Some medical applications of example-based super-resolution

04/17/2016 ∙ by Ramin Zabih, et al. ∙ 0

Example-based super-resolution (EBSR) reconstructs a high-resolution image from a low-resolution image, given a training set of high-resolution images. In this note I propose some applications of EBSR to medical imaging. A particular interesting application, which I call "x-ray voxelization", approximates the result of a CT scan from an x-ray image.

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1 Example-based super-resolution

Example-based super-resolution (EBSR) [3, 4]

produces a high-resolution image from a low resolution image, given a training database of high-resolution images. The basic idea is that an observed low-resolution pixel is the average of a set of high-resolution pixels. The training set can be viewed as a probabilistic map from a set of underlying high-resolution pixels to the corresponding observed low-resolution pixel, since in general it is straightforward to model the blurring and subsampling process that turns a high-resolution image into a low-resolution one. Naively EBSR could be solved using a nearest neighbors scheme, but of course it is necessary to make use of inference and machine learning techniques to handle the spatial relationships between pixels (see

[3, 4] for details).

2 Applications to medical imaging

While super-resolution has many application, EBSR for medical applications seems to have been under-explored (see [6] for a rare example). Yet there are many medical applications where EBSR would be extremely natural.

As one example, consider CT scans, where perhaps the most serious limitation is the risk posed by ionizing radiation [1]. For screening purposes, lung CT is often done at low-dose, with the resulting loss of image detail. Example-based super-resolution has the potential to provide the level of detail that occurs with normal levels of radiation, while keeping the radiation risk advantages of low-dose.

This would be achieved by collecting a database of normal-dose CT’s along with the corresponding low-dose images. If there are accurate simulations of the noise and artifacts introduced by low-dose, it would be possible to model a corresponding low-dose image from a normal-dose one. It is also possible that under certain circumstances both a low-dose and a normal-dose image of a patient may be available (for example, if the patient undergoes a low-dose screening CT, and the findings warrant a follow-up normal-dose CT).

In this particular example, the problem is not precisely EBSR since the low-dose and the high-dose images are likely to have identical resolution; however, it is basically the same problem since the goal is to add details using a database of training images. A similar application would involve inferring the images seen in contrast-enhanced CT from non-contrast images, since the IV contrast agents used have risks, especially for patients with compromised kidney function.

Other natural medical examples appear in MRI, where the chief limitation is scanning time. A collection of high-resolution images may be obtained as the database, despite the extended scanning time, and used to fill in details for low-resolution images obtained with faster scans. This could be particularly useful for parts of the body that undergo motion. Obviously, some of these applications would require registration as well (generally non-rigid registration).

2.1 Advantages of medical applications for EBSR

Medical applications of EBSR have some interesting advantages over conventional photography (i.e., the application for which EBSR was developed).

Spatial coordinates in medical applications are generally meaningful, while they are of very limited utility in conventional photography. For example, in a chest CT pixels near the center of the image are very likely to be of the heart or lungs, while pixels at the border are generally air. In contrast, any statistical argument about the content of pixels at the center of a conventional photograph is likely to be extremely generic, and thus of limited utility. This holds out the promise that medical EBSR applications would obtain higher resolution from a smaller dataset than most EBSR applications. (Alternatively, medical EBSR could obtain better accuracy at the same resolution.)

Related to this advantage is the fact that in medical imaging the size of a pixel (or voxel) is specified in physical units (e.g., a voxel might be 1mm on a side). The contrast to conventional photography is dramatic, since a pixel could contain a portion of galaxy, or a part of a mouse’s whisker. This regularity should result in better result from medical EBSR.

Finally, it is often the case that in medical applications multiple imaging modalities are available. A patient may undergo an MRI and a CT, and with proper registration this significantly increases the amount of information available for EBSR.

3 X-ray voxelization

The goal of what I call “x-ray voxelization” (XRV) is to approximate the result of a CT scan based on an x-ray, or perhaps a small number of x-rays.111The term “voxelization” comes from the graphics community [2], where it has a distinct but related meaning. Technically this is a fairly straightforward application of EBSR; the main difference is that while in conventional photography EBSR might replace an observed low-resolution pixel by a high resolution 2x2 patch, in XRV we replace an observed x-ray pixel by a 4x1 “stack” of CT voxels.

A typical CT consists of 512x512 axial slices, where by convention the x-axis points along the patient’s left-right, and the y-axis points into the patient (assuming the patient is lying on their back in the scanner). Some number of slices are obtained for varying choices of z, pointing from the patient’s feet to head. Depending on the exact configuration, the number of z-slices can run into the dozens, and there can be overlap between the slices. The key here is the y-axis, which is where the “stack” of reconstructed voxels will be oriented.

To be more precise, consider an x-ray taken of a patient before undergoing a CT. (This in fact happens as part of the standard CT process, where a ’scout’ image is take by the CT scanner and used to localize the area where the full high-resolution CT will be taken. The scout is essentially a conventional x-ray image, though its spatial resolution is typically lower.) An individual pixel in the scout is the average over the y-axis of the 512 voxels “behind” that pixel.

