A new algorithm for autoreconstruction of catheters in computed ...

A new algorithm for autoreconstruction of catheters in computed tomography based brachytherapy treatment planning N. Milickovic Department of Medical Physics & Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach, Germany. D. Baltas Department of Medical Physics & Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach, Germany. and Institute of Communication & Computer Systems, National Technical University of Athens, 15773 Zografou, Athens, Greece. S. Giannouli Department of Medical Physics & Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach, Germany. and National Technical University of Athens, Dept. of Electrical & Computer Engineering, 15773 Zografou, Athens, Greece. M. Lahanas Department of Medical Physics & Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach, Germany. N. Zamboglou Department of Medical Physics & Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach, Germany. and Institute of Communication & Computer Systems, National Technical University of Athens, 15773 Zografou, Athens, Greece.

Corresponding author: Natasa Milickovic Dept. of Medical Physics & Engineering Strahlenklinik Städtische Kliniken Offenbach Starkenburgring 66 63069 Offenbach am Main, Germany Tel.: + 49 - 69 - 8405 - 4480 or - 4522 Fax: + 49 - 69 - 8405 - 4481 E-mail: [email protected]

Natasa Milickovic et al., A new algorithm for autoreconstruction of catheters…

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ABSTRACT This paper describes innovative software for automatic reconstruction, which we term autoreconstruction, of plastic and metallic brachytherapy catheters using CT data. No such automatic facility has previously existed in any treatment planning software. The patient data consists of a set of post-implantation CT images with the catheters in situ in their final positions. This new software solves those difficulties which arise when the catheters are intersecting or when loop techniques are used. With the software algorithms, catheter reconstruction time is significantly reduced and accuracy is also improved when compared with that achieved using the classical manual method of CT slice by CT slice reconstruction.


I.

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INTRODUCTION

Brachytherapy1,10,11,17 is an established treatment method for cancer which originated following the discovery of radium by Marie Curie at the end of the 19th century. Technological progress and follow-up on many brachytherapy patients has resulted by the 1980s in the replacement radium sources by miniature 192Ir sources which are used within computer controlled afterloading machines such as microSelectron HDR11,17. Although other radioactive isotopes have been and are used in brachytherapy, for example 60Co, 137 Cs, 125I and 198Au, the isotope 192Ir is now the HDR isotope of choice. In the radium era only low dose rate (LDR) treatments could be applied because of the relatively low specific activity (amount of radioactivity per unit volume) of radium sources. 192Ir sources have a much higher specific activity and have enabled HDR treatments to become feasible. Typical LDR treatments had a duration of 4-7 days continuous irradiation whereas a typical HDR treatments are fractionated and an individual treatment fraction may last only 5-10 min depending on the dose prescription and the cancer site. Computers are now essential for the treatment planning procedure, particularly for CT based planning techniques. The planning is mainly based on reconstructing the applicator geometry with the aid of radiographs, termed projectional reconstruction method, PRM, and in some cases a few anatomically defined reference points. CT, MR and ultrasound, although available to most Departments of Radiation Oncology have until now played only a secondary role in the treatment planning procedure. The reconstruction of brachytherapy implants has usually been performed using two or more projected radiographs of the patient that were taken after the implantation. The catheter points are then digitized separately on the films or alternatively, the films are scanned using a high resolution film scanner. A number of algorithms have been developed for catheter reconstruction from such projected radiographs2,6,8,13,14,17. The most time consuming and error sensitive part of the treatment planning procedure is catheter reconstruction because more than 30 catheters can be present in some interstitial brachytherapy clinical applications. Brachytherapy treatment planning is still often based on the use of orthogonal radiographs but a significant advance is the use of 3D CT based planning which will become more frequent in the future3,4,5,7,9,15,16,18,19. When compared to the use of PRM, image based treatment planning methods has been shown to significantly reduce the time required for the catheter reconstruction process15. Our motivation for this study was to provide a useful improvement in the speed of the catheter reconstruction process which in turn will bring patient benefit because the time of the brachytherapy process overall will be reduced and the patient will gain in comfort. A psychological aspect which is often forgotten for the cancer patient is the time they have to wait before treatment because of the length of the planning process. The longer the time wait the more anxious and worried the patient becomes.


II.

