715 research outputs found
Feasibility of using Lodox to perform digital subtraction angiography
Bibliography: leaves 150-157.Many cases in trauma involve vessel imaging to determine integrity and the origin of lesions or blockages. Digital subtraction angiography (DSA) is a tool used to improve the clarity of the vessels being imaged for better and easier decision making in diagnostics and planning. Lodox, a low dose x-ray system developed by Debex (Pty) Ltd, a subsidiary of de Beers, was designed specifically for the trauma environment. It therefore follows that, if possible, a function so readily used in trauma, such as DSA, should be added to the imaging repertoire of an x-ray system designed for use in this environment. In this dissertation the feasibility of using Lodox to perform DSA is therefore explored. In doing so, the requirements of a trauma unit and the theory behind DSA were researched so as to obtain a better understanding into what would be required
Advanced Algorithms for 3D Medical Image Data Fusion in Specific Medical Problems
Fúze obrazu je dnes jednou z nejběžnějších avšak stále velmi diskutovanou oblastí v lékařském zobrazování a hraje důležitou roli ve všech oblastech lékařské péče jako je diagnóza, léčba a chirurgie. V této dizertační práci jsou představeny tři projekty, které jsou velmi úzce spojeny s oblastí fúze medicínských dat. První projekt pojednává o 3D CT subtrakční angiografii dolních končetin. V práci je využito kombinace kontrastních a nekontrastních dat pro získání kompletního cévního stromu. Druhý projekt se zabývá fúzí DTI a T1 váhovaných MRI dat mozku. Cílem tohoto projektu je zkombinovat stukturální a funkční informace, které umožňují zlepšit znalosti konektivity v mozkové tkáni. Třetí projekt se zabývá metastázemi v CT časových datech páteře. Tento projekt je zaměřen na studium vývoje metastáz uvnitř obratlů ve fúzované časové řadě snímků. Tato dizertační práce představuje novou metodologii pro klasifikaci těchto metastáz. Všechny projekty zmíněné v této dizertační práci byly řešeny v rámci pracovní skupiny zabývající se analýzou lékařských dat, kterou vedl pan Prof. Jiří Jan. Tato dizertační práce obsahuje registrační část prvního a klasifikační část třetího projektu. Druhý projekt je představen kompletně. Další část prvního a třetího projektu, obsahující specifické předzpracování dat, jsou obsaženy v disertační práci mého kolegy Ing. Romana Petera.Image fusion is one of today´s most common and still challenging tasks in medical imaging and it plays crucial role in all areas of medical care such as diagnosis, treatment and surgery. Three projects crucially dependent on image fusion are introduced in this thesis. The first project deals with the 3D CT subtraction angiography of lower limbs. It combines pre-contrast and contrast enhanced data to extract the blood vessel tree. The second project fuses the DTI and T1-weighted MRI brain data. The aim of this project is to combine the brain structural and functional information that purvey improved knowledge about intrinsic brain connectivity. The third project deals with the time series of CT spine data where the metastases occur. In this project the progression of metastases within the vertebrae is studied based on fusion of the successive elements of the image series. This thesis introduces new methodology of classifying metastatic tissue. All the projects mentioned in this thesis have been solved by the medical image analysis group led by Prof. Jiří Jan. This dissertation concerns primarily the registration part of the first project and the classification part of the third project. The second project is described completely. The other parts of the first and third project, including the specific preprocessing of the data, are introduced in detail in the dissertation thesis of my colleague Roman Peter, M.Sc.
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Combination of high-resolution cone beam computed tomography and metal artefact reduction software: a new image fusion technique for evaluating intracranial stent apposition after aneurysm treatment.
We introduce a new imaging technique to improve visualisation of stent apposition after endovascular treatment of brain aneurysms employing high-resolution cone beam CT and three-dimensional digital subtraction angiography. After performing a stent-assisted coil embolisation of brain aneurysm, the image datasets were processed with a metal artefact reduction software followed by the automated image fusion programmes. Two patients who underwent aneurysm coiling using a Neuroform stent were evaluated. The reconstructed 3D images showed a detailed structure of the stent struts and identified malappositions of the deployed stents. Case 1 showed good apposition on the outer curvature side of the carotid siphon, while the inner curvature side showed prominent malapposition. Case 2, with multiple aneurysms, showed good apposition on both outer and inner curvature sides, although inward prolapse of the struts was observed. This new imaging technique may help evaluate stent apposition after the endovascular aneurysm treatment
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Image enhancement in digital X-ray angiography
Anyone who does not look back to the beginning throughout a
course of action, does not look forward to the end. Hence it
necessarily follows that an intention which looks ahead, depends
on a recollection which looks back.
| Aurelius Augustinus, De civitate Dei, VII.7 (417 A.D.)
