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					         9        Image Processing on Diagnostic Workstations

                                                        ments from simple PACS viewing into a really
BART M. TER HAAR ROMENY
                                                        versatile viewing environment. This chapter gives
Professor, Eindhoven University of Technology,
Department of Biomedical Engineering, Image             an overview of these developments, aimed at radi-
Analysis and Interpretation, PO Box 513, WH 2.106,      ologists’ readership. Many references and internet
5600 MB Eindhoven, The Netherlands
                                                        links are given which discuss the topics in more

CONTENTS                                                depth than is possible in this short paper. This
                                                        paper is necessarily incomplete.
9.1    Introduction
                                                               Viewing stations are core business in a ra-
9.2    Hardware
                                                        diologist’s daily work. All big medical imaging
9.3    Software
                                                        industries supply professional and integrated envi-
9.4    3D Visualization
                                                        ronments (such as Philips ViewForum, Siemens
9.5    Computer Aided Detection (CAD)
9.6    Atlases                                          Syngo X, GE Advantage, etc.). There are dedi-
9.7    CAD/CAM Design                                   cated companies for viewing software (a.o. Merge
9.8    Diffusion Tensor Imaging (DTI) - Tractography    eFilm) or OEM solutions (a.o. Mercury Visage,
9.9    Registration                                     Barco). The application domain of workstations is
9.10   RT Dose Planning                                 increasing. We now see them regularly employed
9.11   Quantitative Image Analysis
                                                        in PACS and teleradiology diagnostic review,
9.12   Workstations for Life Sciences
                                                        3D/3D-time (4D) visualization, computer-aided
9.13   Computer-Aided Surgery (CAS)
                                                        detection (CAD), quantitative image analysis,
9.14   New Developments
                                                        computer-assisted surgery (CAS), radiotherapy
9.15   Outlook
       References                                       treatment planning, and pathology. Also the appli-
                                                        cations for medical image analysis in the life-
                                                        sciences research are increasing, due to the in-
                                                       creased attention to small-animal scanning sys-
Scientific terms marked with         are explained in
Wikipedia: www.wikipedia.org                            tems for molecular imaging, and the many types
                                                        of advanced microscopes (such as confocal mi-
9.1          Introduction                               croscopy and two-photon laser scanning micro-
                                                        scopes), all giving huge 3D datasets. The focus
Medical workstations have developed into the super-
                                                        of this chapter is on image processing (also termed
assistants of radiologists. The overwhelming produc-
                                                        image analysis or computer vision) applications.
tion of images, hardware that rapidly became
cheaper and powerful 3D visualization and quantita-
tive analysis software have all pushed the develop-
9.2         Hardware                                    plains why increasing the RAM of a slow com-
                                                        puter can markedly upgrade its performance. In a
Early systems were based on expensive hardware          PACS system, the disk storage is typically done
platforms, called workstations, often based on the      on a ‘redundant array of inexpensive disks’
                          
