Image enhancement in digital X-ray angiography (2001)

Anyone who does not look back to the beginning throughout a\ncourse of action, does not look forward to the end. Hence it\nnecessarily follows that an intention which looks ahead, depends\non a recollection which looks back.\n| Aurelius Augustinus, De civitate Dei, VII.7 (417 A.D.)\nChapter 1\nIntroduction and Summary\nD\nespite the development of imaging techniques based on alternative physical\nphenomena, such as nuclear magnetic resonance, emission of single photons\n( -radiation) by radio-pharmaceuticals and photon pairs by electron-positron\nannihilations, re ection of ultrasonic waves, and the Doppler eect, X-ray based im-\nage acquisition is still daily practice in medicine. Perhaps this can be attributed to\nthe fact that, contrary to many other phenomena, X-rays lend themselves naturally\nfor registration by means of materials and methods widely available at the time of\ntheir discovery | a fact that gave X-ray based medical imaging an at least 50-year\nhead start over possible alternatives. Immediately after the preliminary communica-\ntion on the discovery of the \\new light" by R¨ ontgen [317], late December 1895, the\npossible applications of X-rays were investigated intensively. In 1896 alone, almost\none 1,000 articles about the new phenomenon appeared in print (Glasser [119] lists\nall of them). Although most of the basics of the diagnostic as well as the therapeutic\nuses of X-rays had been worked out by the end of that year [289], research on im-\nproved acquisition and reduction of potential risks for humans continued steadily in\nthe century to follow. The development of improved X-ray tubes, rapid lm changers,\nimage intensiers, the introduction of television cameras into uoroscopy, and com-\nputers in digital radiography and computerized tomography, formed a succession of\nachievements which increased the diagnostic potential of X-ray based imaging.\nOne of the areas in medical imaging where X-rays have always played an im-\nportant role is angiography,y which concerns the visualization of blood vessels in the\nhuman body. As already suggested, research on the possibility of visualization of the\nhuman vasculature was initiated shortly after the discovery of X-rays. A photograph\nof a rst \\angiogram" | obtained by injection of a mixture of chalk, red mercury,\nand petroleum into an amputated hand, followed by almost an hour of exposure to\nX-rays | was published as early as January 1896, by Hascheck & Lindenthal [139].\nAlthough studies on cadavers led to greatly improved knowledge of the anatomy of\nthe human vascular system, angiography in living man for the purpose of diagnosis\nand intervention became feasible only after substantial progress in the development\nyA term originating from the Greek words o (aggeion), meaning \\vessel" or \\bucket", and\n-' (graphein), meaning \\to write" or \\to record". 2 1 Introduction and Summary\nof relatively safe contrast media and methods of administration, as well as advance-\nments in radiological equipment. Of special interest in the context of this thesis is\nthe improvement brought by photographic subtraction, a technique known since the\nearly 1900s and since then used successfully in e.g. astronomy, but rst introduced\nin X-ray angiography in 1934, by Ziedses des Plantes [425, 426]. This technique al-\nlowed for a considerable enhancement of vessel visibility by cancellation of unwanted\nbackground structures. In the 1960s, the time consuming lm subtraction process\nwas replaced by analog video subtraction techniques [156, 275] which, with the in-\ntroduction of digital computers, gave rise to the development of digital subtraction\nangiography [194] | a technique still considered by many the \\gold standard" for de-\ntection and quantication of vascular anomalies. Today, research on improved X-ray\nbased imaging techniques for angiography continues, witness the recent developments\nin three-dimensional rotational angiography [88, 185, 186, 341,373].\nThe subject of this thesis is enhancement of digital X-ray angiography images. In\ncontrast with the previously mentioned developments, the emphasis is not on the\nfurther improvement of image acquisition techniques, but rather on the development\nand evaluation of digital image processing techniques for retrospective enhancement\nof images acquired with existing techniques. In the context of this thesis, the term\n\\enhancement" must be regarded in a rather broad sense. It does not only refer\nto improvement of image quality by reduction of disturbing artifacts and noise, but\nalso to minimization of possible image quality degradation and loss of quantitative\ninformation, inevitably introduced by required image processing operations. These\ntwo aspects of image enhancement will be claried further in a brief summary of each\nof the chapters of this thesis.