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MATERIALS AND METHODS

A. Introduction We undertook an analysis of 35 implants in actual clinical practice using the PLATO♣ treatment planning system software. This standard procedure was necessary before we developed our algorithm because we wished to know the time taken in standard practice for various aspects of the procedure such as the manual CT slice by CT slice catheter reconstruction. This took an average of 41% of the total treatment planning time without taking into account image processing and contouring. The reconstruction time per catheter took on average 147.1 s. Searching for implanted catheters is made using a sequence of CT slices and is based on the Hounsfield number, HU, of the catheter material (see Appendix), catheter outer diameter, interslice distance, slice thickness and geometry of the catheter shape on the CT slices. If there is no patient movement during CT data acquisition there is virtually no error in the autoreconstruction process. Generally, 3 mm slice thickness and 3 mm interslice distance are satisfactory. This is because the catheter information could be lost when these thicknesses exceed 3 mm and a catheter passes through the CT slices and is approximately parallel to the slices. This happens due to the slice thickness which is large when compared to the catheter outer diameter of 1.9 mm - 2 mm. Pixel size should be small enough such that the catheter shape on a CT slice is not lost in the image reconstruction. 1)

Hardware and Software Description

We developed the software using a Silicon Graphics O2 Workstation, CPU MIPS R5000. Rev. 2.1, with processor speed 180 MHz and operating system IRIS 6.3, to perform the plastic and metallic catheter autoreconstruction. Algorithms are written in ANSI C++ programming language. 2D graphics have been made with the assistance of OpenGL libraries, and 3D graphics with Open Inventor libraries. OSIF/Motif was used to create the windows, buttons and menus that are a part of GUI. We have also developed classes for all objects used in the algorithms. This provides code reusability and modularity. 2)

Definition of Terms

The area on the CT image that presents the catheter cross-section shape through that image is termed catheter area. Any point or pixel that belongs to this area is termed catheter point or pixel. The set of image pixels recognized by the algorithm as belonging to the catheter area on CT slice is termed a catheter recognized area. Any point or pixel belonging to this area is termed a catheter recognized point or pixel15. That part of the algorithm which evaluates a catheter recognized area is termed a recognition process. One central point or pixel from a catheter area is considered to represent the catheter describing point15 on the CT slice. This is termed the reconstruction process part of the algorithm. Each catheter is considered to be a geometrical entity that can be described by a set of arbitrary points lying on the CT slices: the catheter describing points15. We can distinguish between the following two catheter situations. (I) The catheter is lying in-volume, which means that it cuts more than one CT slice. (II) The on-plane situation where the entire catheter is lying only in a single plane. This plane can be a CT slice or an oblique cut. The user identifies a catheter by only a single point on any CT slice or oblique cut. 3)

♣

GUI

Nucletron International B.V., Veenendaal, The Netherlands


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The GUI consists of four windows which are: (i) menus and buttons, (ii) 2D CT slice viewing, (iii) 3D viewing of reconstructed catheters, and (iv) oblique slice viewing and controls. After reconstruction the results can be seen in both the 3D viewing window and on any slice intersected by catheters. The GUI is used to define the following five parameters for the operation of the algorithm. (a) Catheter type. “Plastic” or “metal” which selects ranges of HU values for catheter pixel identification. Default ranges can be overridden to take into account, for example, the effect of slice thickness on the HU values of the catheters. Refer to the Appendix to see how the default HU ranges were determined. (b) Search region. “On-plane” (in a single slice or an oblique cut) or “in-volume” (not in a single slice). (c) Search direction. This can be defined either as forward, backward or in both directions, according to the increase of the CT slice number12. (d) Loop techniques. Included or excluded. (e) Catheter characterization. The catheters’ dwell positions15 will be produced automatically after the autoreconstruction process is completed. B. Algorithm Description Before the algorithm starts, the user needs to identify each catheter by only one single point P. This is given using the GUI on any CT slice or oblique plane. 1)

In-volume searching

Firstly we need to find all pixels that belong to the catheter area around the given point P. The pixel region around P is searched for all edge-connected points with HU values within the selected HU range. We place them in a temporary list. Edge-connected pixels are those two pixels that have one common edge. We now find a central point of the recognized catheter area, PC. Point PC will be the first catheter describing point in the search:

PC .x =

1 n ⋅ ∑ C .x n i=1 i

PC .y =

1 n ⋅ ∑ C .y n i=1 i

PC .z = Z CT

where n is the total number of catheter recognized pixels and Ci.x and Ci.y are their x and y coordinates. ZCT is the z coordinate of the current CT slice where the recognition process was made. We next need to find catheter direction. For the moment, the only data we have are one catheter describing point PC and group of catheter recognized pixels. When the catheter intersects (cuts) a CT slice an ellipse shape is seen on the image. That is, except in the case where the catheter cuts the slice orthogonally or lies entirely within the CT slice. We find the two most distant pixels of the group that lie on the ellipse’s major axis. This search will be made between all pairs of pixels of the previously formed list. We assign these two pixels the notation P1 and P2. We next find the angle ϕ between the normal on CT slice and the catheter, Fig. 1. We assign the notation r1 to the catheter outer diameter defined by user and r2 to the distance between two pixels P1 and P2. Then:


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  ϕ = arccos r1  .    r2  We next find two points PC1 and PC2 (see Fig. 1) that belong to the line passing through the two pixels P1 and P2 and are at a distance d from the point PC, where the

d = h ⋅ tan( ϕ)

and h is the interslice distance. We next evaluate the new possible positions of catheter points PC11, PC12, PC21 and PC22, see Fig. 1. The next step is to search the 9 x 9 pixels region surrounding the pixels to which these the four points PCij , i,j=1,2 belong. We assign dij to the distances of each point PCij to the nearest pixel PĆij which belongs to catheter area and is within the 9x9 pixels region PCij. If d1=d11+d22 is less than d2=d12+d21, we accept the catheter recognized points PC11 and PC22, and we continue searching only in the direction for which d11 and/or d22 is within the 9x9 region searched. This procedure is similar to that where d2 is less than d1. We now have the first catheter describing point (PC) in the catheter point list and the first searching direction is defined. We can continue searching for the remainder of the catheter describing points in both the forward and the backward directions relevant to the first catheter describing point which was found. A further explanation for the backward searching algorithm is as follows. Searching in the forward direction is made in an analogue way. Let us suppose that the catheter pixel PĆ12 is accepted as a catheter recognized point on the previous slice. We extract the catheter area A around PĆ12 and than calculate the central point of area A, CPĆ12, and place it in the catheter point list as the second catheter describing point. The next search direction is determined by extrapolation from points PĆ12 and CPĆ12. The new possible catheter point is calculated as an intersection of the line defined by the pair (PĆ12, CPĆ12) with the previous CT slice. In the same way we continue searching until we reach the first slice or there is no catheter point around the possible catheter point. When the searching in both directions is completed, we have found the entire set of describing points which are then saved in the catheter point list of a particular catheter. The same procedure is repeated for all catheters. 2) Artifacts As the physical dimensions of the beam are not ideal, because we are not dealing with the point source, and the process of digitization is applied after an image acquisition, each image has a slice thickness that is in the best case 2 mm. When two or more catheters are very near to each other on a CT slice they may be seen as a single catheter, see Fig. 2. This happens usually in the case of metallic catheters which have very high HU numbers and are placed in the soft or fat tissue characterized with a much lower HU number. If we do not make all the necessary checks and corrections the algorithm could miss the correct catheter direction. In this case it is not adequate merely to use a simple region following to choose which of several possible pixel clusters on the next plane should be associated with particular catheter and therefore we must impose an additional constraint of continuity in catheter direction. Fitting a catheter curvature to the already accepted catheter points on one side of the crossing plane, each catheter’s axis is projected through the crossing planes beyond to identify which pixels belong to which catheter and restart the search. 3) Loops in-volume If the catheter makes a loop in a volume, Fig. 3, the search starts in the same way as previously described. The catheter area around the two end catheter describing points is then analyzed to find out at which end to continue searching and to find out the new


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searching direction. This process is analogue to the searching of the first catheter direction that was described. The difference is that in this case when we find the angle ϕ and distance d we search for the next catheter point in the direction opposite to that of the previous search, Fig. 3. We then continue with the autoreconstruction in exactly the same way as described for non-loop techniques. When we have found the last catheter describing points the autoreconstruction process has been completed and the process of automatic tip characterization and creation of catheter dwell points is made15. 4)

On-plane searching

In this case the entire catheter is laying on only one CT slice or calculated oblique cut. The search process is made in two dimensions. Our aim is now same as in the case of catheter in-volume search. From the group of catheter recognized points we want to extract the list of catheter describing points that will satisfactory describe catheter curvature. The user defines one catheter point using the GUI on the searching plane. The list of catheter describing points is then automatically extracted. 4.a