UNIX operating system . Today, most systems are         (RAID), where many disks in parallel prevent
based on readily available and affordable PC and        loss of data in case of failure of a component.
Mac hardware platforms (running MS-Windows or
Mac-OS respectively), which are still following
Moore’s law of increasing performance (a doubling
every 24 months) at a stable price level.
       The central processor unit (CPU) is the core
of the system, running today at several Gigahertz,
and performance is expressed in Giga-FLOPS (109
floating point operations per second). Famous
CPUs are the Intel Pentium chip, and the AMD Ath-
lon processor. Today, we see the current 32 bit proc-
essors being replaced by 64 bit processors, which are
capable of processing more instructions simultane-
ously and addressing a larger number of memory
elements (232 = 4.2 × 109, so a 32 bit system cannot
have more than 4.2 GB of memory(109 = Giga)).
There is also a trend to have more CPUs (‘dualcore’)
on the motherboard, paving the way to parallel
processing, which is currently still in its infancy.
       The memory in the diagnostic workstation is
organized in a hierarchical fashion. From small to
large: the CPU has a so-called cache on its chip, as
a local memory scratchpad for super-fast access, and
communicates with the main RAM (random access
memory, today typically 1–4 GB) through the data
bus, a data highway in the computer. As the RAM
is fully electronic, access is fast (nanoseconds),      Fig. 9.1a–c. Brain aneurysm (a) and carotids (b):
much faster than access to a local hard disk (milli-   examples of volume renderings with a computer
seconds). When the RAM is fully occupied, the CPU       game graphics card (3Mensio Inc) (c)
starts communicating with the hard disk. This ex-
       The speed of the network should be able to           They have finally become fully programmable
accommodate the network traffic. Typically the              (and can be instructed by languages as DirectX
workstation is part of a local area network (LAN).         and OpenGL) and are equipped with 1–
Today gigabit/second speeds are attained over wired         1.5 gigabytes of local memory. These ‘games’
networks, wireless is slower (30–100 Mbit/s) but            hardware boards are now increasingly used in 3D
convenient for laptops and ‘person digital assistants’      medical visualization applications (a.o. 3Mensio
(PDAs). Many PACS installations can be serviced             Medical Systems). There is also a community ex-
remotely through LAN connections to the supplier,           ploring the use of GPUs for general processing
anywhere.                                                   (DICOM undated a).
       Networks are so fast nowadays that 3D vol-                  The viewing screens of diagnostic work-
ume rendering can be distributed from a central             stations have to be of special diagnostic quality.
powerful computer to simple (and thus low cost)             Excellent reviews of the important parameters
viewing stations, called ‘thin clients’ (a.o. Terare-      (resolution, contrast, brightness, 8, 10 or 12 bit
con Aquarius). A powerful dedicated graphics board          intensity range, homogeneity, stability, viewing
(in this case the VolumePro 1000) with dedicated            angle, speed, etc. are available in the so-called
hardware runs several 3D viewing applications si-           white papers by a variety of vendors (a.o. Barco –
multaneously, and is remotely controlled by the us-         BARCO undated, Eizo – EIZO undated).
ers of the thin clients. Advantage is the capability to
handle huge datasets (e.g. > 3000 slices) easily, but       9.3        Software
scalability (to e.g. dozens of users) is limited.
                                                            The revolution in PACS (and teleradiology) view-
       Interestingly, the power of ‘graphical proc-
                                                            ing stations was fired by the standard “Digital
                        
essing units’       (GPU , the processor on the video                                                       
                                                            Imaging and Communications in Medicine”
card (or graphics accelerator card) in the system)
                                                            (DICOM) standard (DICOM undated a), 4000
has increased even faster than CPU power, mainly
                                                            pages). In the 1990s the ACR (American College
due to the fact that GPUs form the core of the com-
                                                            of Radiology) and NEMA (National Electrical
puter game industry. The millions of systems needed
                                                            Manufacturers Association) formed a joint com-
for this lucrative market and the high competition
                                                            mittee to develop this standard. The standard is
between the market leaders NVIDIA and ATI have
                                                            developed in liaison with other standardization
created a huge performance/price ratio. A GPU has a
                                                            organizations including CEN TC251 in Europe
50 times faster communication speed of the data
                                                            and JIRA in Japan, with review also by other or-
internally between memory and processor, and has
                                                            ganizations including IEEE, HL7 and ANSI in the
dedicated hardware for rendering artificial environ-
                                                            USA. It is now widely accepted. Convenient short
                                                   
ments, such as texture mapping , pixel shaders and
                                                            tutorials are available (BARCO undated). As the
                                                        
