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Chapter 35
Image Registration, Segmentation and Virtual Simulation
Vibeke Nordmark Hansen and Jean-Claude Rosenwald*
35.1 Introduction*
In the three previous chapters, the main imaging modali- ties for external radiotherapy treatment planning have been introduced. Computed tomography (CT) imaging is the most commonly used method for patient data acquisition.
It provides the information for the electron density, which is required for the dose calculation of the planning process.
However, frequently, several imaging modalities are combined to allow better delineation of target volumes and organs at risk according to the specifications given in Chapter 31. This requires some form of three-dimensional (3D) registration between these modalities The methods for performing image registration resulting in a multimodality 3D image dataset are described in Section 35.2. The process consisting of the extraction of specific anatomic structures from this dataset is called segmentation; it is addressed in Section 35.3.
To deliver a high dose to the tumour while limiting the dose to the healthy tissues, the beams must be directed precisely towards the target volume while minimising the inclusion of
*With contribution from Vincent Khoo (Section 35.2).
the organs at risk. In the early days of radiotherapy, this used to be done directly on the patient with the help of radio- logical images obtained from a simulator (see Section 9.2).
It is now, most of the time, performed using a computer to display the 3D-reconstructed representation of the patient with overlay of the beam representation. Such process is called virtual simulation and is described in Section 35.4.
Virtual simulation allows the shape of the treatment field to be adjusted precisely according to the shape of the target volume as obtained from high-resolution 3D imaging. Beam shaping is repeated for each one of several beams converging towards the target, which results in a dose distribution (i.e.
a treatment volume) adapted to the shape of the target. This approach is usually referred to as 3D conformal radiotherapy.
A more advanced solution consists in combining several field shapes for each beam direction in order to create intensity- modulated patterns. When combined, these patterns will generate an optimal dose distribution on the basis of pre- defined criteria. This technique, called intensity-modulated radiotherapy (IMRT) will be dealt with in Chapter 37.
CONTENTS
35.1 Introduction ... 705
35.2 Image Registration ... 706
35.2.1 Fundamentals of Multimodality Image Registration ... 706
35.2.2 Extrinsic Methods of Image Registration ... 706
35.2.3 Intrinsic Methods of Image Registration ... 706
35.2.3.1 Landmark-based registration ... 706
35.2.3.2 Structure-based registration... 707
35.2.3.3 Voxel-based registration ... 707
35.2.3.4 Deformable registration ... 707
35.2.4 Evaluation of Registered Images ... 707
35.3 Image Segmentation ... 708
35.3.1 Volumes of Interest – Structures ... 708
35.3.2 Tools for Delineation of Structures ... 709
35.3.3 Structure Editing – Application of Margins ... 709
35.3.4 Consensus Guidelines – Atlas-Based Segmentation ... 710
35.4 Virtual Simulation and Three Dimensional Conformal Radiotherapy ... 710
35.4.1 Principle of Virtual Simulation ... 710
35.4.2 Beam Axis Direction – the Beam’s-Eye View concept ... 710
35.4.3 Field Shaping ... 711
35.4.4 Digitally Reconstructed Radiographs (DRRs) ...712
35.4.5 Tangential Beams for Breast Treatment... 714
35.2 Image Registration
35.2.1 Fundamentals of Multimodality Image Registration
Despite the prominent role of CT imaging in radiotherapy treat- ment planning (Chapter 31), other imaging modalities such as magnetic resonance imaging (MRI) (Chapter 33) and positron emission tomography (PET) (Chapter 34) are of importance for modern radiotherapy treatment planning. Since such a multimo- dality approach allows anatomical and functional information to be combined, its major input is to help define the extent of the gross tumour volume (GTV) and of the clinical target volume (CTV) (see Section 31.2). In most cases, the principle is to use the CT dataset as a reference and to ‘map’ onto this dataset the information obtained from other modalities.* The process of spatial alignment of several imaging modalities is called image registration. It implies coordinate transformations. The process of combining the information is often called fusion.
In some cases, it is useful to register images from the same modality but acquired at different times. This is, for instance, helpful to account for the dose already received by a patient who comes back for re-irradiation in the same area. It is also use- ful to perform several CTs during the treatment course to get a measure of the inter-fraction motion of the internal target and organs at risk (e.g. assessment of bladder filling for treat- ing the bladder or of surrounding tissue such as when treating the cervix). Depending on the magnitude of this motion, re- planning might be necessary to adapt the treatment plan to the anatomy (see Section 48.4). An overview of the numerous pos- sibilities offered by image registration and data fusion in radio- therapy can be found in Kessler (2006). More information on the integration of these techniques in the treatment planning workflow can be found in the TG-132 report of the American Association of Physicists in Medicine (AAPM 2017b).
According to Maintz and Viergever (1998), medical image registration can be divided into two main categories:
• Extrinsic methods, based on additional objects vis- ible on all images of the multimodalilty dataset;
• Intrinsic methods, based on anatomical information belonging to the patient and visible on all images.
