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Abstract As computed tomography and related technologies have become mainstream tools across a broad range of scientific applications, each new generation of instrumentation produces larger volumes of more-complex 3D data. Lagging behind are step-wise improvements in computational methods to rapidly analyze these new large, complex datasets. Here we describe novel computational methods to capture and quantify volumetric information, and to efficiently characterize and compare shape volumes. It is based on innovative theoretical and computational reformulation of volumetric computing. It consists of two theoretical constructs and their numerical implementation: the spherical wave decomposition ( SWD ), that provides fast, accurate automated characterization of shapes embedded within complex 3D datasets; and symplectomorphic registration with phase space regularization by entropy spectrum pathways ( SYMREG ), that is a non-linear volumetric registration method that allows homologous structures to be correctly warped to each other or a common template for comparison. Together, these constitute the Shape Analysis for Phenomics from Imaging Data ( SAPID ) method. We demonstrate its ability to automatically provide rapid quantitative segmentation and characterization of single unique datasets, and both inter-and intra-specific comparative analyses. We go beyond pairwise comparisons and analyze collections of samples from 3D data repositories, highlighting the magnified potential our method has when applied to data collections. We discuss the potential of SAPID in the broader context of generating normative morphologies required for meaningfully quantifying and comparing variations in complex 3D anatomical structures and systems. 

Ectothermy and endothermy in extant fishes are defined by distinct integrated suites of characters. Although only ⁓0.1% of fishes are known to have endothermic capacity, recent discoveries suggest that there may still be uncommon pelagic fish species with yet to be discovered endothermic traits. Among the most rarely encountered marine fishes, the louvarLuvarus imperialisis a remarkable example of adaptive evolution as the only extant pelagic species in the order Acanthuriformes (including surgeonfishes, tangs, unicornfishes and Moorish idol). Magnetic resonance imaging and gross necropsy did not yield evidence of cranial or visceral endothermy but revealed a central‐posterior distribution of myotomal red muscle that is a mixture of the character states typifying ectotherms (lateral‐posterior) and red muscle endotherms (central‐anterior). Dissection of a specimen confirmed, and an osteological proxy supported, thatL. imperialishas not evolved the vascular rete that is vital to retaining heat in the red muscle. The combination of presumably relying on caudal propulsion while exhibiting internal red muscle without associated retia is unique toL. imperialisamong all extant fishes, raising the macroevolutionary question of whether this species – in geologic timescales – will remain an ectotherm or evolve red muscle endothermy.

 

A new method for enhancing the sensitivity of diffusion MRI (dMRI) by combining the data from single (sPFG) and double (dPFG) pulsed field gradient experiments is presented.

This method uses our JESTER framework to combine microscopic anisotropy information from dFPG experiments using a new method called diffusion tensor subspace imaging (DiTSI) to augment the macroscopic anisotropy information from sPFG data analyzed using our guided by entropy spectrum pathways method. This new method, called joint estimation diffusion imaging (JEDI), combines the sensitivity to macroscopic diffusion anisotropy of sPFG with the sensitivity to microscopic diffusion anisotropy of dPFG methods.

Its ability to produce significantly more detailed anisotropy maps and more complete fiber tracts than existing methods within both brain white matter (WM) and gray matter (GM) is demonstrated on normal human subjects on data collected using a novel fast, robust, and clinically feasible sPFG/dPFG acquisition.

The potential utility of this method is suggested by an initial demonstration of its ability to mitigate the problem of gyral bias. The capability of more completely characterizing the tissue structure and connectivity throughout the entire brain has broad implications for the utility and scope of dMRI in a wide range of research and clinical applications.