We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 140k unique images annotated with ground truth by human raters who received minimal instruction. The resulting metric shows competitive performance on TID 2013, a database widely used to assess image quality assessments methods. More interestingly, it shows strong responses to objects potentially carrying semantic relevance such as faces and text, which we demonstrate using a visualization technique and ablation experiments. In effect, the metric appears to model a higher influence of semantic context on judgments, which we observe particularly in untrained raters. As the vast majority of users of image processing systems are unfamiliar with Image Quality Assessment (IQA) tasks, these findings may have significant impact on real-world applications of perceptual metrics.

Intravascularultrasound (IVUS) sequences recorded in vivo are subject to a wide array of motion artifacts as the majority of these studies are performed within the coronary arteries of a beating heart. To eliminate these artifacts, an electrocardiogram (ECG) signal is typically used to gate (collect) those frames recorded at the points in time associated with a particular fraction of the cardiac cycle. However, this technique may be suboptimal for a number of reasons, among which is the difficulty of determining the optimal fraction at which to gate. This value is generally nonobvious. To circumvent this problem, we introduce a frame-gating method for IVUS pullbacks that mimics ECG (i.e., in the sense that it selects only one frame per cardiac cycle), but will automatically choose the fraction of the cycle that renders the most stable gated frame set. Stability here is gauged by measuring interframe similarity. Our method operates exclusively on the imagery data and does not require ECG or any form of image segmentation or other high-level image analysis. To validate our algorithm, we compare its behavior versus true ECG gating.

Intravascular ultrasound (IVUS) is a catheter-based modality which is used to produce high-resolution, cross-sectional images of the interior of blood vessels. By capturing 2-D IVUS images continually while translating the catheter, a volumetric image of a vessel may be digitally reconstructed. To improve the quality of these volumes, electrocardiogram (ECG)-based frame gating is often applied to alleviate motion artifacts caused by the beating heart. However, there are several issues surrounding the use of ECG signals which make their use for this purpose potentially suboptimal. We introduce a method which gates pullback sequences by examining the imaging data alone, without requiring synchronous ECG, and guarantees that frames will be collected at those points in time when the heart is maximally motionless (i.e., regardless of the fraction of cardiac phase associated with those points). We compare the results of our method and of ECG on pullbacks captured in vivo in swine.

In recent years, high dynamic range imaging (HDRI) has become a topic of intense research interest in the fields of computer vision, computer graphics, and commercial visualization. Yet, despite the inherent limitations of traditional low dynamic range imaging (LDRI) and the emerging need for HDRI in many applications, existing systems for HDR image capture still remain proprietary, expensive, or manually-guided. All of these factors limit the availability of effective HDRI tools, thereby restricting studies which could otherwise benefit from this technology. To help alleviate this problem, we introduce a system and method for automated capture of high-quality, high-range images using commercially-available digital cameras. We report results comparing acquisition time, image resolution, and dynamic range and show these factors to compare very favorably both to traditional manual capture methods and to specialized HDRI systems developed commercially.