OTHER FUNCTIONS AUTOMATED DATA SEGMENTATION TO ENABLE REAL-TIME IN VIVO 3-D IMAG

活体医学成像正在成为癌症和其他疾病的筛查、诊断、分期、治疗和监测中不可或缺的工具。然而，这些技术的全部潜力已经受到大数据集的图像处理时间和内存管理的严重限制。为了解决这一问题，诊断光子公司建议开发和分发一种数据分割/去分割方法和相应的算法，用于从大型多路复用数据集中重建图像，以优化速度和存储器使用。将物理学原理与计算机科学相结合，与包括改进的计算机硬件和软件编译器在内的竞争对手相比，这些算法将能够产生更快、更好的图像。这些算法可以在广泛的医学成像模式中实现，包括合成孔径技术和超声、MRI、光学量子点成像和光学相干断层成像等中的其他成像模式。除了医学之外，该方法的用途还扩展到国防应用，如合成孔径雷达。

In vivo medical imaging is becoming an indispensable tool in the screening, diagnosis, staging, treatment, and monitoring of cancer and a host of other diseases. However, the full potential of these technologies has been severely limited by image processing time and memory management of large data sets. In order to address this problem, Diagnostic Photonics, Inc. proposes to develop and distribute a data segmentation/ desegmentation method and corresponding algorithms for the reconstruction of images from large multiplexed data sets for optimized speed and memory usage. Utilizing principles of physics in conjunction with computer science, the algorithms will be able to produce faster and better images compared to their competition which includes improved computer hardware and software compilers. These algorithms can be implemented across a wide array of medical imaging modalities including synthetic aperture techniques and other imaging modes in ultrasound, MRI, optical quantum dot imaging and optical coherence tomography among others. Beyond medicine, the utility of the method extends to defense applications such as synthetic aperture radar.