There is a third category allowing multimodality image fusion with very simple registration. It refers to situations in which several modalities are applied quasi-simultaneously while the patient remains in the same position (e.g. PET-CT hybrid device or combined CT-MRI device sitting in the same room).
Provided that the coordinate systems of the various devices are mutually calibrated, and that the patients are perfectly immo- bilised, registration is reduced to the application of translation vectors that are the same for all patients.
35.2.2 Extrinsic Methods of Image Registration Most common extrinsic methods used for radiotherapy treat- ment planning are based on invasive (e.g. screwed into the
*It is also possible to use the electronic density information from the CT, mapped onto the anatomical or functional information from the other imaging modalities.
skull) or non-invasive frames fixed to the patient. They are applied in the so-called stereotactic techniques (see Chapter 40). One could also use skin markers or implantable markers (seeds), compatible with all modalities of interest, such as are applied sometimes for daily verification and adjustment of the patient position (see Section 48.2.4.3). When skin mark- ers are used, imaging should be carried out on the same day without removing them. If this is not possible, the marker positions must be documented with photographs, diagrams and measurements of their position relative to reproducible anatomical landmarks. Non-invasive solutions (i.e. masks and skin markers) are less accurate than invasive methods.
Registration based on extrinsic methods implies gener- ally that a rigid transformation is applied (translations and rotations only). One constraint is that it must be decided in advance (prospectively): if an image acquisition has been already performed without a frame or markers, this solution is impracticable, and one must use an intrinsic method.
35.2.3 Intrinsic Methods of Image Registration Three-dimensional intrinsic registration methods are com- plex, and a multitude of parameters need to be considered in the matching procedure, such as the reproducibility of the patient position, the angle of the imaging plane, the image contrast and resolution, and the volume of the image set, including the width and gap distance of each slice. Intrinsic registration techniques may be applied retrospectively.
There are several approaches for intrinsic methods. They can be rigid (only translation and rotations are allowed) or deformable (allowing distortions along some directions). The deformable image registration can be global (applied to the full image content) or more often local (specific registration parameters differing for various sub-volumes).
Rigid deformation can be performed manually, automati- cally or semi-automatically (i.e. partly guided manually), whereas deformable registration will be automatic, possibly as a second step to a rigid registration.
Global rigid transformation algorithms are available in most treatment planning systems (TPSs). For such transfor- mations, the same patient set-up is essential. It cannot be stressed enough that getting the patient into a reproducible position is the first step to a reliable and robust radiotherapy treatment plan and radiotherapy treatment image match.
The only exception to this is for intra-cranial volumes, where different modalities can have different patient positioning, and the cranial bone can be used as a matching structure to ensure the registration of intra-cranial structures (see struc- ture matching in the following list).
More generally, there are three main classes of intrinsic methods: methods based on internal landmarks, on anatom- ical structures and on voxel intensities.
35.2.3.1 Landmark-based registration
In landmark matching (e.g. Hill et al. 1991), a number (usually at least five) of 3D anatomical reference points are nominated on each of the two image sets. These points are preferably easily identified, small and immobile, e.g. bony landmarks. The more points that are nominated, the more
accurate the final match of the images is likely to be. However, such points are often difficult to identify. It is important that the points used for registration are well separated in 3D. This method can be combined with external fiducial markers (the extrinsic approach). It is advisable that when external fidu- cials are used, some internal points are used as well. A well- designed registration algorithm will provide information on the mean and individual point errors so that the operator can concentrate on those points with the greatest uncertainty.
35.2.3.2 Structure-based registration
In structure matching (e.g. Levin et al. 1988), one or more 3D contours are outlined (e.g. the external contour and spe- cific anatomical structures that are well visualised on the two image sets) and then brought together in 3D space until the best mathematical match is made. The acquisition of contigu- ous and narrow CT images ensures good spatial definition and optimises the outcome. However, the boundaries of structures may not be identical in all modalities. For instance, there is likely to be more distortion of the external contour in an MR image compared with a CT image. The rigid surface-matching method is probably among the most popular methods in clini- cal use. It is quite successful for the head (Pelizzari et al. 1989).
The alignment of the structures is performed efficiently by using the chamfer algorithm introduced by Borgefors (1988).
This algorithm creates automatic threshold binary images of the bone from one modality and automatic contours of the same structure on the other modality. This binary image is then made into a ‘distance map’. The distance map is ‘scanned’
across the points belonging to the contours of the other image with an iterative process involving translation, rotation and scaling. The minimum of the product (cost function) is now the best match. These algorithms require no user interaction.
They work for CT-CT, CT-MRI and CT-SPECT* registration (van Herk and Kooy 1994). They are particularly accurate for CT-CT and therefore, can be used for CT to PET-CT, where the PET and CT datasets come automatically aligned from the PET-CT scanner (see Section 9.5.3).