On-CT slice searching

In the case of a catheter which does not make a loop on the CT slice, the catheter describing points are extracted from the recognized catheter area in a simple way. We enter one temporary list A for all edge connected recognized catheter points and start searching from the given catheter point P. We then find the two most distant pixels of the group. We assign these two pixels as P1 and P2. These, together with the temporary list A are the input data for the searching algorithm. Suppose the case where the change of direction dy=|P1.y P2.y| along the y-axis is larger than the change of direction dx=|P1.x - P2.x| along the x-axis. Starting from j=jmin to jmax extract the group of points assigned Pj , where Pj.x=xmean of all recognized catheter points with same j (j is the pixel index corresponding to y coordinate). This provides what we term the catheter skeleton. We can accept them as catheter describing points or make one more adaptive step. This step is to choose from this group only the necessary number of points that will be enough to successfully describe the catheter curvature. We choose the end points of the subgroups that belong to the same line segment. We have therefore optimized the number of catheter describing points. 4.b

On-Oblique plane searching

In this case the search process is made in almost exactly the same way as in the case of on-CT slice searching. The only difference is in the way we extract catheter describing points from a catheter skeleton. We distinguish below between three cases: (a) If the oblique plane lies on the CT slice the catheter describing points are obtained in the completely same way as in on-CT plane case. (b) If the oblique plane lies between two CT slices the catheter describing points are obtained as in the case of the on-CT slice search where they are assumed to belong to the nearest CT slice and their z coordinate is set to the z coordinate of the nearest slice. (c) In the general case the catheter describing points are obtained as the intersections of the segments of the catheter skeleton (or segments [Pj , Pj+1], j=jmin to jmax-1, if the adaptive step is applied) with the CT slices. 4.c

Loops on-plane

When the catheter lies entirely on the CT or the oblique plane and also makes a loop, the search process differs from that previously described for non-loop plane techniques. We


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use the property of constant catheter diameter and the logic of a stepping algorithm to extract catheter describing points from the group of catheter recognized points. We start searching from the user given point P and find the first n catheter recognized pixels around P. From this group of pixels we determine in which direction the variation is larger: along the x-axis or y-axis (i, j direction in the pixel matrix). Assume that the variation along the y-axis is larger. We now find the central point of the group of points that belong to the same row i as the user given point P and place this point in a temporarily list of points PL. The rest of this stepping process is presented in Fig. 4. We now have an entire list of points PL from which we need to extract the list of catheter describing points. When this step is completed, we start extracting from one of the end points, A1 in Fig. 4. The catheter describing point near the chosen point A1 is taken as the center of the profile through the catheter points in either the horizontal or vertical direction, whichever profile is shorter. Now find the central point of the segment [A1 , A2], where the A2 is the next point from the list PL. That is the second catheter describing point. We continue this process in the same way until we reach the last catheter point from the list PL. At the end of this process the entire set of catheter describing points is obtained. If this search was made on an oblique plane the set of the points obtained is the catheter skeleton. We than obtain catheter describing points in an exactly same way as it was described for the non-loop technique catheter on-oblique-plane searching.


