Accuracy of inverse treatment planning on substitute CT images derived from MR data for brain lesions
© Jonsson et al.; licensee BioMed Central. 2015
Received: 13 December 2013
Accepted: 15 December 2014
Published: 10 January 2015
In this pilot study we evaluated the performance of a substitute CT (s-CT) image derived from MR data of the brain, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning.
S-CT images were created using a Gaussian mixture regression model on five patients previously treated with radiotherapy. Optimizations were compared using D max, D min, D median and D mean measures for the target volume and relevant risk structures. Final dose calculations were compared using gamma index with 1%/1 mm and 3%/3 mm acceptance criteria. 3D geometric evaluation was conducted using the DICE similarity coefficient for bony structures. 2D geometric comparison of digitally reconstructed radiographs (DRRs) was performed by manual delineation of relevant structures on the s-CT DRR that were transferred to the CT DRR and compared by visual inspection.
Differences for the target volumes in optimization comparisons were small in general, e.g. a mean difference in both D min and D max within ±0.3%. For the final dose calculation gamma evaluations, 100% of the voxels passed the 1%/1 mm criterion within the PTV. Within the entire external volume between 99.4% and 100% of the voxels passed the 3%/3 mm criterion. In the 3D geometric comparison, the DICE index varied between approximately 0.8-0.9, depending on the position in the skull. In the 2D DRR comparisons, no appreciable visual differences were found.
Even though the present work involves a limited number of patients, the results provide a strong indication that optimization and dose calculation based on s-CT data is accurate regarding both geometry and dosimetry.
KeywordsRadiotherapy Treatment planning MRI Substitute CT s-CT
For several common patient groups in the radiotherapy clinic, there is strong support within the scientific community for using magnetic resonance (MR) imaging as the primary imaging modality when defining the target volume. Examples of such patient groups are those with prostate , brain , head and neck  and cervical cancers . Treatment planning, including inverse planning optimization, dose calculations and generation of reference images for patient positioning are still dependent on the access to computed tomography (CT) images of the patient due to the electron density information content and excellent contrast between bone and soft tissue. However, a workflow in which the target volume is defined on MR images and the treatment is planned on CT data is dependent on the ability to align these images in the same coordinate system, i.e. image registration. The literature indicates that multi-modal image registration on actual patient data is associated with uncertainties of clinically relevant magnitude, affecting the geometric accuracy of the treatment systematically . Therefore, it has been suggested to use MR data exclusively for the planning of the treatment [6-9] to avoid such spatial uncertainties. In order to use MR data exclusively, some form of conversion of MR image intensities into values that resemble Hounsfield units (HU) is necessary, since dose calculations rely on the connection between HUs and electron densities. Several different methods have been proposed, and the reported dosimetric results of calculations based on MR are generally good [10-13]. The early work within the area was based on either manual delineation of relevant anatomical structures and assigning them bulk densities, which is highly time consuming [10,13,14], or using a single bulk density for the entire anatomy, i.e. that of water or mixture of muscle and adipose tissue. Such single density conversions compromises the dosimetric accuracy to a higher degree than multiple bulk density assignment and makes it impossible to generate adequate reference images for positioning . The introduction of MR imaging with ultra-short echo-times (UTE), enabled automatic separation of bone and air on a voxel-by-voxel level [15,16]. UTE images with different contrasts, combined with a statistical method associating the intensities in the MR images with HUs has been shown to provide a CT-like image without manual procedures . These CT-like images can be used as a substitute CT (s-CT) for MR only treatment planning , or as basis for attenuation correction of positron emission tomography (PET) data acquired with a combined PET/MR scanner . In the present pilot study we evaluate the performance of an s-CT image, derived from MR data using a specific method, as a basis for optimization of intensity modulated rotational therapy, final dose calculation and derivation of reference images for patient positioning.
Patient data and imaging
Derivation of s-CT
The s-CT was estimated using the method described by Johansson et al. , with the image data from the two dual-echo UTE scans and a T2-weighted 3D sequence (Siemens SPACE). The patients in the present study were not imaged in treatment position in the MR scanner. Therefore, an off-line image registration was performed to align the CT and s-CT in the same coordinate system using a rigid Mattes mutual information image algorithm from the Insight Toolkit (ITK). The result of each registration was verified by visual inspection.
The optimization objectives used in the study
Min dose > 58
Min dose > 57
Max dose < 63
Max dose < 5
Max dose < 5
Max dose < 5
Max dose < 5
Max dose < 50
Max dose < 60
Max dose < 40
Surrounding dose falloff: 60 to 30 Gy in 10 mm
For the evaluation of the optimization results, the D max, D min, D median and D mean doses to the PTV and OAR’s were compared between plans optimized on CT and s-CT image data. The evaluation of the dosimetric accuracy of the s-CT based calculations was performed by copying the plan optimized on the s-CT to the CT study, calculating the resulting dose and comparing dose matrices via gamma analysis  using acceptance criteria of 3%/3 mm and 1%/1 mm. Although measurement errors are absent, small geometrical deviations may be present due to registration inaccuracies or geometric distortions in the MR images.