In x-ray voxelization the goal is to create a set of y-slices from an x-ray. (To visualize this, imagine a standard chest x-ray, where we use EBSR to create images at different values of y that we can navigate through. The average of the different y-slices will be the original x-ray.) While the technology proposed is related to tomographic imaging [5], a modality that was the direct predecessor of CT, the underlying algorithms and techniques are quite different, especially in terms of the use of machine learning and related inference methods.

3.1 Theme and variations

There are a number of straightforward extensions of the basic XRV scheme. For example, in many situations there are several x-rays taken (chest x-rays are a good example, as are hand x-rays). With careful registration these images can also be used to increase the accuracy of the voxelization.

Another natural extension, suggested by Bill Freeman, is to present the user with multiple alternative voxelizations that are supported by the input data. Since voxelization is an ill-posed problem, at the end we have some highly complex probability distribution over voxelizations, and we could for example present local modes in descending order of probability.

4 XRV research program and applications

There are several key steps to a research program investigating XRV, some of which can be carried out in parallel.

Despite the obvious appeal of XRV, it is necessary to identify a few clinical applications where it would be of most use. The key properties of such an application are that it should be common, and that a low degree of reconstruction would be useful. It is also desirable that CT and x-rays are commonly done, to facilitate the collection of training data.

The natural way to proceed is to create synthetic voxelizations, where we take a CT scan and reduce the resolution in y. This emulates the result of perfect noise-free voxelization at various resolutions. We can present patients whose diagnosis is clear on CT, and determine at what voxelization resolution the diagnosis becomes obvious. This would effectively give us a lower bound on the resolution that we need to achieve.

CT scouts are the next step in the process. Scouts are widely available, and we can look at the XRV computed using the scout as the input x-ray. This is probably the best way to investigate the various inference algorithms that would be needed for XRV. In addition, XRV from a scout image would be of some clinical benefit, though not on the scale of XRV from x-rays. For example, it could indicate that a full CT is not required.

The final step in the process is to go to complete XRV, starting with an x-ray. The scout can serve as a useful intermediate datapoint; for example, we will need to register the x-ray to the scout. Multiple x-rays pose a more interesting technical problem, since they need to be registered explicitly to the 3d volume that we wish to reconstruct. Note that in general x-ray resolution is significantly higher than CT, typically around 2k by 2k.

It is an under-appreciated fact among clinicians that CT reconstructions themselves are not veridical, but instead rely on computational methods to regularize an ill-posed problem. While the CT scanner manufacturers are closed-mouthed about their exact reconstruction methods, a review of the literature suggests that Tikhonov regularization or its variants are likely to be used. A natural objection to XRV is that bias in the training set would reduce its accuracy – for example, the training set may never contain any images of a suitably rare condition, so how would the system properly infer the relevant anatomy? But conventional CT has its own biases, they are just procedural in nature as well as undocumented. Just as with CT, clinical acceptance of XRV would follow the time-honored path of randomized clinical trials and imilar methods to measure its diagnostic accuracy. For example, a clinical trial could focus on patients who obtained both chest x-rays and chest CT (which is fairly common), perhaps using biopsy results to establish ground truth. The hope would be that for some clinically important conditions the accuracy of XRV would be much higher than that of simple x-rays, and might provide sufficient information to remove the need for CT in many patients.

4.1 Applications of XRV

XRV would have significant clinical utility, depending on the voxelization resolution that can be computed with reasonable accuracy. X-ray machines have near universal availability and the radiation risk is generally viewed as quite small. The payoff for XRV would be especially large in developing nations, where an x-ray machine and an Internet connection could give patients some of the power of a CT scanner. But even in the US, there are plenty of situations where CT is not immediately available, or even where XRV could be used to determine whether or not a full CT is warranted.

References

  • [1] David J Brenner and Eric J Hall. Computed tomography—an increasing source of radiation exposure. New England Journal of Medicine, 357(22):2277–2284, 2007.
  • [2] Zhao Dong, Wei Chen, Hujun Bao, Hongxin Zhang, and Qunsheng Peng. Real-time voxelization for complex polygonal models. In Computer Graphics and Applications, 2004. PG 2004. Proceedings. 12th Pacific Conference on, pages 43–50. IEEE, 2004.
  • [3] W. Freeman, T. Jones, and E. Pasztor. Example-based super-resolution. IEEE Computer Graphics and Applications, 22(2):56–65, March–April 2002.
  • [4] W. T. Freeman, E. C. Pasztor, and O. T. Carmichael. Learning low-level vision. IJCV, 40(1):25–47, 2000.
  • [5] Avinash C.. Kak and Malcolm Slaney. Principles of computerized tomographic imaging. IEEE press, 1988.
  • [6] Dinh-Hoan Trinh, Marie Luong, Franccoise Dibos, Jean-Marie Rocchisani, Canh-Duong Pham, and Truong Q Nguyen. Novel example-based method for super-resolution and denoising of medical images. IEEE Transactions on Image Processing, 23(4):1882–1895, 2014.