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EXPERIMENTAL VALIDATION OF THE SOFTWARE Our algorithms have been tested in routine clinical practice. The implants were selected to include a representative spectrum of anatomical sites as well as implant geometries. We have data for tumors of the brain, prostate, breast, cervix, chest, scapula, skin and neck, together with a phantom† constructed for the tests made for the catheters that make loops in volume. Different catheter types and materials are detailed in the Appendix. These experiments refer to 35 clinical implants and one phantom implant with three looped plastic catheters. Representative CT images and corresponding 3D views of the reconstructed catheters for four clinical implants and the test phantom implant are shown in Fig. 5 to 8. The accuracy analysis is subdivided into two parts: geometrical and source dwell position differences, Tables I and II. (I) The geometrical difference is defined as the geometrical shift between the manually and automatically reconstructed catheter describing points on each transaxial image. The geometrical difference is only relevant for in-volume searching. (II) The source dwell position difference is defined as the geometrical shift between the corresponding dwell positions generated by the manual and automatic catheter reconstruction procedure. An analysis was made for the dwell positions produced at each 2.5 mm starting from a given catheter tip. The catheter describing point based difference analysis gave mean geometrical difference in the range (0.3 ± 0.2) mm to (1.1 ± 0.3) mm with a grand mean for all 30 implants of (0.7 ± 0.3) mm. The source dwell position based difference analysis gave mean geometrical difference in the range (0.4 ± 0.2) mm to (1.3 ± 0.4) mm with a grand mean for all 30 implants of (0.8 ± 0.4) mm. A reconstruction time analysis was made using the same group of 30 in-volume and five on-slice clinical cases, Tables I and II. This analysis showed that this new algorithm is very time-efficient. In 27/30 in-volume cases no manual intervention by the user was required during the autoreconstruction based process. For these 27/30 cases the catheter reconstruction with the algorithm was on average 25.7 times faster than the manual reconstruction: 21.4 s compared to 547 s. In five on-slice clinical cases no manual intervention by the user was needed. For these five cases the catheter reconstruction based on our autoreconstruction algorithm was on average 21.8 times faster than the manual reconstruction: 25.9 s compared to 684 s. For the 3/30 in-volume cases where manual intervention was required, the catheter reconstruction based on our autoreconstruction algorithm was on average 9.1 times faster than the manual reconstruction: 81.7 s compared to 740 s. In the case of phantom implant with three looped plastic catheters no manual intervention by user was needed during the autoreconstruction based process. The reconstruction time with our algorithm was 25.4 times faster (mean value) than the corresponding manual reconstruction: 22 s compared to 558 s. In the two cases of brain tumors, with respectively 10 and 4 plastic catheters, manual intervention was required because one catheter in each case was lost within the region of the bony skeleton of the skull. This occurred because bone has a significantly higher HU characteristic. In the breast tumor case with 10 plastic catheters, manual intervention was required because significant patient movement occurred during CT slice acquisition.

†

Plastic plates where three plastic catheters were inserted using a loop technique


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CONCLUSIONS The new software proved to be extremely useful during brachytherapy treatment planning in clinical practice. It significantly accelerates the imaging based brachytherapy treatment planning procedure. Therefore the time needed for catheter reconstruction decreases only to the level that the user requires to define each catheter by a single point on a CT slice or an oblique plane and to select the input parameters using the GUI. The user has complete control of the entire autoreconstruction process and can at any moment make necessary changes or accept the reconstructed catheters. An index of success rate, defined as use only of the automatic algorithm without any use of manual intervention, was 35/32, which we regard as highly significant for clinical brachytherapy. The next stage in possible uses of this algorithm is to implement it with MR images as an alternative to CT imaging. MR will be particularly relevant to use of plastic applicators.

APPENDIX Hounsfield number properties of the catheters The HU profile of the catheters on CT slice depends on the HU properties of the neighboring tissues or materials, on the slice thickness and on the angle at which the catheter enters the CT slice. This is because the CT images are smoothed during the reconstruction process of the CT slice acquisition. We have analyzed the HU profiles of the flexible plastic catheters which have an outer diameter of 2.0 mm, wall thickness of 0.25 mm and effective wall density of 1.019 g/cm3. We have also analyzed profiles of brain implant flexible needles with an outer diameter of 2.0 mm, wall thickness of 0.3 mm and effective wall density of 1.42 g/cm3. Finally, we analyzed profiles for stainless steel trocar point needles with an outer diameter of 1.9 mm, wall thickness of 0.2 mm and wall density 8.02 g/cm3 . Our results for catheters placed in water are shown in Fig. 9. All have been obtained using a Somatom Plus 4 CT scanner‡. The HU profile observed for catheters depend on slice thickness, HU properties of the surrounding material and angle ϕ between the catheter central axis at the catheter entrance position to the CT slice and the vertical axis through the CT slice. When the catheter is not orthogonal to the CT slice (ϕ ≠ 0 ) the catheter area on the CT slice has an ellipsoid shape and its HU profile along the ellipse’s major axis is shown in Fig. 9 for ϕ=70o. The default HU values we use are in the following ranges, [-600, -200] HU for typical plastic catheter material and [2800, 3071] HU for typical metallic catheter material. We have defined default HU ranges which are most appropriate for the majority of clinical cases, but nevertheless these default values can be changed by the user. Included in our software is an option to enable the user to obtain a catheter-HU histogram along any defined line on a CT slice. For a flexible plastic catheter the HU range is more sensitive to the surrounding material than in the case of metallic catheter or a plastic flexible brain needle. In the graph in Fig. 9d we show a HU-Distance curve for a flexible plastic catheter. There is an angle of ϕ=70o between the catheter central axis and a line which is orthogonal ‡

Siemens, Medizinische Technik, Erlangen, Germany


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to the plane of the CT slice. When this angle is less than 70o, it is easier to determine from such a graph the location of the catheter. From the curves in Fig. 9d it can be seen that it is easier to determine the location of the catheter when the CT slice thickness is less than 3 mm. If slice thickness is greater of 3 mm and the angle ϕ is greater than some 50o, autoreconstruction would require manual intervention by the user. Without such manual intervention autoreconstruction of the flexible plastic catheter would not be possible for this case. We drew this conclusion for an evaluation of more than 100 CT slices which compared the flexible plastic catheter-HU range behavior as a function of each of the aforementioned parameters.


