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15-03 Digital Imaging — Fundamentals

he main objective of medical image processing is to facilitate the gathering or provision of information not easily seen, or not seen at all, on un­­pro­­ces­­sed images. Improvement of diagnostic performance to reduce the ever existing level of uncertainty is one of the main propelling forces in diagnostic imaging research.

Since the human visual-perception system is unable to perform multichannel analysis in order to achieve a new dimension, image processing developed in pa­ral­lel to the introduction of MR imaging as a clinical tool.

Researchers wanted to de­tect any message, possibly hidden, in a single MR image or a series of MR ima­ges. Given that only minimal information existed about how to approach this scien­ti­fic problem, the course of research was mostly empiric.

Historically, the following main lines of approach were followed:

spaceholder darkbluesubtraction or superposition (overlay, data fusion) of multichannel images;

spaceholder darkbluequantification of MR parameters, i.e., T1, T2 and proton density;

spaceholder darkblueimage segmentation and multispectral analysis;

spaceholder darkblue3D visualization.

In general, the processing of digitally acquired images is aimed at im­prov­ing pic­to­ri­al information for human interpretation and (or) processing data for autonomous ma­chine perception. Both aims have been targeted in mag­ne­tic re­so­nan­ce imaging and in both areas successful applications have been found.

All computed image-processing requires digitized imaging. In digitized ra­dio­lo­gy, the equivalent of a regular x-ray is taken and digitized directly by a spe­cia­li­zed x-ray system. In nuclear medicine, CT, and MR, imaging slices through the human body are acquired and subdivided into volume elements. Then, the numerical signal from each voxel, in turn, can be translated into a distinct shade of the gray scale and be represented as a picture element in the final image (Figure 15-04). Both, single images or a series of similar images can be manipulated, e.g., by noise re­­duc­­tion, edge or contrast enhancement [⇒ Godtliebsen 1989].

In multichannel imaging, several channels representing n different pa­ra­me­ters can be acquired simultaneously or by consecutive procedures, leading even­tu­al­ly to n images of exactly the same object (Figure 15-05).

Figure 15-04:
Numerical image date output (left) turned into gray-scale image (right). Typically, one finds medical ima­ges with an image matrix of 256×256, 512×512, or 1024×1024 and 256 gray levels.

Figure 15-05:
If multichannel images of the same object are properly aligned, it is easy to compare or compute their sig­nal intensities.

Image-processing allows the connection of picture element data of the same lo­­ca­­tion in different images with changed parameters; known connections can be com­­put­­ed, e.g., by using appropriate equations.

Such procedures may extract ad­di­tio­nal information or allow quantification of data and thus a — perhaps — 'objective' de­fi­ni­tion of structures, tissues, or me­ta­bo­lic pro­ces­ses. Several single images can, e.g., be add­ed to a new multispectral image (synthetic image) which does not necessarily add useful information or even depict reality (Figure 15-06).

Details on image pro­cess­ing can be found in a number of monographs [⇒ Gonzalez 2008, ⇒ Russ 2017].

Figure 15-06:
Examples of multichannel images: (a) proton-density-weighted, (b) T1-weighted, and (c) T2-weighted ima­ges of a slice through the brain. The anatomic location of the pi­xels is ex­act­ly the same; according to ima­ge weight­ing the pixel representation is dif­fe­rent.
(d) is a pixel-by-pixel compilation of images a-c. This synthetic image does not reveal any additional dia­gnos­tic in­for­ma­tion.

spaceholder redThere are different ways of classifying image-processing techniques, for in­­stan­­ce, they can be defined by what they are supposed to achieve.

Types of tech­ni­ques include noise reduction, image segmentation, fea­ture ex­trac­tion, and clas­si­fi­ca­tion.

Whereas noise reduction is of vital importance for more noisy modalities like ul­tra­sound, MR imaging has, due to a rapid development of MR hard­ware and soft­ware, not the same need for such techniques, perhaps with the exception of dy­na­mic imag­ing (see Chapter 16).

However, image segmentation and image classification have found much more wide­spread use in MR imaging, partly due to the possibility of ac­quir­ing mul­ti­­chan­nel data suitable for such processing.

Another important group of image-processing techniques is image registration or image alignment, which sometimes employs image segmentation techniques to align ima­ges. Image registration is important for aligning multimodality data (for in­stan­ce, nuclear medicine data and MR imaging data from the same pa­tient) or re­gis­tra­tion of time series. A specific type of time series (dynamic contrast-enhanced MR imaging) is de­scrib­ed in Chapter 16. Furthermore, time series can also be used for mo­ni­tor­ing tumor growth or growth of bones in children.

Another important type of MR imaging time series is BOLD functional MR imag­ing (fMRI) of the brain. Image registration is routinely performed on fMRI data.