Abstract
Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views. Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I$^3$Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution. The through-plane branch supplements the limited information contained in low through-plane resolution from high in-plane resolution and enables continual and diverse feature learning. In-plane branch transforms features to the frequency domain and enforces an equal learning opportunity for all frequency bands in a global context learning paradigm. We further propose a cross-view block to take advantage of the information from all three views online. Extensive experiments on two public datasets demonstrate the effectiveness of I$^3$Net, and noticeably outperforms state-of-the-art super-resolution, video frame interpolation and slice interpolation methods by a large margin. We achieve 43.90dB in PSNR, with at least 1.14dB improvement under the upscale factor of $\times$2 on MSD dataset with faster inference. Code is available at this https URL.
Abstract (translated)
医学影像受到采集时间和扫描设备的限制。使用更厚的切片进行CT和MR体积重建时,它们在平面分辨率和通过分辨率方面存在非均匀性。我们揭示了由于数据提及的性质,从轴向视图中进行切片级插值会比从其他视图执行超分辨率带来更大的好处。根据这个观察结果,我们提出了一个跨切片内插网络(I$^3$Net),它完全探索高平面分辨率和低通过分辨率的信息,并弥补低通过分辨率从高平面分辨率中的有限信息。通过平面分支将特征变换到频域,并确保全局上下文学习范式中所有频率带之间的平等学习机会。我们进一步提出了一个跨视图分支,以利用所有三个视图的信息。在两个公开数据集上的广泛实验证明,I$^3$Net的有效性非常显著,明显超过了最先进的超分辨率、视频帧插值和切片插值方法。在放大因子为$\times$2的MSD数据集上,其性能提高了43.90dB,并且在提高因子为$\times$2时,其性能显著超过了最先进的超分辨率方法。代码可在此处下载:https://www.github.com/your_username/I3Net_Experiment。
URL
https://arxiv.org/abs/2405.02857
