Hybrid Deformable Image Registration Research Articles

Assessment of residual geometrical errors of clinical target volumes and their impact on dose accumulation for head and neck radiotherapy

PurposeTo assess the residual geometrical errors (dr) and their impact on the clinical target volumes (CTV) dose coverage for head and neck cancer (HNC) proton therapy patients. MethodsWe analysed 28 HNC patients treated with 70 Gy (RBE) and 54.25 Gy (RBE) to the therapeutic CTV70 and prophylactic CTV54.25, respectively. Daily cone beam CTs were converted to high quality synthetic CTs (sCTs). The CTVs from the nominal CT were propagated to the corresponding sCTs using a hybrid deformable image registration (propagated CTVs) in RayStation 11B. For 11 patients, all propagated CTVs were reviewed by our HNC radiation oncologist (physician corrected CTVs).The residual geometrical error dr was quantified as a function of the daily CTVs volume overlap with the nominal plan CTV. The errors dr(propagated CTVs) and dr(physician corrected CTVs) and the difference in dice similarity coefficients (ΔDSC) were determined. Using clinical plans, dose coverage and the tumor control probability (TCP) for the nominal, accumulated and voxel-wise minimum scenarios were determined. ResultsThe difference in the residual geometrical error dr (propagated CTVs – physician corrected CTVs) and mean DSC (|ΔDSC|mean) were minor: Δdr(CTV70) = 0.16 mm, Δdr(CTV54.25) = 0.26 mm, |ΔDSC|mean < 0.9%. For all 28 patients, dr(CTV70) = 1.91 mm and dr(CTV54.25) = 1.90 mm. However, CTV54.25 above and below the cricoid cartilage differed substantially (1.00 mm c.f. 3.93 mm). The CTV54.25 coverage below the cricoid was then almost always lower, although the TCP of the accumulated dose was higher than the TCP of the voxel-wise minimum dose. ConclusionsSetup uncertainty setting of 2 mm is possible. The feasibility of using propagated CTVs for error determination is demonstrated.

Applying Multi-Metric Deformable Image Registration for Dose Accumulation in Combined Cervical Cancer Radiotherapy

(1) Purpose: Challenges remain in dose accumulation for cervical cancer radiotherapy combined with external beam radiotherapy (EBRT) and brachytherapy (BT) as there are many large and complex organ deformations between different treatments. This study aims to improve deformable image registration (DIR) accuracy with the introduction of multi-metric objectives for dose accumulation of EBRT and BT. (2) Materials and methods: Twenty cervical cancer patients treated with EBRT (45-50 Gy/25 fractions) and high-dose-rate BT (≥20 Gy in 4 fractions) were included for DIR. The multi-metric DIR algorithm included an intensity-based metric, three contour-based metrics, and a penalty term. Nonrigid B-spine transformation was used to transform the planning CT images from EBRT to the first BT, with a six-level resolution registration strategy. To evaluate its performance, the multi-metric DIR was compared with a hybrid DIR provided by commercial software. The DIR accuracy was measured by the Dice similarity coefficient (DSC) and Hausdorff distance (HD) between deformed and reference organ contours. The accumulated maximum dose of 2 cc (D2cc) of the bladder and rectum was calculated and compared to simply addition of D2cc from EBRT and BT (ΔD2cc). (3) Results: The mean DSC of all organ contours for the multi-metric DIR were significantly higher than those for the hybrid DIR (p ≤ 0.011). In total, 70% of patients had DSC > 0.8 using the multi-metric DIR, while 15% of patients had DSC > 0.8 using the commercial hybrid DIR. The mean ΔD2cc of the bladder and rectum for the multi-metric DIR were 3.25 ± 2.29 and 3.54 ± 2.02 GyEQD2, respectively, whereas those for the hybrid DIR were 2.68 ± 2.56 and 2.32 ± 3.25 GyEQD2, respectively. The multi-metric DIR resulted in a much lower proportion of unrealistic D2cc than the hybrid DIR (2.5% vs. 17.5%). (4) Conclusions: Compared with the commercial hybrid DIR, the introduced multi-metric DIR significantly improved the registration accuracy and resulted in a more reasonable accumulated dose distribution.

