论文标题
数据一致的局部医学成像局部分辨率
Data-Consistent Local Superresolution for Medical Imaging
论文作者
论文摘要
在这项工作中，我们提出了一个新的基于迭代模型的重建算法的范式，用于为缩放和完善医学和临床层析成像（例如CT/MRI/PET等）图像的实时解决方案提供实时解决方案。该算法框架是针对医学成像实践中的临床需求量身定制的，在重建完整的层析成像图像之后，临床医生可能会认为，图像的某些关键部分还不够清晰，并且希望看到更清晰的这些区域。一种天真的方法（不建议使用）将执行更高分辨率图像的全局重建，该图像具有两个主要局限性：首先，它在计算上效率低下，其次，图像正则化仍然是全球应用的，这可能会超过某些地方区域。此外，如果一个人希望微调本地零件的正则化参数，那么对于使用全局重建的情况下，它在实际上是不可行的。我们针对此类任务的新迭代方法是基于共同利用测量信息，有效的跨图像空间的升压/下采样，并在当地调整的图像以前进行了有效且高质量的后处理。低剂量X射线CT图像局部变焦的数值结果证明了我们方法的有效性。
In this work we propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest in medical and clinical tomographic (such as CT/MRI/PET, etc) images. This algorithmic framework is tailor for a clinical need in medical imaging practice, that after a reconstruction of the full tomographic image, the clinician may believe that some critical parts of the image are not clear enough, and may wish to see clearer these regions-of-interest. A naive approach (which is highly not recommended) would be performing the global reconstruction of a higher resolution image, which has two major limitations: firstly, it is computationally inefficient, and secondly, the image regularization is still applied globally which may over-smooth some local regions. Furthermore if one wish to fine-tune the regularization parameter for local parts, it would be computationally infeasible in practice for the case of using global reconstruction. Our new iterative approaches for such tasks are based on jointly utilizing the measurement information, efficient upsampling/downsampling across image spaces, and locally adjusted image prior for efficient and high-quality post-processing. The numerical results in low-dose X-ray CT image local zoom-in demonstrate the effectiveness of our approach.