an intrinsic parallel design with pixel pipelines .
                                                            scanners and viewing software continue to de-
velop, new features have to be added to the standard      into lower languages, like C, C++, Java. When
continuously. Vendors are required to make avail-         ultimate speed (and limited variability) is required,
able their so-called conformity statements (see for       the code can be implemented in hardware (GPU,
example BURRONI et al. 2004), i.e. a specified list of    field programmable gate array’s (FPGA), dedi-
what conforms to the current version of the standard.     cated chips, etc.). Many packages offer software
       The second revolution was the standardiza-         development kits for joint development (e.g.
tion of the internal procedural organization of medi-     MevisLab by MEVIS, ‘Insight Segmentation and
cal data handling in the ‘Health Level 7’ standard       Registration Toolkit’ (ITK) by NLM, etc.).
(HL7) (DICOM undated a).
       The basic function of a viewing station is the     9.4          3D Visualization
convenient viewing of the data, with a patient selec-
                                                          The first breakthrough in the use of workstations
tion section. The functions are grouped in a so-called
                                                          has been by the invention of generating realistic
‘graphical user interface’ (GUI). Versatile PC
                                                          3D views from tomographic volume data in the
based viewing packages are now widely available
                                                          1980s. Now 3D volume rendering is fully interac-
(see RSNA 2006 for an extensive list), many also
                                                          tive, at high resolution and real-time speed, and
                            
offering ‘extended ASCI’         character sets for the
                                                          with a wide variety of options, making it a non-
Chinese, Japanese and Korean markets.
                                                          trivial matter to use it.
       Basic functions of the GUI include adminis-
                                                                  Many dedicated companies are now estab-
trative functions as patient and study selection, re-
                                                          lished (such as Vital Images with Vitrea, Mercury
port viewing and generation, and visualization func-
                                                          Computer Systems with Amira, Barco with Voxar,
tions as cine loop, ‘maximum intensity projection’
                                                          3Mensio with 3Vision, Terarecon with Aquarius,
                                            
(MIP), ‘multi-planar reformatting’ (MPR ) includ-
                                                          etc.). Often a third party 3D viewing application is
ing oblique and curved reconstructions, cut planes,
                                                          integrated in the PACS viewing application, and
measurement tools for distances and angles, magni-
                                                          supplied as a complete system by such an ‘original
fying glass, annotations, etc.
                                                          equipment manufacturer’ (OEM).
       The development of computer vision algo-
                                                                  The principle of ray tracing (‘rendering’)
rithms often follows a hierarchical pathway. The
                                                          (NOWINSKI et al. 2005) is actually based on mim-
design process (rapid prototyping) is done in high-
                                                          icking the physics of light reflection with the
                                                 
level software (examples are Mathematica , Ma-
                                                          computer: the value of a pixel in a 2D image of a
ple, Matlab), where very powerful statements and
                                                          3D view (also called a 2.5D view) is calculated
algebraic functionality make up for very short code,
                                                          from the reflected amount of light from a virtual
but his is difficult to extent to the huge multi-
                                                          light source, either bouncing on the surface of the
dimensional medical images. When the formulas are
                                                          3D data (this process is called ‘surface render-
understood and stable, the implementation is made
                                                          ing’), or as the summation of all contributions
Fig. 9.3. Virtual colonoscopy with unfolding enables inspection of folds from all sides.
From VILANOVA ET AL. (2003)

from the inside of the 3D dataset along the line of       tings possible, users often get confused, and a
the ray in question, composed with a formula that         standard set of settings is supplied, e.g. for lung
takes into account the transparency (or the in-           vessels, skull, abdominal vascular, etc., or a set of
verse: the opacity) of the voxels (this process is        thumbnails is given with examples of presets,
called ‘volume rendering’).                              from which the user can choose. Attempts are
                                                          underway to extract the optimal settings from the
                                                          statistics of the data itself (NOWINSKI et al. 2005).
                                                                 In virtual endoscopy (e.g. colonoscopy)
                                                          the camera is positioned inside the 3D dataset.
                                                          Challenges for the computer vision application
                                                          are the automatic calculation of the optimal path
                                                          for the fly-through through the center of the
                                                          winding colon, bronchus or vessel. Clever new
                                                          visualizations have been designed to screen the
                                                          foldings in the colon for polyps at both the for-
                                                          ward as backward pass simultaneously: unfolding
                                                          (VILANOVA 2003) (see Fig. 9.3) and viewing an
                                                          unfolded cube (VOS et al. 2003) (see Fig. 9.4).
Fig. 9.2. Volume rendering of the heart and coro-
                                                                 Segmentation is the process of dividing
naries (Terarecon Inc)
                                                          the 3D dataset in meaningful entities, which are