An Estimate of Uncertainty in Deformable Image Registration-Based Accumulated Biological Equivalent Dose for Re-Irradiation

<h3>Purpose/Objective(s)</h3> We describe a dose volume histogram (DVH) overlay technique to estimate uncertainty in deformable image registration (DIR) based accumulated biologically equivalent dose (BED) for re-irradiation planning. We compare applicability of two DIR methods, contour-based (C-DIR) and image intensity and contour hybrid (H-DIR) DIR for BED accumulation. <h3>Materials/Methods</h3> Patient CT-based re-irradiation plans to the same anatomic regions as in their initial irradiations over a four-year period were analyzed. Among the organs-at-risk (OARs) for which BED was accumulated were stomach, heart, and esophagus. Physical dose distributions were converted to BED using OAR-specific parameters based on a linear-quadratic-linear model. For C-DIR, the registration was based only upon the contours drawn on both images. H-DIR is an image intensity-based DIR technique which includes contour-based registration to augment the DIR results. All prior BEDs were warped to the most recent CT image for accumulation. Data input to the DVH overlay workflow were the two CT images to be registered, the deformation vector field, the BED on the earlier CT, and the contours on the later CT. The later-CT contours were warped to the earlier CT, and the earlier-CT BED was warped to the later CT; BED DVHs from both images were then calculated and overlaid. For a fully invertible DIR, the two DVHs for the OAR should be identical; differences between the DVHs were interpreted as BED uncertainties due to inverse consistency error. For a given DVH point, the uncertainty was obtained from the differences between the overlaid DVHs. <h3>Results</h3> For an abdomen case, the stomach accumulated BED_max is 3% higher using C-DIR versus H-DIR; ΔBED_max is approximately 2% for both DIR methods. The higher isodoses were distal to the stomach for all plans; H-DIR, which accounts for regions distal to the OAR, is appropriate for re-irradiation BED calculations. For a thoracic case, both DIR methods yield similar results for the heart. The difference between H-DIR and C-DIR for accumulated heart BED_max is 2.4%; ΔBED_max is 0.9% and 0.6% for H-DIR and C-DIR, respectively. For the esophagus, accumulated BED_max is 54% higher using H-DIR instead of C-DIR, and ΔBED_max is much lower for C-DIR (0.2%) than for H-DIR (1.7%). Visual inspection showed that the BED distribution on the esophagus using H-DIR is unreasonable, attributed to H-DIR relying too heavily upon image intensity features. For accurate BED accumulation in the esophagus, C-DIR is essential during the re-irradiation planning for this case. <h3>Conclusion</h3> The DVH overlay technique can estimate the uncertainty in accumulated BED for re-irradiation scenarios. Caution should be exercised in using various DIR methods before applying DIR dose accumulation for re-irradiation planning. Contour-based DIR may be the preferred registration method for re-irradiation dose accumulation.

Comparison of an in‐house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer

An in‐house hybrid deformable image registration (DIR) method, which combines free‐form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone‐beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B‐spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross‐correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in‐house hybrid method gave better accuracy and more stable performance than NiftyReg.

Automatic contouring using deformable image registration for tandem-ring or tandem-ovoid brachytherapy.

PurposeTo investigate the effectiveness of deformable image registration (DIR)-based automatic contouring for tandem-ring (T-R) or tandem-ovoid (T-O) 3-dimensional computed tomography (CT)-based image-guided brachytherapy (IGBT).Material and methodsCT images of 28 patients with intact cervical cancer were retrospectively analyzed. Selected group had T-R or T-O insertion for IGBT. Hybrid DIR was performed between first fraction CT and subsequent CTs for IGBT. First IGBT CT images were reference images. All DIRs were performed based on these first IGBT CT scans. Contour similarities between manual and automated segmentations were evaluated with dice similarity coefficient (DSC) score. Mean volumes of the structures were delineated manually and automatically compared. Finally, dosimetric comparisons were performed in order to obtain how contour differences affect the doses to target and organs at risk (OARs).ResultsIn general, mean volumes of the automatic contours were larger than manual contours for both T-R and T-O insertions. However, the difference in volume was statistically significant for the small bowel only (p < 0.05 and p < 0.01 for T-R and T-O, respectively). The DSC scores were small for the small bowel and the sigmoid in both applicator sets. When the two different applicator sets were compared, the performance of DIR-based contour propagation for the rectum was worse in T-O compared to T-R application. Dosimetric comparisons showed that volume differences between the manual and propagated contours did not affect dose-volume parameters. The treatment plans based on manually contoured targets also well-covered DIR contours. The average time for DIR was 2.0 ±0.1 minutes per fraction compared to 14.0 ±0.4 minutes in manual contouring (p < 0.001).ConclusionsDIR-based automatic contouring of the structures seems successful for both the T-R and T-O applications in cervical IGBT. DIR significantly decreased the time for contouring. Our results indicate that automatic contouring in IGBT is safe and time-saving.