35.2.3.3 Voxel-based registration
Voxel-intensity-based registration methods are fully auto- mated. One difficulty is the fact that the intensity has very different meanings for different modalities. However, several solutions exist, the most popular being the cross-correlation and mutual information algorithms. The cross-correlation algorithm is a measure of the similarity between the two image datasets. Mathematically, one dataset is used to do a convolution with the other dataset. When the two are most similar (hence, having high correlation in intensity match- ing), the convolution (or product) will be at a maximum.
The mutual information algorithm (e.g. Studholme et al.
1996) is more general and more often used for matching two different imaging modalities, such as CT to MRI, where
‘mutual structures’ may have different intensities. The match is achieved by maximising this mutual information.
*SPECT = Single-Photon Emission Computed Tomography (see Section 34.5).
35.2.3.4 Deformable registration
Although rigid image registration is more frequently used, deformable image registration is required when the patient has not been scanned in the same treatment position or if the anatomy of the patient has changed, which is often the case for bladder volumes despite the same patient set-up.
Deformable image registration can be based on structure matching or can be ‘hybrid’, combining structure-based and voxel-intensity-based matching.
For structure-based deformable image registration, the same structure must be accurately outlined on both datasets, and an elasticity parameter can be assigned to this structure. Each structure needs to be handled as a triangular mesh, which is then deformed to match the other corresponding structure.
The deformation matrix is then applied to the whole image.
For ‘hybrid’ deformable registrations, outlining structures is not required but outlined structures can be used to restrict the intensity-based deformable registration.
35.2.4 Evaluation of Registered Images
Manual registration methods are time consuming and require a high level of skill and regular practice. Automated approaches may allow more efficient registration, but without the devel- oped skill, errors in automatic registration may go unnoticed.
Whatever method is used, once the registration is complete, it is important that a careful visual check is carried out to verify that it has been satisfactorily performed. For this purpose, it is useful to have access to specific tools designed for accurate comparison of several image sets (West et al. 1996).
Graphical solutions are available on most TPSs. They allow scanning across the 3D image dataset and displaying instanta- neously any two-dimensional (2D) transverse, coronal and/
or sagittal sections obtained from any modality. Since these 2D images from two (or more) modalities are the result of the registration process, they are naturally registered to the same coordinate system. Then, several tools might be used, such as:
• Side-by-side display of two modalities, with linked cursors pin-pointing the location of matched ana- tomical points.
• Alpha blending, where superimposed images from each modality are displayed with variable transpar- ency on each of them that can be changed gradually from 100% to 0%.
• Thermal fusion, where one modality is displayed in grey scale, while the pixel intensity of the other one is mapped to a colour scale.
• Split image, where a line can be freely translated and rotated across the image. This line separates the dis- play into two parts, each of them from a different modality. Alternatively, there could be two orthog- onal lines generating four parts.
• Checker board, based on a similar principle, but where the display appears in squares with images of a different modality alternating in adjacent squares.
• Looking glass, where a square or circular cur- sor encompasses one modality, keeping the other modality as background. This is useful to check the match at specific points.
Some of these tools are illustrated in Figure 35.1.
These tools are useful during the segmentation phase (see Section 35.3) to check on the fly the consistency of the regis- tration. In addition, the possibility of switching the displayed images instantaneously from one modality to another is very useful for delineation of target and organs at risk (OARs).
Quantitative assessment of the registration accuracy is dif- ficult to perform, except for point-landmark matching, in which an estimate can be calculated from the average sepa- ration between matched pairs as soon as more than three pairs of landmarks have been used. For deformable image registrations, a good test is to deform ‘back’ to the origi- nal image set and evaluate the difference in the deformation vector fields to quantify by how much it differs from the original image set.
35.3 Image Segmentation
35.3.1 Volumes of Interest – Structures
In Section 31.2, the volumes of interest related to the target (GTV, CTV, PTV) and to the organs at risk (OAR and PRV) have been defined.* PTV and PRV are derived from the ana- tomically or functionally defined volumes (GTV, CTV and OAR) which are expected to be ‘seen’ on the image data- set pertaining to the patient. Ideally, these volumes would be automatically extracted from the dataset (automatic seg- mentation). However, despite efforts to develop robust and accurate segmentation algorithms, manual delineation of
*PTV = planning target volume; PRV = planning organ at risk volume.
FIGURE 35.1 Examples of graphical tools designed to check the validity of the registration between several modalities: here, CT (grey scale) and MRI (coloured scale). (a) Grey-scale side-by-side display with linked cursors represented as a cross (CT left, MRI right); (b) alpha blending; (c) split image; (d) checker board (3 × 3); (e) square looking glass.
such volumes is still used preponderantly for radiotherapy treatment planning. Since it is a time-consuming task, it is important to have access to user-friendly systems. In some instances, complementary semi-automated tools help to speed up the process and to improve the patient-to-patient consistency.