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REFERENCES 1. G. Bruggmoser, R. F. Mould, Eds., “Brachytherapy Review”, Proceedings German Brachytherapy Conference 1994, Freiburg, Germany: Albert-Ludwigs-University Freiburg FRG, 1994. 2. A. Kassaee and M. Altschuler, “Semiautomated matching and seed position location for implanted ribons”, Med. Phys. 21, 643-650, 1994. 3. V.R. Kini, G.K. Edmundson, F.A. Vicini, D.A. Jaffray, G. Gustafson, and A.A. Martinez, “Use of Three-Dimensional Radiation Therapy Planning Tools and Intraoperative Ultrasound to Evaluate High Dose Rate Prostate Brachytherapy Implants”, Int. J. Radiation Oncology Biolog. Phys. 43, 571-578, 1999. 4. C. Kolotas, G. Birn, D. Baltas, H.G. Fogt, T. Martin, and N. Zamboglou, “CT Guided Template Technique Interstitial Brachytherapy”, in New Developments in Interstitial Remote Controlled Brachytherapy, edited by N. Zamboglou (München, Bern, Wien, New York: Zuckschwerdt, 1997), 143-152. 5. C. Kolotas, G. Birn, D. Baltas, B. Rogge, P. Ulrich, and N. Zamboglou, “CT guided interstitial high dose rate brachytherapy for recurrent malignant gliomas”, The British J. of Radiology, to be published. 6. S. Li, G. T. Y. Chen, C. A. Pelizzari, C. Reft, j. C. Roeske and Y. Lu, “A new source localization algorithm with no requirement of one-to-one source correspondence between biplane radiographs, Med. Phys. 23, 921-927, 1996. 7. M.K. Martel, and V. Narayana, “Brachytherapy for the Next Century: Use of Image-Based Treatment Planning”, Radiation Research 150 (Suppl.), 178-188, 1998. 8. C.E. Metz and L. E. Fencil, “Determination of three-dimensional structure in biplane radiography without prior knowledge of the relationship between two views: Theory”, Med. Phys. 16, 445-51, 1989. 9. N. Milickovic, “Three Dimensional CT Based Reconstruction Techniques in Modern Brachytherapy Treatment Planning”, Ph.D. Thesis, Chapter 6, National Technical University of Athens, Athens, Greece, Dec. 1999. 10. R. F. Mould, J. J. Battermann, A. A. Martinez and B. L. Speiser, Eds., Brachytherapy from Radium to Optimization, Veenendaal, NL: Nucletron International B.V. 1994. 11. S. Nag, Eds., “High Dose Rate Brachytherapy, A Textbook”, Armonk, NY: Futura Publishing Company Inc., 1994. 12. National Electrical Manufacturers Association, Digital imaging and communications in medicine (DICOM), NEMA Standards Publication, PS 3.6 - 1993, NEMA: Washington, 1993. 13. R. L. Siddon and L. M. Chin, “Two-film brachytherapy reconstruction algorithm”, Med. Phys. 12, 77-83, 1985. 14. K. Tabushi, S. Itoh, M. Sakura, Y. Kutsutani-Nakamura, T. A. Iinuma, T. Arai and T. Irifune, “Two-radiograph reconstruction using six geometrical solution sets and leastsquares method”, Med. Phys. 19, 1307-1310, 1992. 15. A. Tsalpatouros, D. Baltas, C. Kolotas, R. van der Laarse, D. Koutsouris, N.K. Uzunoglu, N. Zamboglou, “CT-Based Software for 3-D Localization and Reconstruction in Stepping Source Brachytherapy”, IEEE Trans. Information Technology in Biomedicine 1, 229-242, 1997.