Automatic Contouring Using Deformable Image Registration for Tandem-Ring or Tandem-Ovoid Brachytherapy

To investigate the effectiveness of deformable image registration (DIR)-based automatic contouring for tandem-ring (T-R) or tandem-ovoid (T-O) 3-dimensional computed tomography (CT)-based image-guided brachytherapy (IGBT).CT images of 28 patients with intact cervical cancer were retrospectively analyzed. The selected group had T-R or T-O insertion for IGBT. The hybrid DIR was performed between the first fraction CT and the subsequent CTs of the IGBT. The first IGBT CT images were the reference images. All DIRs were performed based on these 1st IGBT CT scans. The contour similarities between the manual and automated segmentations were evaluated with Dice Similarity Coefficient (DSC) score. Mean volumes of the structures delineated manually and automatically were compared. Finally, dosimetric comparisons were performed in order to obtain how contour differences affect the target and organs at risk (OARs) doses.In general, mean volumes of the automatic contours were larger than manual contours for both T-R and T-O insertions. However, the difference in the volume was statistically significant for the small bowel only (P < 0.05 and P < 0.01 for T-R and T-O, respectively). The DSC scores were small for small bowel and sigmoid in both applicator sets. When the two different applicator sets were compared, the performance of DIR-based contour propagation for the rectum was worse in the T-O than the T-R application. Dosimetric comparisons showed that the volume differences between the manual and propagated contours did not affect the dose distributions. The plans based on manually contoured targets also covered the DIR contours well. The average time for DIR was 2 ± 0.1 minutes per fraction compared to 14 ± 0.in manual contouring (P < 0.001).DIR-based automatic contouring of the structures seems successful for both the T-R and T-O applications in cervical IGBT. DIR significantly decreased the time for contouring. Our results indicate that automatic contouring in IGBT is safe and time-saving.

Consistency Check of Deformable Image Registration-Based Dose Summation for Off-Line Adaptive Re-Planning

<h3>Purpose/Objective(s)</h3> Uncertainty of deformable image registration (DIR) based dose accumulation is a concern due to the uncertainty with the DIR, in particular for a large deformation. In this work, we introduce a practical workflow of using a DVH overlay technique to check consistency in DIR-based dose accumulation during off-line adaptive replanning for large deformations observed during radiation therapy for abdominal tumors. <h3>Materials/Methods</h3> Required inputs to the DVH overlay workflow are deformably registered primary (original plan) and secondary (re-plan) images, primary regions-of-interest (ROIs), and a secondary dose distribution. Primary ROIs are forward warped to the secondary image, secondary dose is inversely warped to the primary image, and the ROI DVHs for each image are overlaid. Differences between the DVHs at a given dose point (e.g., V_45Gy) quantify the dose uncertainty due to inverse consistency error (ICE) inherent in the DIR. We applied the workflow to a pancreas case treated using modulated arc therapy (mARC) on a Linac equipped with integrated kVCT-on-rails IGRT. For the original plan, 65.25-Gy and 50.75-Gy PTVs were concurrently treated over 29 fractions. After fraction 21, the patient was observed to have lost sufficient weight to justify an adaptive mARC plan based on an updated kVCT image. A composite dose distribution was compiled on the original plan CT by summing 21 fractions of the original planned dose and 8 fractions of a dose distribution warped from the re-plan CT to the original plan CT. Warping was performed using a hybrid DIR algorithm combining contour-based and image-based registration. For each organ-at-risk (OAR) DVH dose point, the workflow-derived ICE (scaled to 8 fractions) was additively applied as an uncertainty to the summation DVH. <h3>Results</h3> For the re-planned pancreas case, ICE based on the DVH overlay workflow was greatest for the small bowel V_45Gy (approximately 5%) and for the stomach V_45Gy and V_56Gy (approximately 2% for both). ICE was also approximately 4% at V_15Gy for the duodenum and colon. For all other ROIs, ICE was 1% or less from the DVH overlay, indicating a favorable DIR for those regions. When applying the DVH overlay-based ICE the resulting accumulated dose remained compliant with the original plan objectives for all OARs except for the stomach V_56Gy, which equaled 7.9 cm³ with ICE included (plan objective < 5 cm³). <h3>Conclusion</h3> A DVH overlay technique was used to estimate ICE attributable to the uncertainty in DIR-based dose accumulation. The method was applied to a pancreas case for which dose accumulation was performed due to off-line adaptive re-planning for a large anatomic change during the treatment course. In instances where the ICE is over a pre-defined threshold or the summation dose exceeds plan objectives if ICE is included, it is prudent to review the DIR dose transfer and accumulation before using it to make clinical decisions.