In addition to targets and OARs, other volumes of inter- est must be extracted from the image dataset. The identi- fication of the ‘body’ volume, as delineated by the external surface of the patient, is often required for dose calculation (see Part F). It is also useful to calculate the dose distribution in the remaining volume at risk (RVR) (see Section 31.2.9).
Finally, it helps to reconstruct a meaningful 3D represen- tation of the patient’s anatomy (sometimes called room’s-eye view or observer’s-eye view), where the beam directions and beam intersections with the patient’s skin are also visual- ised (see Figure 35.2). As seen in Part F, most modern dose calculation algorithms make direct use of the voxel density without any need to delineate patients’ inhomogeneities.
However, it is good practice to extract the lung volumes, since they can often be considered as an OAR, and they provide useful information for patient positioning. The same is true for some bony structures that can be used as landmarks and are advantageously included in beam’s-eye-view (BEV) rep- resentations (see Section 35.4.2) to assist patient set-up. For all these structures, automatic segmentation algorithms are generally quite successful.
All volumes of interest required for treatment planning are considered as structures. A structure is usually characterised by its name, its type (e.g. ‘body’, ‘PTV’, ‘OAR’, etc.) and is made of a series of planar contours defined by their (x,y) coordinates and located in parallel sections defined by their z position. For display purposes, a colour and a line style
may be assigned to each structure. Other attributes may be considered. To facilitate exchanges between various worksta- tions, all relevant information is standardised and included into a DICOM-RT object named ‘DICOM-RT Structure Set’ (see Section 49.4.1). It is also very important to adopt a consistent nomenclature at the departmental level as well as between institutions; hopefully, the standardisation recom- mended by the Task Group 263 of the AAPM (2018) will help to facilitate inter-institutional exchanges and organisa- tion of clinical trials (see Section 45.8).
35.3.2 Tools for Delineation of Structures
Manual delineation of structures such as the GTV is cur- rently performed by using a mouse to trace the contours of the structure on the display of the workstation. This is repeated in all adjacent axial slices in the region of interest (Goitein and Abrams 1983).
Several tools similar to those used in graphics editing soft- ware (e.g. Photoshop©) are generally available. In addition to the standard pencil, one can use, for instance, a brush to fill in regions of interest and an eraser. Contouring might be guided by some semi-automated functions, such as painting an area where the pixel intensities are similar or using magne- tism to force the manual tracing to follow a region delimited by a pixel intensity threshold more closely. Such semi-auto- mated procedures could be conducted in 2D or 3D. For 3D segmentation, an efficient solution is the region growing approach, whereby starting from a seed located either manu- ally or automatically, the neighbouring voxels with similar intensity are progressively included until the variation is larger than a specified threshold.
For the body contour, a simple threshold can generally be applied to a CT dataset. A Hounsfield unit (HU) threshold of −200 will usually work. For the lungs, a threshold can again be applied, but care needs to be taken to ensure that the airways are excluded from the lung volume.
Interpolating contours between slices, copying contours from one slice to the next, and tracing contours not only in axial planes but also in multiplanar orientations (e.g. sagittal and coronal) are useful options.
More information on image segmentation methods applied to radiotherapy may be found, e.g., in Yang et al. (2009), McNutt (2013) and Sharp et al. (2014).
35.3.3 Structure Editing – Application of Margins Since automated or semi-automated segmentation may fail for a given structure, it is essential to have the possibility to accept or reject the result. It is also important to have effi- cient tools to edit the existing structures, either to correct inconsistencies of automated processes or to adapt the struc- ture after reconsideration of what was done before.
An important feature for creating the CTV, PTV and PRV is the possibility of adding margins to an existing structure (typically the GTV or an OAR). It should be realised that the application of margins (isotropic or anisotropic) to a given structure is not a simple 2D expansion of the axial outlines;
a 3D treatment is required. A typical algorithm to perform a FIGURE 35.2 Room’s-eye view of a conformal prostate beam with a cut
plane of the patient CT including the 3D structures of the PTV (tur- quoise), bladder (blue), rectum (orange) and right femoral head (wire frame, khaki).
3D expansion can be thought of as a spherical ball (possibly with variable diameter depending on the direction) running around the surface of the original internal structure (e.g.
the CTV); the outer part of this ‘running ball’ would then generate the expanded structure (e.g. the PTV) (Stroom and Storchi 1997; Belshi et al. 1997).
Several dedicated workstations or TPSs offer the possibil- ity to create new structures from Boolean operations (e.g.
union or intersection) on existing structures. This could be useful for structures consisting of several sub-volumes into which it is required to calculate the global dose-volume distri- bution (see Section 43.3).
35.3.4 Consensus Guidelines – Atlas-Based Segmentation
To get reliable image segmentation, a common consensus of what should be included in specific outlines is desirable.
