Abstract
We recently elevated interior tomography from its origin in computed tomography (CT) to a general tomographic principle, and proved its validity for other tomographic modalities including SPECT, MRI, and others. Here we propose “omni-tomography”, a novel concept for the grand fusion of multiple tomographic modalities for simultaneous data acquisition in a region of interest (ROI). Omni-tomography can be instrumental when physiological processes under investigation are multi-dimensional, multi-scale, multi-temporal and multi-parametric. Both preclinical and clinical studies now depend on in vivo tomography, often requiring separate evaluations by different imaging modalities. Over the past decade, two approaches have been used for multimodality fusion: Software based image registration and hybrid scanners such as PET-CT, PET-MRI, and SPECT-CT among others. While there are intrinsic limitations with both approaches, the main obstacle to the seamless fusion of multiple imaging modalities has been the bulkiness of each individual imager and the conflict of their physical (especially spatial) requirements. To address this challenge, omni-tomography is now unveiled as an emerging direction for biomedical imaging and systems biomedicine.
Highlights
The physiome concept was first presented to the International Union of Physiological Sciences (IUPS) in 1993, and later designated as a strategic area by IUPS in 2001 [1,2,3]
We have systematically analyzed a number of architectures for omnitomography, and realized that each has advantages and disadvantages
All the major tomographic modalities are incorporated into three rings: a C-arm-like magnet; a middle ring containing an x-ray tube and a detector array, and a pair of SPECT detectors; and an outer ring for PET
Summary
The physiome concept was first presented to the International Union of Physiological Sciences (IUPS) in 1993, and later designated as a strategic area by IUPS in 2001 [1,2,3]. Current x-ray CT produces a limited amount of information from gray-scale images based on differences in linear attenuation coefficients of various tissues. An information explosion is seen from genetic and epigenetic profiling This imbalance between phenotypic information (e.g., CT images) and genome-level information (e.g., RNA data) demands more capabilities from the in vivo imaging side. Turning again to x-ray CT as an example, the transition has started from gray-scale to true-color images with energy-sensitive, photon-counting detector technology [6]. Another area of advancement is x-ray phase-contrast and dark-field imaging [7,8]. Both imaging modalities and contrast agents are being rapidly improved
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