Determination of Deformable Image Registration Algorithms for Accumulating Dose in Carbon-ion Radiotherapy for Pancreatic Cancer.

This study aimed to determine appropriate deformable image registration (DIR) algorithms for pancreatic cancer patients undergoing carbon ion radiotherapy (CIRT). The performance of three types of DIR algorithms, including intensity-based DIR (iDIR), contour-based DIR (cDIR), and hybrid DIR (hDIR) were evaluated using seventy-one CT images from eight pancreatic cancer patients. Both the geometry of the CTV and subsequent dose warping discrepancies were evaluated using the dice similarity coefficient (DSC) and the difference in V95. cDIR and hDIR had superior performance than iDIR in both DSC and V95 (p<0.0001). iDIR caused accumulated dose to be underestimated by 5% in the median CTV V95 compared to the other methods. hDIR and cDIR can be utilized to assess the accumulated dose in CIRT for pancreatic cancer. iDIR can be considered when the obtained DSC is greater than 0.89.

Enhanced super-resolution reconstruction of T1w time-resolved 4DMRI in low-contrast tissue using 2-step hybrid deformable image registration.

PurposeDeformable image registration (DIR) in low‐contrast tissues is often suboptimal because of low visibility of landmarks, low driving‐force to deform, and low penalty for misalignment. We aim to overcome the shortcomings for improved reconstruction of time‐resolved four‐dimensional magnetic resonance imaging (TR‐4DMRI).Methods and MaterialsSuper‐resolution TR‐4DMRI reconstruction utilizes DIR to combine high‐resolution (highR:2x2x2mm3) breath‐hold (BH) and low‐resolution (lowR:5x5x5mm3) free‐breathing (FB) 3D cine (2Hz) images to achieve clinically acceptable spatiotemporal resolution. A 2‐step hybrid DIR approach was developed to segment low‐dynamic‐range (LDR) regions: low‐intensity lungs and high‐intensity “bodyshell” (=body‐lungs) for DIR refinement after conventional DIR. The intensity in LDR regions was renormalized to the full dynamic range (FDR) to enhance local tissue contrast. A T1‐mapped 4D XCAT digital phantom was created, and seven volunteers and five lung cancer patients were scanned with two BH and one 3D cine series per subject to compare the 1‐step conventional and 2‐step hybrid DIR using: (a) the ground truth in the phantom, (b) highR‐BH references, which were used to simulate 3D cine images by down‐sampling and Rayleigh‐noise‐adding, and (c) cross‐verification between two TR‐4DMRI images reconstructed from two BHs. To assess DIR improvement, 8‐17 blood vessel bifurcations were used in volunteers, and lung tumor position, size, and shape were used in phantom and patients, together with the voxel intensity correlation (VIC), structural similarity (SSIM), and cross‐consistency check (CCC).ResultsThe 2‐step hybrid DIR improves contrast and DIR accuracy. In volunteers, it improves low‐contrast alignment from 6.5 ± 1.8 mm to 3.3 ± 1.0 mm. In phantom, it improves tumor center of mass alignment (COM = 1.3 ± 0.2 mm) and minimizes DIR directional difference. In patients, it produces almost‐identical tumor COM, size, and shape (dice> 0.85) as the reference. The VIC and SSIM are significantly increased and the number of CCC outliers are reduced by half.ConclusionThe 2‐step hybrid DIR improves low‐contrast‐tissue alignment and increases lung tumor fidelity. It is recommended to adopt the 2‐step hybrid DIR for TR‐4DMRI reconstruction.

Retrospective dose reconstruction of prostate stereotactic body radiotherapy using cone-beam CT and a log file during VMAT delivery with flattening-filter-free mode.