Consensuses for segmentation of many structures including CTVs have been published in the form of guidelines; e.g.
for pelvic normal tissue (Gay et al. 2012), for pelvic lymph nodes (Harris et al. 2015) and for genitalia in anal cancer (Brooks et al. 2015), and also for the prostate CTV after prostatectomy (Michalski et al. 2010a), for head and neck OARs (Brouwer et al. 2015) and for head and neck lymph nodes (Gregoire et al. 2003, 2006, 2014).
More recently, anatomical atlas-based outlining methods are increasingly being used and are available as standard on advanced TPSs. There are a number of different implementa- tions (Schipaanboord et al. 2019). One approach is to base the atlas on a single outline set and do a deformable image match from the atlas outlines onto the patient’s anatomy.
There are also more advanced learning anatomical atlas algorithms, whereby the atlas is updated based on correc- tions of previously accepted segmentations.
Atlas-based segmentation algorithms have been successfully developed for segmentation of normal regions of interest in the brain (Conson et al. 2014), the thorax (Yang et al. 2013) and the pelvis region (Young et al. 2011; Greenham et al. 2014; Delpon et al. 2016), and for head and neck lymph node segmentation (Sjöberg et al. 2013; Daisne and Blumhofer 2013). Despite consensus guidelines, there are still differences in outlines, and hence, not all clinicians favour atlas-based contouring; indeed, the resulting contours may be incorrect (Langmack et al. 2014;
Hoang Duc et al. 2015). However, with machine learning based on many image datasets from multimodality imaging, the auto- matic image segmentations will improve.
As adaptive on-line planning is becoming more popular (see Section 48.4), improvement of automatic image segmentation is required. However, for on-line adaptation, the best starting point is to use the outline set from the patient’s own original planning CT as the reference. This can already be done on many planning systems and generally yields an accurate segmentation based on deformation of the patient. This deformation is likely to be minor, as the patient is set up in the same position, and only internal anatomy changes may have happened.
A review of deep learning methods applied to automatic structure segmentation and more generally to the radiother- apy process can be found in Meyer et al. (2018).
35.4 Virtual Simulation and Three
Dimensional Conformal Radiotherapy 35.4.1 Principle of Virtual Simulation
When the patient data have been acquired and identification of the volume to be treated has been established, the next step consists in taking a decision about the beam set-up that will be used to give the prescribed dose to the target while preserving as much as possible the OARs and the remainder of the healthy tissues
Virtual simulation is now used in most situations as a replacement for the conventional simulation process. It is generally performed as part of the treatment planning pro- cess when the patient is no longer present (see Section 32.3).
The basis for virtual simulation is the availability of a full anatomical CT (or multimodal) dataset while the patient is in the treatment position. To allow a better appreciation of the basic anatomy and of the 3D extent of the various anatomical structures, 3D planning systems (Goitein et al. 1983; Mohan et al. 1988; Galvin et al. 1995) and dedicated workstations offer the possibility of viewing the image data not only in transaxial (i.e. the original CT slices) but also in sagittal and coronal views. Depending on the slice thickness, the sagit- tal and coronal views could show a coarser resolution in the cranio-caudal direction.
In the virtual simulation approach, the target and OARs must be outlined on all slices using one of the segmenta- tion methods described in Section 35.3. The projections of these outlines are shown on the transverse planes (where they were defined) and on the sagittal and coronal planes, either as contours or as colour wash. In many 3D planning systems, the user can toggle any structures on or off, which can be very helpful, as overlying structures can obscure each other and the underlying greyscale CT data. In addition to the three orthogonal 2D views, it is helpful to have a 3D surface- rendered view of the structures. In particular, it is useful to view the 3D volumes from different angles to decide on the beam directions.
A clear advantage of virtual simulation is that you can, without extra irradiation and without painful constraints for the patient, examine several treatment approaches or beam directions.
35.4.2 Beam Axis Direction – the Beam’s-Eye View concept
In some cases, ‘standard’ beam angles can be used (e.g. for the treatment of breast with tangential beams), but in oth- ers, the treatment planner selects the incident directions for the beams for individual patients. The planner needs to find the beam direction that treats the PTV while best avoiding irradiation of the OARs. This may be done through the use of various computer visualisations.
A very useful representation, complementary to the visu- alisation of the field limits in transverse sections, is the so- called beam’s-eye-view (BEV) concept (Goitein et al. 1983;
Mc Shan et al. 1990). The name beam’s-eye view refers to the
principle of showing what you ‘see’ when your eye is located at the source position. It consists in taking a plane perpen- dicular to the beam axis and computing the conical projec- tions on this plane of the field limits and of structures such as the PTV and OARs (Figure 35.3). As the beam direc- tion and couch angle are interactively changed, the projec- tions of the structures are shifted with respect to the beam edges until the best compromise between PTV coverage and OAR avoidance is achieved. In addition, the BEV concept offers the possibility of tailoring the field shape according to the shape of the target (PTV) while protecting the OARs.