       The user can change the opacity settings           then visualized separately. It is essential for 3D

by manipulating the so-called ‘transfer func-             viewing by, e.g. cut-away views, and also, unfor-

tion’, this function giving the relation between         tunately, one of the most difficult issues in com-

the measured pixel value from the scanner and             puter vision. It is discussed in more detail in

the opacity. As there is an infinite number of set-       Sect. 9.5. When clear contrasts are available, such
as in contrast enhanced CT or MR angiography           9.5         Computer Aided Detec-
and bone structures in CT, the simple techniques
                                                       tion (CAD)
of thresholding and region growing can be em-
ployed, up to now the most often used segmenta-        One of the primary challenges of intelligent soft-
tion technique for 3D volume visualization.            ware in modern workstations is to assist the hu-
                                                       man expert in recognition and classification of
                                                       disease processes by clever computer vision algo-
                                                       rithms. The often used term ‘computer-aided di-
                                                       agnosis’ may be an overstatement (better: ‘com-
                                                       puter-aided detection’), as the final judgement
                                                       will remain with the radiologist. Typically, the
                                                       computer program marks a region on a medical
                                                       image with an annotation, as an attention sign to
                                                       inspect the location or area in further detail. The
                                                       task for the software developer is to translate the
Fig. 9.4. Unfolded cube projection in virtual          detection strategy of the expert into an efficient,
colonoscopy. From VOS et al. (2003)                    effective and robust computer vision algorithm.
                                                       Modern techniques are also based on (supervised
       This also explains the popularity of
                                                       and unsupervised) ‘data mining’ of huge imag-
maximum intensity projection, where pixels in
                                                       ing databases, to collect statistical appearances.
the 2.5D view are determined from the maximum
                                                       E.g. learning the shape and texture properties of a
along each ray from the viewing eye through the
                                                       lung nodule from 1500 or more patients in a
dataset (such a diverging set of rays leads to a so-
                                                       PACS database is now within reach. Excellent
                                
called ‘perspective rendering’ ). As this may
                                                       reviews exist on current CAD techniques and the
easily lead to depth ambiguities, often the more
                                                       perspectives for CAD (DOI 2006; GILBERT and
appealing ‘closest vessel projection’ (CVP) is
                                                       LEMKE 2005). The field has just begun, and some
applied, where the local maximum values closest
                                                       first successes have been achieved. However,
to the viewer is taken. The sampled points of the
                                                       there is a huge amount of development still to be
(oblique) rays through the dataset are mostly lo-
                                                       done in years to come.
cated in between the regular pixels, and are calcu-
                                                              Some advances in CAD techniques that
lated by means of interpolation.
                                                       have brought good progress are in the following
                                                       application areas.
Fig. 9.5a–c. Virtual colonoscopy with surface smoothing.
a Original dose (64 mAs); b 6.25 mAs; c 1.6 mAs. From PETERS (2006b)



       Mammography: this has been the first            ceeding some threshold are possible candidates
field where commercial applications found              for further inspection.
ground, in particular due to the volume produc-               The location of the nipple is important as
tion of the associated screening, the high resolu-     a general coordinate origin for localization refer-
tion of the modality and the specific search tasks.    ences with, e.g. previous recordings. The general
Typical search tasks involve the automated detec-      statistical ‘flow’ of line structures points towards
tion of masses, micro-calcifications, stellate or      the nipple; the location can reasonably well be
spiculated tumors, and the location of the nipple.     found by modelling the apparent statistical line
       How do such algorithms work? Let us             structure with physical flow models.
look in some detail to one example: stellate tumor            The role of MRI in breast screening is ris-
detection (HOFMAN et al. 2006). As breast tissue       ing. As in regular anatomical scans, too many
consists of tubular structures from the milk-          false negative detections are found, and current
glands to the nipple, tumor extensions may pref-       attention now focuses on dynamic contrast en-
erably follow these tubular pathways. In a projec-     hanced MRI. The rationale is the high vascularity
tion radiograph this leads to a spiculated or star-    of the neoplasm, leading to a faster uptake and
shaped pattern. The computer will inspect the          outwash over time of the contrast medium com-
contextual environment of each pixel (say 50 ×         pared to normal tissue. Current research focuses
50 pixels) on the presence of lines with an orien-     on the understanding of this vascular flow pattern
tation pointing towards the relevant pixel. In this    (e.g. by two-compartment modelling) and the
way a total of 2500 ‘votes’ are collected for each     optimal timing of the image sequence.
pixel. The pixels with a voting probability ex-
       Polyp detection in virtual colonography:         9.6         Atlases
polyps are characterized by a mushroom-like ex-
trusion of the colon wall, and can be detected by       The use of interactive 3D atlases on medical