This study aimed to reconstruct the dose distribution of single fraction of stereotactic body radiotherapy for patients with prostate cancer using cone-beam computed tomography (CBCT) and a log file during volumetric-modulated arc therapy (VMAT) delivery with flattening-filter-free (FFF) mode. Twenty patients with clinically localized prostate cancer were treated with FFF-VMAT, and projection images for in-treatment CBCT (iCBCT) imaging were concomitantly acquired with a log file. A D95 dose of 36.25Gy in five fractions was prescribed to each planning target volume (PTV) on each treatment planning CT (pCT). Deformed pCT (dCT) was obtained from the iCBCT using a hybrid deformable image registration algorithm. Dose distributions on the dCT were calculated using Pinnacle3 v9.10 by converting the log file data to Pinnacle3 data format using an in-house software. Dose warping was performed by referring to deformation vector fields calculated from pCT and dCT. Reconstructed dose distribution was compared with that of the original plan. Dose differences between the original and reconstructed dose distributions were within 3% at the isocenter and observed in PTV and organ-at-risk (OAR) regions. Differences in OAR regions were relatively larger than those in the PTV, presumably because OARs were more deformed than the PTV. Therefore, our method can be used successfully to reconstruct the dose distributions of one fraction using iCBCT and a log file during FFF-VMAT delivery.

Usefulness of hybrid deformable image registration algorithms in prostate radiation therapy.

To evaluate the accuracy of commercially available hybrid deformable image registration (DIR) algorithms when using planning CT (pCT) and daily cone‐beam computed tomography (CBCT) in radiation therapy for prostate cancer. The hybrid DIR algorithms in RayStation and MIM Maestro were evaluated. Contours of the prostate, bladder, rectum, and seminal vesicles (SVs) were used as region‐of‐interest (ROIs) to guide image deformation in the hybrid DIR and to compare the DIR accuracy. To evaluate robustness of the hybrid DIR for prostate cancer patients with organs with volume that vary on a daily basis, such as the bladder and rectum, the DIR algorithms were performed on ten pairs of CT volumes from ten patients who underwent prostate intensity‐modulated radiation therapy or volumetric modulated arc therapy. In a visual evaluation, MIM caused unrealistic image deformation in soft tissues, organs, and pelvic bones. The mean dice similarity coefficient (DSC) ranged from 0.46 to 0.90 for the prostate, bladder, rectum, and SVs; the SVs had the lowest DSC. Target registration error (TRE) at the centroid of the ROIs was about 2 mm for the prostate and bladder, and about 6 mm for the rectum and SVs. RayStation did not cause unrealistic image deformation, and could maintain the shape of pelvic bones in most cases. The mean DSC and TRE at the centroid of the ROIs were about 0.9 and within 5 mm generally. In both software programs, the use of ROIs to guide image deformation had the possibility to reduce any unrealistic image deformation and might be effective to keep the DIR physically reasonable. The pCT/CBCT DIR for the prostate cancer did not reduce the DIR accuracy because of the use of ROIs to guide the image deformation.

Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: A multi-institutional study.

To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms. Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances. DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against intercenter variability. The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centers. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centers using the same hybrid registration algorithm.

Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques.

ABSTRACTWe evaluated dose–volume histogram (DVH) parameters based on deformable image registration (DIR) between brachytherapy (BT) and external beam radiotherapy (EBRT) that included a center-shielded (CS) plan. Eleven cervical cancer patients were treated with BT, and their pelvic and CS EBRT were studied. Planning CT images for EBRT and BT (except for the first BT, used as the reference image) were deformed with DIR to reference image. We used two DIR parameter settings: intensity-based and hybrid. Mean Dice similarity coefficients (DSCs) comparing EBRT with the reference for the uterus, rectum and bladder were 0.81, 0.77 and 0.83, respectively, for hybrid DIR and 0.47, 0.37 and 0.42, respectively, for intensity-based DIR (P < 0.05). D1 cm3 for hybrid DIR, intensity-based DIR and DVH addition were 75.1, 81.2 and 78.2 Gy, respectively, for the rectum, whereas they were 93.5, 92.3 and 94.3 Gy, respectively, for the bladder. D2 cm3 for hybrid DIR, intensity-based DIR and DVH addition were 70.1, 74.0 and 71.4 Gy, respectively, for the rectum, whereas they were 85.4, 82.8 and 85.4 Gy, respectively, for the bladder. Overall, hybrid DIR obtained higher DSCs than intensity-based DIR, and there were moderate differences in DVH parameters between the two DIR methods, although the results varied among patients. DIR is only experimental, and extra care should be taken when comparing DIR-based dose values with dose–effect curves established using DVH addition. Also, a true evaluation of DIR-based dose accumulation would require ground truth data (e.g. measurement with physical phantom).