It opened the way for 3D conformal radiotherapy (see for instance Webb 1993, 1997 and also Section 37.1).
35.4.3 Field Shaping
For each beam direction, the field shape is generated in the BEV from the projection of the PTV by adding a mar- gin to allow for the penumbra of the beam (Brewster et al.
1993). The ideal width of this margin depends on the field size and depth and whether the fields are coplanar or not.
In a coplanar situation, the margin will need to be between 4 mm and 6 mm in all directions. The shielding required used to be provided by custom-made low-melting-point alloy blocks, manufactured either by having the blocks manually designed using a printed template or by sending the digitised shape from the treatment planning system to a block cutter.
Nowadays, it is almost exclusively achieved with a multi-leaf collimator (MLC) (see Section 11.5.2).
MLCs do not produce smoothly rounded isodose distri- butions. This phenomenon was more crucial with the first generation of MLCs, since the width of each leaf was typi- cally 1 cm. Nowadays, widths of 0.5 cm or less are frequently
encountered. The geometric projection of the MLC leaves approximately defines the 50% isodose; hence, a margin to account for penumbra (~6 mm) must first be applied to the PTV to establish the required field shape. It is this field shape that is fitted by the MLC, using one of several shape- fitting algorithms. Generally, TPSs support fitting leaves to the outside, the inside or the middle of the MLC leaf (see Figure 35.4 and Section 23.5.4). In addition, some TPSs allow the area of overexposure and underexposure to be averaged. Optimising the collimator angle to obtain a bet- ter conformation to the target shape is also an option. In all cases, the conformity of the dose distribution to the PTV is the principal criterion in determining the optimal positions of the leaves.
For most MLC designs, main jaws are still present. They must be adjusted to reduce as much as possible the inter-leaf leakage without obscuring the MLC-delimited area. In prin- ciple, this adjustment should be performed during treatment planning. Generally, TPSs offer options for automated posi- tioning of the main jaws. In the direction parallel to the leaf travel, it is usual to add a safety margin of 5 mm to 10 mm exterior to the most retracted leaves. In the perpendicular direction, the beam edge could be defined by a main jaw, but one should avoid superimposing the edge of the jaw and the flank of a leaf, since it might be improperly taken into account by the dose calculation algorithm. A typical example of main jaw positioning with respect to the field shape delim- ited by the MLC is shown in Figure 35.5.
With IMRT, the field shape is not any longer defined by the planner. It is calculated automatically as part of the inverse planning process (see Section 37.3).
FIGURE 35.3 Beam’s-eye view representation of a conformal beam for a prostate treatment; same patient and beam as in Figure 35.2. Prostate +5 mm margin is PTV (turquoise), rectum (orange), bladder (blue), and right and left femoral head (wire frame, khaki). The outline of the field shape and the position of the MLC (using average fitting method) are shown in white.
FIGURE 35.4 Multi-leaf collimator (MLC) fitting to a smooth field aperture. Here, the aperture is elliptical (only one quadrant is shown).
Three possibilities for MLC fitting are shown: external to aperture (solid bold line, dark grey shaded); middle (light grey shaded); and internal (dotted line). It should be remembered that an additional mar- gin must be applied between the field aperture and the PTV to account for the beam penumbra.
35.4.4 Digitally Reconstructed Radiographs (DRRs) Just as real patient simulation uses fluoroscopy or conventional radiographs, virtual simulation uses DRRs to enable treat- ment fields to be visualised in relation to the patient’s anatomy.
The advantage of a DRR produced from a therapy CT dataset with the patient in the treatment position is that the data used for verification of the patient position relative to the treatment beams (see Section 48.2.3) are derived from the same dataset as that used for target localisation. This ensures full consis- tency between the treatment plan used to calculate the dose distribution and the actual beam delivery. However, to be use- ful tools in the clinic, DRRs must be of comparable quality to conventional radiographs. An understanding of how DRRs are produced will enable the user to ensure that the best use is made of the software to create and enhance DRRs.
DRRs are generated by summing the radiological thick- nesses along each ray line from the virtual source of the beam through the 3D patient model (the CT dataset) to a point in the plane of the DRR. For a sketch of the principles, see Figure 35.6. The geometry used to make the DRRs and the mathematical transformations required are described by Siddon (1981, 1985). The attenuation along the ray lines can be derived from the definition of the HU as follows (Section 32.2.1 and Killoran et al. 2001):
HU =1000m m-
mw w (35.1)
This can be rearranged in the form:
m m= æ +
èç ö
ø÷
w HU
1000 1 (35.2)
where m and mw are the effective linear attenuation coeffi- cients of tissue and water, respectively, at the energy used to produce the CT.
The linear attenuation along a ray line will be the sum of the attenuation in the voxels in the path of the ray line.
