed, the provided signal that a matched filter detects has remained largely unaltered since the introduction of the matched filter technique for gas concentration retrieval in imaging spectroscopy. A library of laboratory absorption measurements for gases has been the most used input search signal. Laboratory measurements do not account for atmospheric and geometric parameters which affect radiative transfer. We introduce atmospheric and geometric parameters into the generation of a scene-specific unit absorption spectrum which is compatible with all matched filter techniques. Specifically, we model four parameters that are expected to change between scenes: solar zenith angle, column water vapor, ground altitude, and sensor altitude. These parameter values are well defined, with low variation of these parameters within a single scene. This scene-specific target spectrum affects gas retrieval concentrations, and thereby integrated mass enhancements and estimated source flux, versus results using the generic-library spectrum. Scene-specific spectra improve confidence of retrieved gas content when operating imaging spectroscopy in diverse conditions.