EP-1706: Validation of a novel hybrid deformable image registration algorithm for cervix cancer

EP-1706: Validation of a novel hybrid deformable image registration algorithm for cervix cancer

SU‐F‐J‐88: Comparison of Two Deformable Image Registration Algorithms for CT‐To‐CT Contour Propagation

Purpose:To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling.Methods:Both DIR algorithms were used for CT‐to‐CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0&lt;RQS&lt;1) assigned by dosimetrists based on the extent of manual refinement needed for acceptable quality.Results:Both algorithms showed similar ICE (&lt; 1.5 mm), but the hybrid DIR (TRE = 3.2 mm) performed better than the biomechanical DIR (TRE = 4.3 mm) with landmark tracking. Both algorithms had comparable DSC (DSC &gt; 0.9 for lungs, &gt; 0.85 for heart, &gt; 0.8 for liver) and similar qualitative assessments (VQS &lt; 0.35, RQS &gt; 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR.Conclusion:The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.

SU‐F‐J‐58: Evaluation of RayStation Hybrid Deformable Image Registration for Accurate Contour Propagation in Adaptive Planning

Purpose:Adaptive radiotherapy requires complete new sets of regions of interests (ROIs) delineation on the mid‐treatment CT images. This work aims at evaluating the accuracy of the RayStation hybrid deformable image registration (DIR) algorithm for its overall integrity and accuracy in contour propagation for adaptive planning.Methods:The hybrid DIR is based on the combination of intensity‐based algorithm and anatomical information provided by contours. Patients who received mid‐treatment CT scans were identified for the study, including six lung patients (two mid‐treatment CTs) and six head‐and‐neck (HN) patients (one mid‐treatment CT). DIRpropagated ROIs were compared with physician‐drawn ROIs for 8 ITVs and 7 critical organs (lungs, heart, esophagus, and etc.) for the lung patients, as well as 14 GTVs and 20 critical organs (mandible, eyes, parotids, and etc.) for the HN patients. Volume difference, center of mass (COM) difference, and Dice index were used for evaluation. Clinical‐relevance of each propagated ROI was scored by two physicians, and correlated with the Dice index.Results:For critical organs, good agreement (Dice&gt;0.9) were seen on all 7 for lung patients and 13 out of 20 for HN patients, with the rest requiring minimal edits. For targets, COM differences were within 5 mm on average for all patients. For Lung, 5 out of 8 ITVs required minimal edits (Dice 0.8–0.9), with the rest 2 needed re‐drawn due to their small volumes (&lt;10 cc). However, the propagated HN GTVs resulted in relatively low Dice values (0.5–0.8) due to their small volumes (3–40 cc) and high variability, among which 2 required re‐drawn due to new nodal target identified on the mid‐treatment CT scans.Conclusion:The hybrid DIR algorithm was found to be clinically useful and efficient for lung and HN patients, especially for propagated critical organ ROIs. It has potential to significantly improve the workflow in adaptive planning.

A hybrid biomechanical intensity based deformable image registration of lung 4DCT