It is important to note that CT scans are acquired at about twice the energy used for conventional radiographs, so that the ratio of the photoelectric effect to the Compton effect is much reduced. Consequently, bone contrast is inherently much lower in DRRs.
Using the relationship of linear attenuation coefficient with energy, it is possible to generate DRRs that resemble simulator kV images (photoelectric) and megavoltage images (Compton) by recalculating radiological thickness maps according to the beam quality. Modifications can also be made to the mapping between CT HU values and relative electron density to emphasise different anatomical features.
For more details, see Sherouse et al. 1990; Cheng et al.
1987; Cullip et al. 1993; Killoran et al. 2001; Staub and Murphy 2013.
DRR quality is influenced by a number of factors related to both data acquisition and reconstruction algorithms:
• The CT data volume: For beam definition and vir- tual simulation, it is important that the CT dataset encompasses the body surface and extends to the whole treatment area, which can be some distance beyond the tumour, especially if non-coplanar beam arrangements are used.
• The thickness of the CT slices: This determines the resolution in the longitudinal plane and provides an objective limit to DRR quality. However, with the use of helical multi-slice CT scanners, the slice thickness is rarely more than 3 mm and can be as low as 0.75 mm, which gives good DRR quality.
• The ray tracing mode: This refers to the way the sum- mation is performed. It uses the nearest CT voxel intensity for fast reconstruction and interpolates between the voxels of adjacent slices or between the eight nearest voxels.
• The step size: This is the size of the increment used along the ray lines when summing up the attenua- tion coefficients to generate the DRR.
• The DRR resolution: This is the density of pixels (e.g. number of pixels per centimetre) on either rows or columns of the generated DRR.
It is possible to enhance DRRs by selectively altering the visibility of different structures according to their relative densities. This is often referred to as a digitally composited radiograph (DCR). For example, by ‘suppressing’ the soft tissue, a bone DCR can be generated, or by ‘suppressing’ the bone, a soft tissue DCR can be generated (Figure 35.7). It is also possible to generate a DCR based exclusively on the voxels encountered within a given depth range and therefore, to reinforce the contrast of the structures lying within this range. Several planning computers incorporate these fea- tures, which in principle, enable, for instance, airways and FIGURE 35.5 Illustration of the recommended position of the main
jaws (represented as white lines) with respect to the MLC shape for an Elekta linear accelerator (MLCi multileaf collimator). Note that the main jaws are hiding the inter-leaf leakage regions in the upper and lower part of the figure.
FIGURE 35.6 The digitally reconstructed radiographs (DRRs) are generated by ray-tracing through the CT-image dataset. This example shows an antero-posterior (A-P) and a left lateral DRR of a pelvic treatment.
(b)
FIGURE 35.7 By changing the look-up table between CT numbers (HU) and densities (i.e. grey levels) as shown in (c) and (f), it is possible to create digitally composited radiographs (DCRs) adapted to various types of structures. Images (a) and (b) demonstrate a soft tissue DCR obtained from curve (c), where all tissues with CT numbers below 450 HU and above 1100 HU have been excluded. Images (d) and (e) demonstrate a bone DCR obtained from curve (f) where the densities of tissues with CT numbers larger than 1100 HU are enhanced linearly as a function of the CT numbers.
bones to be simultaneously enhanced, which is impossible with conventional radiographs from physical simulators.
With the increased number of CT scanners available in radiotherapy departments, x-ray simulators are tending to disappear, and virtual simulation is now also currently used for simple treatments such as parallel pairs. It increases the accuracy of the treatment and often reduces the time the patient needs to spend in the department.
DRRs are also useful to reduce the use of contrast agents administered to patients. One particular site is the treatment of the para-aortic nodes, where the protection of the kid- neys is crucial. Here, the patient benefits from the CT scan by avoiding the intravenous contrast that would be adminis- tered to show the extent of the kidneys. The planning is then performed by simultaneously looking at a coronal recon- struction (see Figure 35.8) and at the DRR. This combina- tion gives a full appreciation of the position of the kidneys (by scrolling through the coronal reconstructions) relative to the para-aortic nodes and allows manual positioning of the MLC leaves to shield them.
If the PTV and other structures have been outlined on the CT dataset, they can be overlaid on the DRR using the BEV approach. The field shapes delimited by conformal blocks or MLCs may also be added as overlays, as shown in Figure 35.9.
The main use of DRRs is to provide a means to check the patient set-up during beam delivery. The DRRs represent what should be achieved on the treatment machine, where they can be compared with portal images (see Chapter 13).
The matching between DRRs and treatment portal images can be achieved by superimposing some reference structures (mostly lung, bony landmarks or additional markers). On DRRs, they can be either drawn directly on the images or obtained from CT segmentation by BEV projection. They are then overlaid on portal images, the deviation between the field edges being representative of the set-up error (see Section 48.2.3). Therefore, it is important that DRRs are produced by skilled and experienced staff who have had the time to test the enhancing tools available and liaise
with the clinicians and the radiographers who will use the images.