\nThe rst three chapters deal with the problem of patient motion artifacts in digital\nsubtraction angiography (DSA). In DSA imaging, a sequence of 2D digital X-ray\nprojection images is acquired, at a rate of e.g. two per second, following the injection\nof contrast material into one of the arteries or veins feeding the part of the vasculature\nto be diagnosed. Acquisition usually starts about one or two seconds prior to arrival\nof the contrast bolus in the vessels of interest, so that the rst few images included\nin the sequence do not show opacied vessels. In a subsequent post-processing step,\none of these \\pre-bolus" images is then subtracted automatically from each of the\ncontrast images so as to mask out background structures such as bone and soft-\ntissue shadows. However, it is clear that in the resulting digital subtraction images,\nthe unwanted background structures will have been removed completely only when\nthe patient lied perfectly still during acquisition of the original images. Since most\npatients show at least some physical reaction to the passage of a contrast medium, this\nproviso is generally not met. As a result, DSA images frequently show patient-motion\ninduced artifacts (see e.g. the bottom-left image in Fig. 1.1), which may in uence the\nsubsequent analysis and diagnosis carried out by radiologists.\nSince the introduction of DSA, in the early 1980s, many solutions to the problem\nof patient motion artifacts have been put forward. Chapter 2 presents an overview\nof the possible types of motion artifacts reported in the literature and the techniques\nthat have been proposed to avoid them. The main purpose of that chapter is to\nreview and discuss the techniques proposed over the past two decades to correct for 1 Introduction and Summary 3\nFigure 1.1. Example of creation and reduction of patient motion artifacts in\ncerebral DSA imaging. Top left: a \\pre-bolus" or mask image acquired just prior\nto the arrival of the contrast medium. Top right: one of the contrast or live images\nshowing opacied vessels. Bottom left: DSA image obtained after subtraction of\nthe mask from the contrast image, followed by contrast enhancement. Due to patient\nmotion, the background structures in the mask and contrast image were not perfectly\naligned, as a result of which the DSA image does not only show blood vessels, but\nalso additional undesired structures (in this example primarily in the bottom-left\npart of the image). Bottom right: DSA image resulting from subtraction of the\nmask and contast image after application of the automatic registration algorithm\ndescribed in Chapter 3. 4 1 Introduction and Summary\npatient motion artifacts retrospectively, by means of digital image processing. The\nchapter addresses fundamental problems, such as whether it is possible to construct\na 2D geometrical transformation that exactly describes the projective eects of an\noriginally 3D transformation, as well as practical problems, such as how to retrieve\nthe correspondence between mask and contrast images by using only the grey-level\ninformation contained in the images, and how to align the images according to that\ncorrespondence in a computationally ecient manner.\nThe review in Chapter 2 reveals that there exists quite some literature on the\ntopic of (semi-)automatic image alignment, or image registration, for the purpose of\nmotion artifact reduction in DSA images. However, to the best of our knowledge,\nresearch in this area has never led to algorithms which are suciently fast and robust\nto be acceptable for routine use in clinical practice. By drawing upon the suggestions\nput forward in Chapter 2, a new approach to automatic registration of digital X-ray\nangiography images is presented in Chapter 3. Apart from describing the functionality\nof the components of the algorithm, special attention is paid to their computationally\noptimal implementation. The results of preliminary experiments described in that\nchapter indicate that the algorithm is eective, very fast, and outperforms alterna-\ntive approaches, in terms of both image quality and required computation time. It is\nconcluded that the algorithm is most eective in cerebral and peripheral DSA imag-\ning. An example of the image quality enhancement obtained after application of the\nalgorithm in the case of a cerebral DSA image is provided in Fig 1.1.\nChapter 4 reports on a clinical evaluation of the automatic registration technique.\nThe evaluation involved 104 cerebral DSA images, which