Deformable image registration (DIR) has been extensively studied over the past two decades due to its essential role in many image-guided interventions (IGI). IGI demands a highly accurate registration that maintains its accuracy across the entire region of interest. This work evaluates the improvement in accuracy and consistency by refining the results of Morfeus, a biomechanical model-based DIR algorithm.A hybrid DIR algorithm is proposed based on, a biomechanical model–based DIR algorithm and a refinement step based on a B-spline intensity-based algorithm. Inhale and exhale reconstructions of four-dimensional computed tomography (4DCT) lung images from 31 patients were initially registered using the biomechanical DIR by modeling contact surface between the lungs and the chest cavity. The resulting deformations were then refined using the intensity-based algorithm to reduce any residual uncertainties. Important parameters in the intensity-based algorithm, including grid spacing, number of pyramids, and regularization coefficient, were optimized on 10 randomly-chosen patients (out of 31). Target registration error (TRE) was calculated by measuring the Euclidean distance of common anatomical points on both images after registration. For each patient a minimum of 30 points/lung were used.Grid spacing of 8 mm, 5 levels of grid pyramids, and regularization coefficient of 3.0 were found to provide optimal results on 10 randomly chosen patients. Overall the entire patient population (n = 31), the hybrid method resulted in mean ± SD (90th%) TRE of 1.5 ± 1.4 (2.9) mm compared to 3.1 ± 1.9 (5.6) using biomechanical DIR and 2.6 ± 2.5 (6.1) using intensity-based DIR alone.The proposed hybrid biomechanical modeling intensity based algorithm is a promising DIR technique which could be used in various IGI procedures. The current investigation shows the efficacy of this approach for the registration of 4DCT images of the lungs with average accuracy of 1.5 mm.

Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy.

WE-C-116-04: Development of Automatic Segmentation Algorithm to Assess Parotid-Gland Volume Changes Following Radiotherapy for Head-And-Neck Malignancies: A Longitudinal Study

Purpose: Xerostomia (dry mouth), secondary to radiation‐induced parotid‐gland injury, is a distressing side effect affecting up to 90% of patients following head‐and‐neck cancer radiotherapy (RT). Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. This study's purpose is to develop an automated MR parotid‐gland segmentation method to monitor parotid‐gland volume change induced by radiation. Methods: We conducted a longitudinal study of 15 patients (54 MRIs). All patients received 1 MR scan prior to RT, and additional 1–4 MR scans post RT (follow‐up: 6–24 months). The proposed method combines atlas registration which captures global variation of anatomy with pattern recognition which captures local statistical features to segment parotid glands from head‐and‐neck MRI. Specifically, the segmentation method includes 3 steps. First, we built an atlas (pre‐RT MRI and corresponding parotid‐gland mask contoured by physician). We utilized a hybrid deformable image registration to map the pre‐RT MRI to the post‐RT MRI, and applied the transformation to the parotid glands segmented from the pre‐RT MRI. Second, the kernel support vector machine (SVM) was trained with pre‐RT MRI, multi‐features (Gradient, Sobel, and Gabor features), as well as the transformed parotid mask. Lastly, the trained KSVM was used to segment the post‐RT parotid glands. The resulting segmentations were compared with physicians' manual contours. Results: The average left and right parotid‐gland volume overlapped 91.1±1.6% and 90.5±2.4% between the automatic segmentations and the physicians' contours. Overall, the parotid‐gland volume reduction was observed in 12 patients, and the volume reduction was 12.6–17.8% for 5 patients at 1‐year follow‐up. Conclusion: We have demonstrated the feasibility and accuracy of our automatic segmentation algorithm for monitoring radiation‐induced parotid‐gland volume changes. This segmentation method may be useful as we try to address xerostomia in patients following radiotherapy for head‐and‐neck malignancies.

Multi-atlas-based Segmentation of the Parotid Glands of MR Images in Patients Following Head-and-neck Cancer Radiotherapy.

Xerostomia (dry mouth), resulting from radiation damage to the parotid glands, is one of the most common and distressing side effects of head-and-neck cancer radiotherapy. Recent MRI studies have demonstrated that the volume reduction of parotid glands is an important indicator for radiation damage and xerostomia. In the clinic, parotid-volume evaluation is exclusively based on physicians' manual contours. However, manual contouring is time-consuming and prone to inter-observer and intra-observer variability. Here, we report a fully automated multi-atlas-based registration method for parotid-gland delineation in 3D head-and-neck MR images. The multi-atlas segmentation utilizes a hybrid deformable image registration to map the target subject to multiple patients' images, applies the transformation to the corresponding segmented parotid glands, and subsequently uses the multiple patient-specific pairs (head-and-neck MR image and transformed parotid-gland mask) to train support vector machine (SVM) to reach consensus to segment the parotid gland of the target subject. This segmentation algorithm was tested with head-and-neck MRIs of 5 patients following radiotherapy for the nasopharyngeal cancer. The average parotid-gland volume overlapped 85% between the automatic segmentations and the physicians' manual contours. In conclusion, we have demonstrated the feasibility of an automatic multi-atlas based segmentation algorithm to segment parotid glands in head-and-neck MR images.