their shape: they exhibit higher local 3D curva-        workstations is primarily focused on education

ture (‘Gaussian curvature’) properties. These         and surgery. As an example, K.-H. Höhne’s pio-

can be detected with methods from ‘differential         neering Voxel-Man series of atlases (HOFMAN et

geometry’ (the theory of shapes and how to             al. 2006) was initiated by the ‘visible human pro-

measure and characterize them), and highlighted         ject’. Atlases for brain surgery (e.g. the Cerefy

as, e.g. colored areas as attention foci for further    Brain atlas family; NOWINSKI et al. 2005) now

inspection.                                             become probabilistic, based on a large number of

       Methods have been developed to carry out         patient studies.

an electronic cleansing of the colon wall when
contrast medium is still present. An interesting
current target is possible to reduce strongly the
radiation dose of the CT scan, and still be able to
detect the polyp structures, despite the deteriora-
tion of the detected colon wall structures. Clever
shape smoothing techniques and edge-preserving
smoothing of the colon surface have indeed en-
abled a substantial dose reduction.
       Thorax CAD: here the focus is on the
automated detection of nodules in the high reso-
lution multi-slice CT (MSCT) data, on the detec-
tion of pulmonary emboli, and of textural analysis      Fig. 9.6. The famous Voxel-Man atlas explored
by classification of pixels, e.g. for the quantifica-   many types of optimal educational visualization.
tion of the extent of sarcoidosis. See SLUIMER et       From HÖHNE (2004)
al. (2006) for a review.
       Other CAD applications include calcium
scoring, used to detect and quantify calcified
                                                        9.7         CAD/CAM Design
coronary and aorta plaques, analysis of retinal
fundus images for leaking blood vessels as an           Workstations can also assist in the creation of
early indicator for diabetes, and the inspection of     implants from the 3D scans of the patients. This
skin spots for melanoma (of particular attention        is a highly active area in ENT, dental surgery,
in Australia).                                          orthopedic surgery and cranio-maxillofacial sur-
gery. Many design techniques have been devel-         ment and register the DTI data with anatomical
oped to create the new shapes of the implants,        data, and find fiber crossings and endings auto-
e.g. by mirroring the healthy parts of the patient    matically. An interesting development is the
of the opposite side of the body, 3D region grow-     photorealistic rendering of the tiny bundle struc-
ing of triangulated ‘finite element models’ in the   tures (with specularities and shadows), based on
assigned space, etc. The ‘standard tesselation        the physics of the rendering of hair.
language’ (STL) is a common format to de-
scribe surfaces for 3D milling equipment for
rapid prototyping, such as stereolithography
systems, plastic droplets ditherers, five-axes
computerized milling machines, laser powder
sintering systems, etc. Many dedicated rapid pro-
totyping companies exist (e.g. Materialize Inc.,
see also www.cc.utah.edu/~asn8200/rapid.html).
In the medical arena several large research insti-
tutes are active in this area (Ceasar, Berlin; Co-
                                                      Fig. 9.7. Muscle fibers tracked in a high-
Me, Zürich).
                                                      resolution DTI MRI of a healthy mouse heart.
                                                      Lighting and shadowing of lines combined with
9.8 Diffusion Tensor Imaging
                                                      color coding of helix angle (h). From PEETERS et
                                         
       (DTI ) – Tractography                          al. (2006a)