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16. F.A. Vicini, D.A. Jaffray, E.M. Horwitz, G.K. Edmundson, D.A. DeBiose, V.R. Kini, and A.A. Martinez, “Implementation of 3D-Virtual Brachytherapy in the Management of Breast Cancer: A Description of a New Method of Interstitial Brachytherapy”, Int. J. Radiation Oncology Biolog. Phys. 40, 629-635, 1998. 17. J. F. Williamson, B. R., B. R. Thomadsen, R. Nath, Eds., “Brachytherapy Physics, AAPM Summer School 1994”, Madison, Winsonsin: Medical Physics Publishing, 1994. 18. N. Zamboglou, “Interstitial Brachytherapy Possibilities”, in New Developments in Interstitial Remote Controlled Brachytherapy, edited by N. Zamboglou (München, Bern, Wien, New York: Zuckschwerdt, 1997) , 174-180. 19. N. Zamboglou, C. Kolotas, D. Baltas, T. Martin, B. Rogge, G. Strassman, A. Tsalpatouros, H.G. Fogt, “Clinical Evaluation of CT Based Software in Treatment Planning for Interstitial HDR Brachytherapy”, in Brachytherapy for the 21st Century, edited by B.L. Spencer and R.F. Mould (Nucletron B. V., 1998), 312-326.

Natasa Milickovic et al.: Catheter Autoreconstruction ...

In-Volume

Implant geometry

Reconstruction Time Analysis

No. of catheters

No. of images

Slice thickness (mm)

Pixel size (mm)

Catheter material

Manual recons. (MR) (min)

Autorecons. (AR) (s)

Prostate

4

36

3

0.68

Metallic

6.2

2

Prostate

4

16

2

0.74

Metallic

3

Prostate

4

46

3

0.76

4

Prostate

4

35

3

5

Prostate

4

70

6

Prostate

4

7

Prostate

8

Case no.

Site

1

Factor =

Positional accuracy analysis MGD ± 1 SD MDPD ± 1 SD 1

2

MR / AR

(mm)

(mm)