The geometric evaluation of the s-CT was performed for the 2D case, i.e. s-CT based digitally reconstructed radiographs (DRRs), as well as in 3D. Patient positioning using 2D images and DRRs for intra-cranial treatments is often performed by manual alignment of bony structures visible on 2D x-ray projections. The verification of the geometric accuracy of s-CT based DRRs therefore focused on the geometric accuracy of the depiction of these structures. DRRs for the CT and s-CT were generated, and the structures commonly used for patient positioning were delineated on the s-CT based DRR. These delineations were then overlaid on the CT based DRR and the result was evaluated by visual inspection.
When using cone beam CT (CBCT) for positioning of the patient, automatic 3D registration is often the method of choice. Given the poor soft tissue contrast of CT and CBCT, this registration will be dominated by the high contrast bony structures. Therefore, to evaluate the geometric accuracy of the s-CT for 3D positioning, the bony structures were identified on both CT and s-CT data using an image intensity threshold of 400 HU and the dice similarity index was calculated, for individual slices as well as for the entire volume.
Dosimetric comparison - optimization
Dose comparison results
Diff. D min (%)
Diff. D max (%)
Diff. D median (%)
Diff. D mean (%)
1.6 ( 4.0)
Dosimetric comparison – dose calculation
Geometric comparison 2D
Geometric comparison 3D
This pilot study evaluated the feasibility of using CT equivalent data (s-CT) derived from MR images for optimization, final dose calculation and generation of reference images for patient positioning at treatment. Even though the patient material was very limited (only five patients), the results did not suggest that neither the quality of the optimizations nor the final dose calculation are compromised using the s-CT data as input for the treatment planning. We therefore conclude that treatment planning based on s-CT and CT may be equivalent for intracranial lesions, provided that the MR acquisition was successful. MR imaging is known to be sensitive to motion artifacts and there is some apprehension that the quality of the s-CT may be insufficient for a currently unknown fraction of the patients. A larger study is needed to investigate this and to serve as basis for the development of a patient specific quality control method for the s-CT.
The DRRs based on s-CT data are very similar to the golden standard – CT based DRRs. The important structures are geometrically correct and are easy to find in the s-CT based DRRs. In 3D, a DICE coefficient between 0.8 and 0.9 was found, with the higher value for the cranial half of the skull. This compares well with other published methods to segment the skull from MR data. Dogdas et al. presented a method to segment the skull based on a single high resolution MR scan, resulting in a DICE coefficient of on average 0.75 . Wagenknecht et al. evaluated an automatic segmentation method based on 3D MP-RAGE or other T1 weighted MR data and presented DICE coefficients between 0.7 and 0.8 in the cranial part of the skull with lower values caudally, using a Hounsfield threshold of 500 HU .
In conclusion, the results of our study provide experimental proof that it is feasible to use MR based s-CTs for optimization of the treatment plans in Oncentra. The differences between plans optimized on s-CT and CT are very small, well below what can be considered clinically important, both in terms of target coverage and avoidance of risk organs. Recalculations of s-CT based treatment plans using CT data revealed very small dose calculation errors. Even though the present work involves a limited number of patients, it provides a strong indication that optimization and dose calculation based on s-CT data is safe and may be used in clinical practice. Other experimental studies on this method with larger number of patients would be of major interest to improve our knowledge and serve as a basis for development of a quality assurance strategy.
This investigation was supported by grants from Lion’s Cancer Research Foundation, Umeå University.
