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X-ray induced acoustic computed tomography (XACT) provides X-ray absorption based contrast with acoustic detection. For its clinical translation, XACT imaging often has a limited field of view. This can result in image artifacts and overall loss of quantification accuracy. In this article, we aim to demonstrate model-based XACT image reconstruction to address these problems. An efficient matrix-free implementation of the regularized LSQR (MF-LSQR) based minimization scheme and a non-iterative model back-projection (MBP) scheme for computing XACT reconstructions have been demonstrated in this paper. The proposed algorithms have been numerically validated and then employed to perform reconstructions from experimental measurements obtained from an XACT setup. While the commonly used back-projection algorithm produces limited-view and noisy artifacts in the region of interest, model-based LSQR minimization overcomes these issues. The model based algorithms also reduce the ring artifacts caused due to the non-uniformity response of the multichannel data acquisition. Using the model-based reconstruction algorithms, we are able to obtain reasonable XACT reconstructions for acoustic measurements of up to 120° view. Although the MBP is more efficient than the model-based LSQR algorithm, it provides only the structural information of the region of interest. Overall, it has been demonstrated that the model-based image reconstruction yields better image quality for XACT than the standard back-projection. Moreover, the combination of model-based image reconstruction with different regularization methods can solve the limited view problem for XACT imaging (in many realistic cases where the full-view dataset is unavailable) and hence pave the way for the future clinical translation.

Summary High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful tool to elucidate developmental programs directing cell lineages. We introduce tSpace, an algorithm based on the concept of “trajectory space”, in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. Graphical mapping of cells in trajectory space allows unsupervised reconstruction and exploration of complex developmental sequences. Applied to flow and mass cytometry data, the method faithfully reconstructs thymic T cell development and reveals development and trafficking regulation of tonsillar B cells. Applied to the single cell transcriptome of mouse intestine and C. elegans, the method recapitulates development from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms and orders C. elegans cells concordantly to the associated embryonic time. tSpace profiling of complex populations is well suited for hypothesis generation in developing cell systems., Graphical Abstract, Highlights • tSpace: trajectory analysis agnostic to biological systems and technology platforms • tSpace faithfully reconstructs complex developmental trees within one or more tissues • tSpace reveals rare transient cells usually missed by trajectory inference algorithms • tSpace confirms known and unveils novel biology in human B cells and mouse intestine, Developmental Biology; Systems Biology; In Silico Biology

High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful means to elucidate developmental sequences and define genetic programs directing cell lineages. Here we introduce tSpace, an algorithm based on the concept of “trajectory space”, in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. tSpace outputs a dense matrix of cell-to-cell distances that quantitatively reflect the extent of phenotypic change along developmental paths (developmental distances). Graphical mapping of cells in trajectory space allows unsupervised reconstruction and straightforward exploration of complex developmental sequences. tSpace is robust, scalable, and implements a global approach that attempts to preserve both local and distant relationships in developmental pathways. Applied to high dimensional flow and mass cytometry data, the method faithfully reconstructs known pathways of thymic T cell development and provides novel insights into regulation of tonsillar B cell development and trafficking. Applied to single cell transcriptomic data, the method unfolds complex developmental sequences, recapitulates pathways leading from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms, and reveals genetic programs that correlate with fate decisions. tSpace profiling of complex populations in high-dimensional trajectory space is well suited for hypothesis generation in developing cell systems.