Spectral images from photon counting detectors are being explored for material decomposition applications such as for obtaining quantitative maps of tissue types and contrast agents. While these detectors allow acquisition of multi-energy data in a single exposure, separating the total photon counts into multiple energy bins can lead to issues of count starvation and increased quantum noise in resultant maps. Furthermore, the complex decomposition problem is often solved in a single inversion step making it difficult to separate materials with close properties. We propose a multi-step decomposition method which allows solving the problem in multiple steps using the same spectral data collected in a single exposure. During each step, quantitative accuracy of a single material is under focus and one can flexibly optimize the bins chosen in that step. The result thus obtained becomes part of the input data for the next step in the multi-step process. This makes the problem less ill-conditioned and allows better quantitation of more challenging materials within the object. In comparison to a conventional single-step method, we show excellent quantitative accuracy for decomposing up to six materials involving a mix of soft tissue types and contrast agents in micro-CT sized digital phantoms.

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image texture, the relative spatial arrangement of intensity values in an image, encodes valuable information about the scene. As it stands, much of this potential information remains untapped. Understanding how to decipher textural details would afford another method of extracting knowledge of the physical world from images. In this work, we attempt to bridge the gap in research between quantitative texture analysis and the visual perception of textures. the impact of changes in image texture on human observer's ability to perform signal detection and localization tasks in complex digital images is not understood. We examine this critical question by studying task-based human observer performance in detecting and localizing signals in tomographic breast images. We have also investigated how these changes impact the formation of second-order image texture. We used digital breast tomosynthesis (DBT) an FDA approved tomographic X-ray breast imaging method as the modality of choice to show our preliminary results. our human observer studies involve localization ROC (LROC) studies for low contrast mass detection in DBT. Simulated images are used as they offer the benefit of known ground truth. Our results prove that changes in system geometry or processing leads to changes in image texture magnitudes. We show that the variations in several well-known texture features estimated in digital images correlate with human observer detection-localization performance for signals embedded in them. This insight can allow efficient and practical techniques to identify the best imaging system design and algorithms or filtering tools by examining the changes in these texture features. This concept linking texture feature estimates and task based image quality assessment can be extended to several other imaging modalities and applications as well. it can also offer feedback in system and algorithm designs with a goal to improve perceptual benefits. Broader impact can be in wide array of areas including imaging system design, image processing, data science, machine learning, computer vision, perceptual and vision science. our results also point to the caution that must be exercised in using these texture features as image-based radiomic features or as predictive markers for risk assessment as they are sensitive to system or image processing changes.

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