Gifani Parisa, Behnam Hamid, Haddadi Farzan, Sani Zahra Alizadeh, Shojaeifard Maryam
IEEE Trans Ultrason Ferroelectr Freq Control. 2016 Jan;63(1):6-19. doi: 10.1109/TUFFC.2015.2493881. Epub 2015 Oct 27.
A challenging issue for echocardiographic image interpretation is the accurate analysis of small transient motions of myocardium and valves during real-time visualization. A higher frame rate video may reduce this difficulty, and temporal super resolution (TSR) is useful for illustrating the fast-moving structures. In this paper, we introduce a novel framework that optimizes TSR enhancement of echocardiographic images by utilizing temporal information and sparse representation. The goal of this method is to increase the frame rate of echocardiographic videos, and therefore enable more accurate analyses of moving structures. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTCs) assessed for each pixel. We then designed both low-resolution and high-resolution overcomplete dictionaries based on prior knowledge of the temporal signals and a set of prespecified known functions. The IVTCs can then be described as linear combinations of a few prototype atoms in the low-resolution dictionary. We used the Bayesian compressive sensing (BCS) sparse recovery algorithm to find the sparse coefficients of the signals. We extracted the sparse coefficients and the corresponding active atoms in the low-resolution dictionary to construct new sparse coefficients corresponding to the high-resolution dictionary. Using the estimated atoms and the high-resolution dictionary, a new IVTC with more samples was constructed. Finally, by placing the new IVTC signals in the original IVTC positions, we were able to reconstruct the original echocardiography video with more frames. The proposed method does not require training of low-resolution and high-resolution dictionaries, nor does it require motion estimation; it does not blur fast-moving objects, and does not have blocking artifacts.
超声心动图图像解读面临的一个具有挑战性的问题是在实时可视化过程中对心肌和瓣膜的微小瞬态运动进行准确分析。更高帧率的视频可能会降低这一难度，而时间超分辨率（TSR）有助于呈现快速移动的结构。在本文中，我们引入了一种新颖的框架，通过利用时间信息和稀疏表示来优化超声心动图图像的TSR增强。该方法的目标是提高超声心动图视频的帧率，从而能够对移动结构进行更准确的分析。对于所提出的方法，我们首先通过提取针对每个像素评估的强度变化时间曲线（IVTC）来推导时间信息。然后，我们基于时间信号的先验知识和一组预先指定的已知函数设计了低分辨率和高分辨率的过完备字典。IVTC随后可以被描述为低分辨率字典中少数原型原子的线性组合。我们使用贝叶斯压缩感知（BCS）稀疏恢复算法来找到信号的稀疏系数。我们提取低分辨率字典中的稀疏系数和相应的活跃原子，以构建与高分辨率字典对应的新稀疏系数。利用估计的原子和高分辨率字典，构建了一个具有更多样本的新IVTC。最后，通过将新的IVTC信号放置在原始IVTC位置，我们能够重建具有更多帧的原始超声心动图视频。所提出的方法不需要训练低分辨率和高分辨率字典，也不需要运动估计；它不会模糊快速移动的物体，也不会产生块状伪影。