Three-dimensional (3D) visualization of fiber
tracts in axonal bundles in the brain and muscle
fiber bundles in heart and skeletal muscles can       9.9           Registration
now be done interactively. The images are no
                                                      Registration, or matching, is a classical technique
longer composed of scalar (single) values in the
                                                      in image analysis (HAJNAL et al. 2001). It is em-
voxels, but a complete diffusion tensor (a 3 × 3
                                                      ployed to register anatomical to anatomical, or
symmetric matrix) is measured in each voxel.
                                                      anatomical to functional data, in any dimension.
       The three so-called eigenvectors can be
                                                      Examples are MRI-CT, PET-CT, etc. The con-
calculated with methods from linear algebra;
                                                      struction of a PET and a CT gantry in a single
they span the ellipsoid of the Brownian motion
                                                      system effectively solves the registration problem
that the water molecules make at the location of
                                                      for this modality.
the voxels due to thermal diffusion. Complex
                                                             The matching can be global (only transla-
mathematical methods are being investigated to
                                                      tion, orientation and zooming of the image as a
group the fibers in meaningful bundles, to seg-
whole) or local (with local deformation, also         of the imaging beam) can be enhanced by such
called warping). Registration can be done by         techniques as (adaptive) histogram equalization.
finding correspondence between (automatically
detected) landmarks, or on the intensity landscape    9.11 Quantitative Image
itself (e.g. by correlation). There is always an             Analysis
entity (a so-called functional) that has to be
                                                      This is the fastest growing application area of
minimized for the best match: e.g. the mean
                                                      medical workstations. The number of applications
squared distance between the landmarks, a Pier-
                                                      is vast, every major vendor has research activities
son correlation coefficient, or others. In particu-
                                                      in this area, and many research institutes are ac-
lar, for multi-modality matching, the mutual in-
                                                      tive. To quote from the scope of ‘Medical Image
formation (MI) has been found to be an effec-
                                                      Analysis’, one of the most influential scientific
tive minimizer. As an example, in MRI bone
                                                      journals in the field:
voxels are black and in CT white; they show as a
                                                              “The journal is interested in approaches
cluster in the joint probability histogram of the
                                                      that utilize biomedical image datasets at all spa-
MR vs CT intensities. The MI is a measure of
                                                      tial scales, ranging from molecular/cellular imag-
entropy (disorder) of this histogram.
                                                      ing to tissue / organ imaging. While not limited to
9.10        RT Dose Planning                          these alone, the typical biomedical image datasets
                                                      of interest include those acquired from: magnetic
The accuracy of radiotherapy dose calculations,
                                                      resonance, ultrasound, computed tomography,
based on the attenuation values of the CT scan of
                                                      nuclear medicine, X-ray, optical and confocal
the patient, needs to be very high to prevent un-
                                                      microscopy, video and range data images.
derexposure of the tumor and overexposure of the
                                                              The types of papers accepted include
healthy tissue. Typically the isodose surfaces are
                                                      those that cover the development and implemen-
calculated and viewed in 3D in the context of the
                                                      tation of algorithms and strategies based on the
local anatomy. Increasingly the images made in
                                                      use of various models (geometrical, statistical,
the linear accellerator with the electronic portal
                                                      physical, functional, etc.) to solve the following
imaging device (EPID) are used for precise lo-
                                                      types of problems, using biomedical image data-
calization of the beam and repeat positioning of
                                                      sets:
the patient, by precise registration techniques.
The low contrast images (due to the high voltage
Fig. 9.8. Multimodality MRI of atherosclerotic plaque in the human carotid artery: (w1) T1-weighted 2D
TSE, (w2) ECG-gated proton density-weighted TSE, (w3) T1-weighted 3D TFE, (w4) ECG-gated partial
T2-weighted TSE, (w5) ECG-gated T2-weighted TSE. Middle: Feature space for cluster analysis. Right:
classification result. From HOFMAN et al. (2006)