Chapter 1
Introduction and Summary
D
espite the development of imaging techniques based on alternative physical
phenomena, such as nuclear magnetic resonance, emission of single photons
( -radiation) by radio-pharmaceuticals and photon pairs by electron-positron
annihilations, re ection of ultrasonic waves, and the Doppler eect, X-ray based im-
age acquisition is still daily practice in medicine. Perhaps this can be attributed to
the fact that, contrary to many other phenomena, X-rays lend themselves naturally
for registration by means of materials and methods widely available at the time of
their discovery | a fact that gave X-ray based medical imaging an at least 50-year
head start over possible alternatives. Immediately after the preliminary communica-
tion on the discovery of the \new light" by R¨ ontgen [317], late December 1895, the
possible applications of X-rays were investigated intensively. In 1896 alone, almost
one 1,000 articles about the new phenomenon appeared in print (Glasser [119] lists
all of them). Although most of the basics of the diagnostic as well as the therapeutic
uses of X-rays had been worked out by the end of that year [289], research on im-
proved acquisition and reduction of potential risks for humans continued steadily in
the century to follow. The development of improved X-ray tubes, rapid lm changers,
image intensiers, the introduction of television cameras into uoroscopy, and com-
puters in digital radiography and computerized tomography, formed a succession of
achievements which increased the diagnostic potential of X-ray based imaging.
One of the areas in medical imaging where X-rays have always played an im-
portant role is angiography,y which concerns the visualization of blood vessels in the
human body. As already suggested, research on the possibility of visualization of the
human vasculature was initiated shortly after the discovery of X-rays. A photograph
of a rst \angiogram" | obtained by injection of a mixture of chalk, red mercury,
and petroleum into an amputated hand, followed by almost an hour of exposure to
X-rays | was published as early as January 1896, by Hascheck & Lindenthal [139].
Although studies on cadavers led to greatly improved knowledge of the anatomy of
the human vascular system, angiography in living man for the purpose of diagnosis
and intervention became feasible only after substantial progress in the development
yA term originating from the Greek words o (aggeion), meaning \vessel" or \bucket", and
-' (graphein), meaning \to write" or \to record". 2 1 Introduction and Summary
of relatively safe contrast media and methods of administration, as well as advance-
ments in radiological equipment. Of special interest in the context of this thesis is
the improvement brought by photographic subtraction, a technique known since the
early 1900s and since then used successfully in e.g. astronomy, but rst introduced
in X-ray angiography in 1934, by Ziedses des Plantes [425, 426]. This technique al-
lowed for a considerable enhancement of vessel visibility by cancellation of unwanted
background structures. In the 1960s, the time consuming lm subtraction process
was replaced by analog video subtraction techniques [156, 275] which, with the in-
troduction of digital computers, gave rise to the development of digital subtraction
angiography [194] | a technique still considered by many the \gold standard" for de-
tection and quantication of vascular anomalies. Today, research on improved X-ray
based imaging techniques for angiography continues, witness the recent developments
in three-dimensional rotational angiography [88, 185, 186, 341,373].
The subject of this thesis is enhancement of digital X-ray angiography images. In
contrast with the previously mentioned developments, the emphasis is not on the
further improvement of image acquisition techniques, but rather on the development
and evaluation of digital image processing techniques for retrospective enhancement
of images acquired with existing techniques. In the context of this thesis, the term
\enhancement" must be regarded in a rather broad sense. It does not only refer
to improvement of image quality by reduction of disturbing artifacts and noise, but
also to minimization of possible image quality degradation and loss of quantitative
information, inevitably introduced by required image processing operations. These
two aspects of image enhancement will be claried further in a brief summary of each
of the chapters of this thesis.