Although DRR reconstruction is logically almost exclu- sively based on CT image datasets, it could be interesting to include information from other imaging modalities (see e.g.
Chen et al. 2007a).
35.4.5 Tangential Beams for Breast Treatment
Despite the European Society for Radiotherapy and Oncology (ESTRO) consensus guidelines on early-stage breast target volume delineation (Offersen et al. 2015), defining the beam from anatomical landmarks using a DRR is still common prac- tice in many centres for breast radiotherapy, where the whole mammary gland should be included in the treatment volume.
FIGURE 35.8 Example of para-aortic node radiotherapy showing side by side a coronal section through the CT image dataset (a) and an antero- posterior DRR (b). Kidneys are clearly seen on the coronal section (a), which can be scrolled in the antero-posterior direction through the full dataset and used to draw on the DRR the exact shape of the shielded area.
FIGURE 35.9 DRR of the anterior field of a prostate conformal treat- ment including the PTV outlines (horizontal purple lines), the main jaws (red rectangle) and the MLC elements (white) as BEV overlaid projections.
FIGURE 35.10 Multiplanar CT-based representation of an anatomical set-up for breast treatment using tangential beams. In the left-hand col- umn, the isocentre is in the middle of the breast (technique 1). In the right-hand column, the isocentre is at the superior–posterior border of the fields (technique 3). Both treatments are coplanar, i.e. without couch rotation.
To simulate tangential breast treatments using virtual simu- lation or fluoroscopic imaging, the gantry angle that covers the breast volume while minimising the amount of underlying lung tissue in the fields can be found by positioning the beam in the TPS or by real-time fluoroscopic adjustment of the beam position. Lead markers can be placed on the skin at the margin of the palpable breast to facilitate this process. Either the breast is marked all around by a wire, or the medial, infe- rior and lateral borders are marked by radio-opaque markers.
The opposing beams should be angled such that their internal beam edges are non-divergent and include a minimal amount of lung. This is achieved by ensuring that the posterior edges of the beams are defined by the line connecting the medial and lateral border The relative gantry angles can be worked out by trigonometry using the field width. The inferior border is defined by the inferior mark placed on the patient, ensuring that there is at least a 1 cm margin in air below the breast and the superior border is at the level of the sternal notch.
For tangential breast beams, it is also advantageous to keep a non-divergent or vertical beam edge at the superior border to allow the addition of an axillary field (which can then be added later even if this is not prescribed as primary treat- ment). Either this can be achieved by using trigonometry to compensate by adjusting the couch angle and the collimator angle of the tangential fields, or one can use half beam blocks (to achieve asymmetric diaphragms) provided the field length is not restricted (see also Section 36.8.3).
In practice, the position of the isocentre determines how the planner needs to ensure non-divergent borders towards the lung and superiorly. The superior limit is of special importance if a supra-clavicular field and/or irradiation of the axillary nodes are included in the plan request.
The three most common ways of choosing the position of the isocentre for breast plans are:
1. The isocentre is placed centrally in the breast tis- sue. This gives near symmetric field sizes, but the opposing tangential fields need to compensate
for the divergence posteriorly; hence, the beams are generally 185–190 degrees apart. The math- ematics to calculate the divergence angle a is
sin( )a =x/SAD, where x is the distance of the field from the central axis to the posterior border and SAD is the source-to-axis distance (100 cm for most linear accelerators).
2. The isocentre is placed at the posterior border of the breast field (half asymmetric field); then, an oppos- ing field will give a non-divergent border towards the lung.
3. The isocentre is placed at the superior–posterior border of the treatment field (double asymmetry).
This ensures non-divergent borders both towards the lung and superiorly, which allows an easy match of a supra-clavicular and/or axillar field. The supra- clavicular field will then use the same isocentre as the tangential breast fields and have a dosimetric match at the central axis, which is the common border.
Technique 1 and technique 3 are illustrated in Figure 35.10.
For technique 1 or 2, adjustment to the couch and colli- mator angles is required to achieve a non-divergent superior border. For all three isocentre placements, it is possible to get non-divergent superior and posterior borders. A possible disadvantage of using technique 3 is that there is a limitation to the asymmetric half-beam field size,* and hence, it may not be possible to cover the full length of the breast volume.
For a more comprehensive and detailed discussion of breast planning, see Donovan et al. (2012).
After the beam set-up has been achieved, its validity must be checked by calculation of the dose distribution. Small adjustments to the plan (i.e. beam weights, beam modifiers or shielded area) might be performed without resuming the simulation. However, large changes (i.e. beam directions) could necessitate a repeated simulation.
*On most linacs, the maximum value is 20 cm, but for some of them, it is as little as 11 cm.