17

21.9

0.7 ± 0.3

0.7 ± 0.2

5.1

18

17.0

0.3 ± 0.2

0.4 ± 0.2

Metallic

6.2

18

20.7

0.7 ± 0.3

1.3 ± 0.4

0.67

Metallic

5.5

16

20.6

0.6 ± 0.3

0.8 ± 0.3

3

0.67

Metallic

6.7

14

28.7

0.7 ± 0.7

1.1 ± 0.6

37

3

0.70

Metallic

6.1

17

21.5

0.6 ± 0.3

1.0 ± 0.5

4

33

3

0.68

Metallic

7

19

22.1

0.8 ± 0.4

0.8 ± 0.4

Prostate

4

33

3

0.55

Metallic

14

25

33.6

0.6 ± 0.3

0.9 ± 0.6

9

Prostate

4

33

3

0.77

Metallic

7.5

22

20.4

0.8 ± 0.3

0.8 ± 0.3

10

Prostate

4

37

3

0.7

Metallic

9.2

25

22.1

0.6 ± 0.4

0.6 ± 0.4

11

Prostate

4

37

3

0.68

Metallic

4.5

21

12.9

0.8 ± 0.4

0.9 ± 0.3

12

Prostate

4

39

3

0.47

Metallic

5

23

13.0

0.3 ± 0.2

0.5 ± 0.4

13

Prostate

4

40

3

0.68

Metallic

7.5

21

21.4

0.7 ± 0.5

0.8 ± 0.7

14

Breast

10

46

3

0.51

Plastic

18

32

33.8

0.7 ± 0.5

0.8 ± 0.4

15

Breast

10

40

3

0.40

Plastic

16.5

31

31.9

0.4 ± 0.2

0.7 ± 0.4

16

Breast

4

63

3

0.91

Plastic

5.1

17

18.0

0.7 ± 0.4

0.9 ± 0.4


*

17

Breast

10

45

3

0.72

Plastic

22.7

115

11.8

0.6 ± 0.3

0.7 ± 0.5

18

Cervix

3

29

5

0.50

Metallic

8.2

12

41.0

0.6 ± 0.3

0.8 ± 0.2

19

Cervix

9

51

5

0.64

Metallic

23

28

49.3

0.8 ± 0.4

1.1 ± 0.4

20

Cervix

5

31

5

0.63

Metallic

9

26

20.8

1.1 ± 0.3

1.2 ± 0.4

21

Cervix

7

42

3

0.62

Metallic

16.5

20

49.5

1.0 ± 0.4

1.1 ± 0.4

22

Cervix

4

55

3

0.57

Metallic

7.5

20

22.5

0.8 ± 0.2

0.9 ± 0.5

23

Brain

4

49

3

0.53

Plastic

4.3

55

*

4.7

0.6 ± 0.2

0.6 ± 0.4

24

Brain

3

49

3

0.47

Plastic

3

23

7.8

0.4 ± 0.2

0.5 ± 0.1

25

Brain

4

30

3

0.50

Plastic

10.75

42

15.4

0.6 ± 0.2

0.6 ± 0.1

26

Brain

10

33

3

0.47

Plastic

10

75

8.0

0.9 ± 0.4

1.0 ± 0.3

27

Chest

2

51

3

0.76

Plastic

5.5

14

23.6

0.6 ± 0.6

0.8 ± 0.4

28

Scapula

3

57

3

0.62

Plastic

15.5

20

46.5

0.4 ± 0.2

0.6 ± 0.3

29

Skin

5

39

3

0.70

Plastic

9.1

16

34.1

0.7 ± 0.4

1.1 ± 0.4

30

Neck

5

45

2

0.5

Plastic

8

21

22.9

0.8 ± 0.4

1.0 ± 0.3

*

*manual correction was necessary after the autoreconstruction was finished mean geometrical difference 2 mean dwell point difference 1

TABLE I. Reconstruction time and positional accuracy analysis for 30 in-volume implants. The factor F illustrates the speed of the autoreconstruction method.


On-Plane

Reconstruction Time Analysis

Implant geometry

Factor =

MDPD ± 1 SD

MR / AR

(mm)

18

21.3

1.1 ± 0.4

7.2

23

18.8

1.0 ± 0.3

Plastic

17.4

36

29.0

0.9 ± 0.6

0.63

Plastic

16.5

33

30.0

1.2 ± 0.5

0.61

Plastic

9.5

22

9.7

1.1 ± 0.3

No. of catheter s

No. of images

Slice thickness (mm)

Pixel size (mm)

Catheter material

Manual recons. (MR) (min)

Autorecons. (AR) (s)

Prostate

4

37

3

0.66

Metallic

6.4

2

Prostate

4

36

3

0.72

Metallic

3

Brain

12

55

3

0.71

4

Brain

9

72

3

5

Brain

4

42

3

Case no.

Site

1

Positional accuracy analysis 1

1

mean dwell point difference

TABLE II. Reconstruction time and positional accuracy analysis for five on-plane implants. The factor F illustrates the speed of the autoreconstruction method.


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FIGURES

Figure 1.

P1 and P2 are the furthermost points of the catheter area on slice i. Points PC1 and PC2 both lie on the catheter area major axis [P1, P2] and their distances from the central point of the catheter area PC is d. Pairs of points and are possible positions of catheter points on the previous, i-1 CT slice, and also on the next, i-1 CT slice.


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four

Figure 2.

Example of an artifact showing four metallic catheters as a single catheter. This is caused by the significantly lower HU characteristics of the surrounding tissue.


Figure 3.

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Change of the search direction when the catheter makes a loop in the CT volume.


Figure 4.

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Extraction of catheter describing points from the set of catheter recognized points, commencing with the user given point P, when the catheter makes a loop on plane.


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Figure 5.

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Brain implant with 8 plastic flexible needles. (a) Representative CT image showing the user given catheter points. (b) 3D view of the autoreconstructed catheters and the PTV volume (red).


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Figure 6.

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Cervix implant with 7 metallic trocar point needles. (a) User given catheter points on the selected CT image. (b) 3D view of the autoreconstructed catheters and the PTV volume (red).


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Figure 7.

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Breast implant with 10 plastic flexible catheters. (a) Representative CT image showing the catheter areas. (b) 3D view of the auto-reconstructed catheters and the PTV volume (red).


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Figure 8.

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Test phantom simulating a looped catheter implant with three flexible plastic catheters. (a) Representative CT image showing the catheter areas. (b) 3D view of the autoreconstructed catheters.


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Figure 9.

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Representative CT slices and HU profiles, respectively, for the stainless steel trocar point (a, b), flexible plastic catheter (c, d) and plastic flexible brain needle (e, f) on the slices of 1 mm, 3 mm, 5 mm and 10 mm thicknesses, in water. The angle ϕ is defined as the angle between the catheter axis and the orthogonal on the CT plane. For ϕ =? 70° the profiles are calculated along the ellipse’s major axis of the catheter area.