The rst three chapters deal with the problem of patient motion artifacts in digital
subtraction angiography (DSA). In DSA imaging, a sequence of 2D digital X-ray
projection images is acquired, at a rate of e.g. two per second, following the injection
of contrast material into one of the arteries or veins feeding the part of the vasculature
to be diagnosed. Acquisition usually starts about one or two seconds prior to arrival
of the contrast bolus in the vessels of interest, so that the rst few images included
in the sequence do not show opacied vessels. In a subsequent post-processing step,
one of these \pre-bolus" images is then subtracted automatically from each of the
contrast images so as to mask out background structures such as bone and soft-
tissue shadows. However, it is clear that in the resulting digital subtraction images,
the unwanted background structures will have been removed completely only when
the patient lied perfectly still during acquisition of the original images. Since most
patients show at least some physical reaction to the passage of a contrast medium, this
proviso is generally not met. As a result, DSA images frequently show patient-motion
induced artifacts (see e.g. the bottom-left image in Fig. 1.1), which may in uence the
subsequent analysis and diagnosis carried out by radiologists.
Since the introduction of DSA, in the early 1980s, many solutions to the problem
of patient motion artifacts have been put forward. Chapter 2 presents an overview
of the possible types of motion artifacts reported in the literature and the techniques
that have been proposed to avoid them. The main purpose of that chapter is to
review and discuss the techniques proposed over the past two decades to correct for 1 Introduction and Summary 3
Figure 1.1. Example of creation and reduction of patient motion artifacts in
cerebral DSA imaging. Top left: a \pre-bolus" or mask image acquired just prior
to the arrival of the contrast medium. Top right: one of the contrast or live images
showing opacied vessels. Bottom left: DSA image obtained after subtraction of
the mask from the contrast image, followed by contrast enhancement. Due to patient
motion, the background structures in the mask and contrast image were not perfectly
aligned, as a result of which the DSA image does not only show blood vessels, but
also additional undesired structures (in this example primarily in the bottom-left
part of the image). Bottom right: DSA image resulting from subtraction of the
mask and contast image after application of the automatic registration algorithm
described in Chapter 3. 4 1 Introduction and Summary
patient motion artifacts retrospectively, by means of digital image processing. The
chapter addresses fundamental problems, such as whether it is possible to construct
a 2D geometrical transformation that exactly describes the projective eects of an
originally 3D transformation, as well as practical problems, such as how to retrieve
the correspondence between mask and contrast images by using only the grey-level
information contained in the images, and how to align the images according to that
correspondence in a computationally ecient manner.
The review in Chapter 2 reveals that there exists quite some literature on the
topic of (semi-)automatic image alignment, or image registration, for the purpose of
motion artifact reduction in DSA images. However, to the best of our knowledge,
research in this area has never led to algorithms which are suciently fast and robust
to be acceptable for routine use in clinical practice. By drawing upon the suggestions
put forward in Chapter 2, a new approach to automatic registration of digital X-ray
angiography images is presented in Chapter 3. Apart from describing the functionality
of the components of the algorithm, special attention is paid to their computationally
optimal implementation. The results of preliminary experiments described in that
chapter indicate that the algorithm is eective, very fast, and outperforms alterna-
tive approaches, in terms of both image quality and required computation time. It is
concluded that the algorithm is most eective in cerebral and peripheral DSA imag-
ing. An example of the image quality enhancement obtained after application of the
algorithm in the case of a cerebral DSA image is provided in Fig 1.1.
Chapter 4 reports on a clinical evaluation of the automatic registration technique.
The evaluation involved 104 cerebral DSA images, which were corrected for patient
motion artifacts by the automatic technique, as well as by pixel shifting | a manual
correction technique currently used in clinical practice. The quality of the DSA images
resulting from the two techniques was assessed by four observers, who compared the
images both mutually and to the corresponding original images. The results of the
evaluation presented in Chapter 4 indicate that the dierence in performance between
the two correction techniques is statistically signicant. From the results of the mutual
comparisons it is concluded that, on average, the automatic registration technique
performs either comparably, better than, or even much better than manual pixel
shifting in 95% of all cases. In the other 5% of the cases, the remaining artifacts are
located near the borders of the image, which are generally diagnostically non-relevant.
In addition, the results show that the automatic technique implies a considerable
reduction of post-processing time compared to manual pixel shifting (on average, one
second versus 12 seconds per DSA image).
