Objectives
Image-guided radiation therapy (IGRT) improves setup accuracy and reduces treatment margins, potentially improving efficacy while decreasing long-term toxicity of whole breast irradiation ...(WBI). This study quantifies intraparenchyma fiducial motion and utility during the course of WBI.
Methods
On a prospective IRB-approved protocol, textured gold fiducials were placed intraoperatively at the periphery of the surgical bed in 25 patients who then received 3D conformal conventional WBI. Free breathing and 4D CT image sets were obtained at pre-treatment simulation and at 6 weeks of treatment. Respiration-induced motion was assessed by comparing fiducials’ position between inspiration and expiration. Fiducial migration and setup variation were determined by comparing the relative positions of each marker from the pre- and post-treatment megavoltage and 4D CT image sets.
Results
Average intrafraction respiratory motion on 4D CT image sets was 0.1 mm. Variation in separation between fiducial pairs compared to simulation CT was on average 0.7 mm. The position of the seroma center was stable with respect to the center of mass (COM) of the fiducials throughout treatment. Based on daily megavoltage portal images, the average variation in the fiducial COM relative to the vertebral body landmark was 9.4 mm. The average variation of the seroma relative to fiducial COM from the 4D CT data was 6.2 mm.
Conclusions
Fiducial position was stable during treatment, and there was minimal respiration-induced motion. Using IGRT may improve the accuracy of daily setup and reduce PTV margins in WBI. Better tumor bed localization and reduced margin size will decrease the volume of normal tissue treated, which may translate into improved outcomes and lower toxicity.
Purpose The 4D-CT data used for comparing a patient's ventilation distributions before and after lung radiotherapy are acquired at different times. As a result, an additional variable - the tidal ...volume (TV) - can alter the results. Therefore, in this paper we propose to normalize the ventilation to the same TV to eliminate that uncertainty. Methods Absolute ventilation (AV) data were generated for 6 stereotactic body radiation therapy (SBRT) cases before and after treatment, using the direct geometric algorithm and diffeomorphic morphons deformable image registration (DIR). Each pair of AV distributions was converted to TV-normalized, percentile ventilation (PV) and low-dose well-ventilated-normalized ventilation (LDWV) distributions. The ventilation change was calculated in various dose regions based on the treatment plans using the DIR-registered before and after treatment data sets. The ventilation change based on TV-normalized ventilation was compared with the AV as well as the data normalized by PV and LDWV. Results AV change may be misleading when the TV differs before and after treatment, which was found to be up to 6.7%. All three normalization methods produced a similar trend in ventilation change: the higher the dose to a region of lung, the greater the degradation in ventilation. In low dose regions (<5 Gy), ventilation appears relatively improved after treatment due to the relative nature of the normalized ventilation. However, the LDWV may not be reliable when the ventilation in the low-dose regions varies. PV exhibited a similar ventilation change trend compared to the TV-normalized in all cases. However, by definition, the ventilation distribution in the PV is significantly different from the original distribution. Conclusion Normalizing ventilation distributions by the TV is a simple and reliable method for evaluation of ventilation changes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Ventilation imaging using 4D-CT is a convenient and cost effective functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. To ...calculate ventilation imaging from 4D-CT we must use deformable image registration (DIR). This study validates the DIR methods and investigates the dependence of calculated ventilation on DIR methods and ventilation algorithms.
The first hypothesis is if ventilation algorithms are robust then they will be insensitive to the precise DIR used provided the DIR is accurate. The second hypothesis is that the change in Houndsfield Unit (HU) method is less dependent on the DIR used and depends more on the CT image quality due to the inherent noise of HUs in normal CT imaging.
DIR of the normal end expiration and inspiration phases of the 4D-CT images was used to correlate the voxels between the two respiratory phases. All DIR algorithms were validated using a 4D pixel-based and point-validated breathing thorax model, consisting of a 4D-CT image data set along with associated landmarks. Three different DIR algorithms, Optical Flow (OF), Diffeomorphic Demons (DD) and Diffeomorphic Morphons (DM), were retrospectively applied to the same group of 10 esophagus and 10 lung cancer cases all of which had associated 4D-CT image sets that encompassed the entire lung volume. Three different ventilation calculation algorithms were compared (Jacobian, ΔV, and HU) using the Dice similarity coefficient comparison.
In the validation of the DIR algorithms, the average target registration errors with one standard deviation for the DIR algorithms were 1.6 ± 0.7 mm, maximum 3.1 mm for OF, 1.3 ± 0.6 mm, maximum 3.3 mm for DM, 1.3 ± 0.6 mm, maximum 2.8 mm for DD, indicating registration errors were within 2 voxels.
Dependence of ventilation images on the DIR was greater for the ΔV and the Jacobian methods than for the HU method. The Dice similarity coefficient for 20% of low ventilation volume for ΔV was 0.33 ± 0.03 between OF and DM, 0.44 ± 0.05 between OF and DD and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian was 0.32 ± 0.03, 0.44 ± 0.05 and 0.51 ± 0.04 respectively, and for HU 0.53 ± 0.03, 0.56 ± 0.03 and 0.76 ± 0.04 respectively.
Dependence of ventilation images on the ventilation method used showed good agreement between the ΔV and Jacobian methods but differences between these two and the HU method were significantly greater. Dice similarity coefficient for using OF as DIR was 0.86 ± 0.01 between ΔV and Jacobian, 0.28 ± 0.04 between ΔV and HU and 0.28 ± 0.04 between Jacobian and HU respectively. When using DM or DD as DIR, similar values were obtained when comparing the different ventilation calculation methods. The similarity values for 20% of the high ventilation volume were close to those found for the 20% low ventilation volume.
Mean target registration error for all three DIR methods was within one voxel suggesting that the registration done by either of the methods is quite accurate. Ventilation calculation from 4D-CT demonstrates some degree of dependency on the DIR algorithm employed. Similarities between ΔV and Jacobian are higher than between ΔV and HU and Jacobian and HU. This shows that ΔV and Jacobian are very similar, but HU is a very different ventilation calculation method.
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