       Representation of pictorial data, visualiza-   are MICCAI, CARS, IPMI, ISBI and SPIE MI. In
tion, feature extraction, segmentation, inter-study   the following some often-used techniques are
and inter-subject registration, longitudinal / tem-   shortly discussed. There are excellent tutorial
poral studies, image-guided surgery and interven-     books (MOLECULAR       VISUALIZATIONS     undated;
tion, texture, shape and motion measurements,         YOO 2004) and review papers for the field.
spectral analysis, digital anatomical atlases, sta-          Segmentation is a basic necessity for
tistical shape analysis, computational anatomy        many subsequent viewing or analysis applica-
(modelling normal anatomy and its variations),        tions. Mostly thresholding and 2D/3D region
computational physiology (modelling organs and        growing are applied, but these often do not give
living systems for image analysis, simulation and     the required result. Proper segmentation is noto-
training), virtual and augmented reality for ther-    riously difficult. There are dozens of techniques,
apy planning and guidance, telemedicine with          such as model-based segmentation, methods
medical images, tele-presence in medicine, tele-      based on statistical shape variations (‘active
surgery and image-guided medical robots, etc.”        shape models’), clustering methods in a high-
       See also the huge amount of toolkits for       dimensional feature space (e.g. for textures), his-
computer vision: http://www.cs.cmu.edu/~cil/v-        togram-based methods, physical models of con-
source.html. Important conferences in the field       tours (‘snakes’, level sets), region-growing
methods, graph partitioning methods, and multi-      e.g. MUSICA (‘Multi-Scale Image Contrast Am-
scale segmentation.                                  plification’, by Agfa), and the Swedish Con-
       The current feeling is that fully automated    textVision (http://www.contextvision.se/). En-
segmentation is a long way off, and a mix should      hancement is often used to cancel the noise-
be made between some (limited, initial) user-         increasing effects of substantially lowering the X-
interaction (semi-automatic segmentation).            ray dose, such as in fluoroscopy and CT screen-
       Feature detection is the finding of spe-      ing for virtual colonoscopy.
cific landmarks in the image, such as edges, cor-            Quantitative MRI is possible by calculat-
ners, T-junctions, highest curvature points, etc.     ing the real T1 and T2 figures from the T1 and T2
The most often used mathematical technique is         weighted acquisitions, using the Bloch equation
multi-scale differential geometry (TER HAAR          of MRI physics. Multi-modal MRI scans can be
ROMENY 2004). It is interesting that the early        exploited for tissue classification: when different
stages of the human visual perception system          MRI techniques are applied to the same volume,
seem to employ this strategy.                         each voxel is measured with a different physical
       Image enhancement is done by calculat-        property, and a feature space can be constructed
ing specific properties which then stand out rela-    with the physical units along the dimensional
tive to the (often noisy) background. Examples        axes: e.g. in the characterization of tissue types in
are the likeness of voxels to a cylindrical struc-    atherosclerotic lesions with T1, T2 and proton
ture by curvature relations (‘vesselness’), edge     density weighted acquisitions, fat pixels tend to
preserving smoothing, coherence enhancing,          cluster, as do blood voxels, muscle voxels, calci-
tensor voting, etc. Commercial applications are,     fied voxels, etc.
Pattern recognition techniques like neural net-       set of variable shapes and performing a ‘principal
works and Bayesian statistics may find the          component analysis’, a well known mathemati-
proper cluster boundaries.                            cal technique. The first eigenmode gives the main
       Shape can be measured with differential        variation, the second the one but largest, etc. Fit-
geometric properties, such as curvature, saddle      ting an atlas or model-based shape on a patient’s
points, etc. It is often applied when, e.g. in the   organ or segmented structure becomes by this
automated search for (almost) occluded lung ves-      means much more computationally efficient.
sels in pulmonary emboli, polyps on the colon                Temporal analysis is used for bolus track-
vessel wall, measuring the stenotic index, spicu-     ing (time-density quantification), functional maps
lated lesions in mammography, etc. A popular          of local perfusion parameters (of heart and brain),
method is based on ‘active shape models’,            contrast-enhanced MRI of the breast, cardiac out-
where the shape variation is established as so-       put calculations by measuring the volume of the
called shape eigenmodes by analyzing a large         left ventricle over time, multiple sclerosis lesion
growth / shrinkage over time, regional cardiac        ity. The source images are from two-photon
wall thickness variations and local stress/strain     microscopy, where the collagen is specifically
calculations, and in fluoroscopy, e.g. the freezing   colored with a collagen specific molecular imag-
of the stent in the video by cancellation of the      ing marker.
motion of the coronary vessel.                                 Another example is the detailed study of
                                                      the micro-vascular structure in the goat heart
9.12        Workstations            for     Life      from ultra-thin slices of a cryogenic microtome
Sciences                                              (degree of branching, vessel diameter, diffusion
                                                      and perfusion distances, etc.). Typical resolution
In life sciences research a huge variety of (high
                                                      is 25–50 micron in all directions, with datasets of
dimensional) images is generated, with many new
                                                      20003.
types of microscopy (confocal, two-photon,
cryogenic transmission electron microscopy,
etc.) and dedicated (bio-) medical small animal
scanners (micro-CT, mini PET, mouse-MRI,
etc.). The research on molecular imaging and
molecular medicine is still primarily done in
small animal models.