The last two chapters deal with somewhat dierent topics. Chapter 5 is concerned
with visualization and quantication of vascular anomalies in three-dimensional rota-
tional angiography (3DRA). Similar to DSA imaging, 3DRA involves the acquisition
of a sequence of 2D digital X-ray projection images, following a single injection of
contrast material. Contrary to DSA, however, this sequence is acquired during a 180
rotation of the C-arch on which the X-ray source and detector are mounted antipo-
dally, with the object of interest positioned in its iso-center. The rotation is completed
in about eight seconds and the resulting image sequence typically contains 100 images,
which form the input to a ltered back-projection algorithm for 3D reconstruction. In
contrast with most other 3D medical imaging techniques, 3DRA is capable of provid- 1 Introduction and Summary 5
Figure 1.2. Visualizations of a clinical 3DRA dataset, illustrating the qualitative
improvement obtained after noise reduction ltering. Left: volume rendering of
the original, raw image. Right: volume rendering of the image after application
of edge-enhancing anisotropic diusion ltering (see Chapter 5 for a description of
this technique). The visualizations were obtained by using the exact same settings
for the parameters of the volume rendering algorithm.
ing high-resolution isotropic datasets. However, due to the relatively high noise level
and the presence of other unwanted background variations caused by surrounding
tissue, the use of noise reduction techniques is inevitable in order to obtain smooth
visualizations of these datasets (see Fig. 1.2). Chapter 5 presents an inquiry into the
eects of several linear and nonlinear noise reduction techniques on the visualization
and subsequent quantication of vascular anomalies in 3DRA images. The evalua-
tion is focussed on frequently occurring anomalies such as a narrowing (or stenosis)
of the internal carotid artery or a circumscribed dilation (or aneurysm) of intracra-
nial arteries. Experiments on anthropomorphic vascular phantoms indicate that, of
the techniques considered, edge-enhancing anisotropic diusion ltering is most suit-
able, although the practical use of this technique may currently be limited due to its
memory and computation-time requirements.
Finally, Chapter 6 addresses the problem of interpolation of sampled data, which
occurs e.g. when applying geometrical transformations to digital medical images for
the purpose of registration or visualization. In most practical situations, interpola-
tion of a sampled image followed by resampling of the resulting continuous image
on a geometrically transformed grid, inevitably implies loss of grey-level information,
and hence image degradation, the amount of which is dependent on image content,
but also on the employed interpolation scheme (see Fig. 1.3). It follows that the
choice for a particular interpolation scheme is important, since it in uences the re-
sults of registrations and visualizations, and the outcome of subsequent quantitative
analyses which rely on grey-level information contained in transformed images. Al-
though many interpolation techniques have been developed over the past decades, 6 1 Introduction and Summary
Figure 1.3. Illustration of the fact that the loss of information due to interpola-
tion and resampling operations is dependent on the employed interpolation scheme.
Left: slice of a 3DRA image after rotation over 5:0, by using linear interpolation.
Middle: the same slice, after rotation by using cubic spline interpolation. Right:
the dierence between the two rotated images. Although it is not possible with
such a comparison to come to conclusions as to which of the two methods yields
the smallest loss of grey-level information, this example clearly illustrates the point
that dierent interpolation methods usually yield dierent results.
thorough quantitative evaluations and comparisons of these techniques for medical
image transformation problems are still lacking. Chapter 6 presents such a compar-
ative evaluation. The study is limited to convolution-based interpolation techniques,
as these are most frequently used for registration and visualization of medical image
data. Because of the ubiquitousness of interpolation in medical image processing and
analysis, the study is not restricted to XRA and 3DRA images, but also includes
datasets from many other modalities. It is concluded that for all modalities, spline
interpolation constitutes the best trade-o between accuracy and computational cost,
and therefore is to be preferred over all other methods.
In summary, this thesis is concerned with the improvement of image quality and the
reduction of image quality degradation and loss of quantitative information. The
subsequent chapters describe techniques for reduction of patient motion artifacts in
DSA images, noise reduction techniques for improved visualization and quantication
of vascular anomalies in 3DRA images, and interpolation techniques for the purpose
of accurate geometrical transformation of medical image data. The results and con-
clusions of the evaluations described in this thesis provide general guidelines for the
applicability and practical use of these techniques
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
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