- Debois M, Oyen R, Maes F, Verswijvel G, Gatti G, Bosmans H, et al. The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer. Int J Radiat Oncol Biol Phys. 1999;45:857–65.PubMedView ArticleGoogle Scholar
- Datta NR, David R, Gupta RK, Lal P. Implications of contrast-enhanced CT-based and MRI-based target volume delineations in radiotherapy treatment planning for brain tumors. J Cancer Res Ther. 2008;4:9–13.PubMedView ArticleGoogle Scholar
- Rasch C, Keus R, Pameijer FA, Koops W, De Ru V, Muller S, et al. The potential impact of CT-MRI matching on tumor volume delineation in advanced head and neck cancer. Int J Radiat Oncol Biol Phys. 1997;39:841–8.PubMedView ArticleGoogle Scholar
- Pötter R, Georg P, Dimopoulos JCA, Grimm M, Berger D, Nesvacil N, et al. Clinical outcome of protocol based image (MRI) guided adaptive brachytherapy combined with 3D conformal radiotherapy with or without chemotherapy in patients with locally advanced cervical cancer. Radiother Oncol. 2011;100:116–23.PubMed CentralPubMedView ArticleGoogle Scholar
- Nyholm T, Nyberg M, Karlsson MG, Karlsson M. Systematisation of spatial uncertainties for comparison between a MR and a CT-based radiotherapy workflow for prostate treatments. Radiat Oncol. 2009;4:54.PubMed CentralPubMedView ArticleGoogle Scholar
- Lee YK, Bollet M, Charles-Edwards G, Flower MA, Leach MO, McNair H, et al. Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging alone. Radiother Oncol. 2003;66:203–16.PubMedView ArticleGoogle Scholar
- Stanescu T, Hans-sonke J, Stavrev P, Fallone BG. 3T MR-based treatment planning for radiotherapy of brain lesions. Radiol Oncol. 2006;40:125–32.Google Scholar
- Prabhakar R, Julka PK, Ganesh T, Munshi A, Joshi RC, Rath GK. Feasibility of using MRI alone for 3D radiation treatment planning in brain tumors. Jpn J Clin Oncol. 2007;37:405–11.PubMedView ArticleGoogle Scholar
- Karlsson M, Karlsson MG, Nyholm T, Amies C, Zackrisson B. Dedicated magnetic resonance imaging in the radiotherapy clinic. Int J Radiat Oncol Biol Phys. 2009;74:644–51.PubMedView ArticleGoogle Scholar
- Chen L, Price RA, Wang L, Li J, Qin L, McNeeley S, et al. MRI-based treatment planning for radiotherapy: dosimetric verification for prostate IMRT. Int J Radiat Oncol Biol Phys. 2004;60:636–47.PubMedView ArticleGoogle Scholar
- Kapanen M, Tenhunen M. T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning. Acta Oncologica. 2013;52:612–8.PubMedView ArticleGoogle Scholar
- Dowling JA, Lambert J, Parker J, Salvado O, Fripp J, Capp A, et al. An atlas-based electron density mapping method for Magnetic Resonance Imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. Int J Radiat Oncol Biol Phys. 2012;83(1):e5–11.PubMedView ArticleGoogle Scholar
- Jonsson JH, Karlsson MG, Karlsson M, Nyholm T. Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions. Radiat Oncol. 2010;5:62.PubMed CentralPubMedView ArticleGoogle Scholar
- Lambert J, Greer PB, Menk F, Patterson J, Parker J, Dahl K, et al. MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning. Radiother Oncol. 2011;98:330–4.PubMedView ArticleGoogle Scholar
- Catana C, Van der Kouwe A, Benner T, Michel CJ, Hamm M, Fenchel M, et al. Toward implementing an MRI-Based PET attenuation-correction method for neurologic studies on the MR-PET brain prototype. J Nucl Med. 2010;51:1431–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Johansson A, Karlsson M, Nyholm T. CT substitute derived from MRI sequences with ultrashort echo time. Med Phys. 2011;38:2708.PubMedView ArticleGoogle Scholar
- Johansson A, Karlsson M, Yu J, Asklund T, Nyholm T. Voxel-wise uncertainty in CT substitute derived from MRI. Med Phys. 2012;39:3283–90.PubMedView ArticleGoogle Scholar
- Jonsson JH, Johansson A, Söderström K, Asklund T, Nyholm T. Treatment planning of intracranial targets on MRI derived substitute CT data. Radiother Oncol. 2013;108:118–22.PubMedView ArticleGoogle Scholar
- Larsson A, Johansson A, Axelsson J, Nyholm T, Asklund T, Riklund K, et al. Evaluation of an attenuation correction method for PET/MR imaging of the head based on substitute CT images. Magn Reson Mater Phy. 2013;26:127–36.View ArticleGoogle Scholar
- Janke A, Zhao H, Cowin GJ, Galloway GJ, Doddrell DM. Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images. Magn Reson Med. 2004;52:115–22.PubMedView ArticleGoogle Scholar
- Karger CP, Höss A, Bendl R, Canda V, Schad L. Accuracy of device-specific 2D and 3D image distortion correction algorithms for magnetic resonance imaging of the head provided by a manufacturer. Phys Med Biol. 2006;51:N253–61.PubMedView ArticleGoogle Scholar
- Dogdas B, Shattuck DW, Leahy RM. Segmentation of skull and scalp in 3-D human MRI using mathematical morphology. Hum Brain Mapp. 2005;26:273–85.PubMedView ArticleGoogle Scholar
- Wagenknecht G, Kops ER, Mantlik F, Fried E, Pilz T, Hautzel H, Tellmann L, Pichler BJ, Herzog H. Attenuation Correction in MR-BrainPET with Segmented Tl-weighted MR images of the Patient ’ s Head - A Comparative Study with CT. 2011:2261–2266.Google Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.