                                                      Fig. 9.10. A 3D visualization of a microtome stack
                                                      (40×40×40 m) of the micro-vasculature of a goat
                                                      heart (VAN BAVEL et al. 2006) [BENNINK 2006]

                                                               This research arena will benefit greatly in
Fig. 9.9a,b. Two-photon florescence microscopy
                                                      the near future from the spectacular developments
of collagen fibers of tissue-engineered heart-
                                                      in the diagnostic image analysis and visualization
valve tissue. a Result of structure preserving de-
                                                      workstations.
noising. From DANIELS et al. 2006

       There is great need for quantitative image
                                                      9.13 Computer-Aided
analysis. An example is, e.g. the measurement of               Surgery (CAS)
quantitative   parameters   that   determine   the
                                                      In the world of CAS some very advanced simula-
strength of newly engineered heart valve tissue of
                                                      tion and training systems (KISMET, Voxel-Man)
the patient’s own cell line, such as collagen fiber
                                                      have been created. Especially in dental implants,
thickness, local orientation variation and tortuos-
craniofacial surgery and laparoscopic surgery
there are many and highly advanced systems to-
day. Surgical navigation workstations are rou-
tinely displaying the combination of the anatomy
and the position and orientation of the instru-
                                                      Fig. 9.12a–c. Abdominal aorta aneurysm: a color
ments in the operating theatre.
                                                      coding of displacement (mm); b Von Mises strain;
                                                      c Von Mises stress (kPa). From   DE   PUTTER et al.
                                                      (2005)




                                                      9.14         New Developments

                                                      The visual perception of depth (when viewing
                                                      3D) data is helped enormously if the viewer can
Fig. 9.11. Virtual laparoscopy trainer (Origin:       move the data himself. There are many depth
Forschungszentrum Karlsruhe KISMET)                   cues (stereo, depth from motion, depth from per-
                                                      spective), but depth from motion is the strongest.
       An interesting development is the use of
                                                      That is why maximum intensity projections
complex fluid dynamics modelling, which en-
                                                      (MIP) are preferably viewed dynamically. By
ables the prediction of rupture chances in ab-
                                                      self-tracking also the muscle’s proprioceptors are
dominal aorta surgery, and selecting optimal
                                                      giving feedback to the brain, adding to the visual
therapeutic procedures with bypass surgery in the
                                                      sensation. The combination with human’s superb
lower aorta.
                                                      eye-hand coordination has led to the concept of
       In neurosurgery workstations can be em-
                                                      the Dextroscope (www.dextroscope.com), where
ployed in the calculation of an optimal (safest)
                                                      a (computer generated) view or object can be
insert path for electrodes for deep brain stimula-
                                                      manipulated under a half-transparent mirror,
tion (DBS), based on a minimal costs path avoid-
                                                      through which the viewer sees the display. Dis-
ing blood vessels and ventricles, and starting in a
                                                      plays can also be equipped with haptic (tactile)
gyrus. Workstations assist in inter-operative visu-
                                                      feedback systems, which are now commercially
alization by warping the pre-operative imagery to
                                                      available.
the real situation in the patient during the opera-
tion, by intra-operative MRI, or ultrasound.
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