Correcting CT misregistration in data-driven gated (DDG) PET with PET self-gating and deformable image registration.

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 4DCT we must use deformable image registration (DIR). This study investigates the dependence of calculated ventilation on DIR methods. 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. Three different DIR algorithms, Optical Flow (OF), Diffeomorphic Demons (DD) and Diffeomorphic Morphons (DM), were retrospectively applied to 10 esophagus and 10 lung cancer cases with 4D-CT image sets that encompassed the entire lung volume. The three ventilation extraction methods were used based on either the Jacobian, the change in volume of the voxel derived from the volume change of a voxel (ΔV) or directly calculated from the Hounsfield Unit (HU). The images were compared using the Dice similarity coefficient comparison. Dependence of ventilation images on the DIR was greater for the Δ V and the Jacobian methods than for the HU method. The Dice similarity coefficient for 20% of low ventilation volume for Δ V 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. Ventilation calculation from 4D-CT demonstrates some degree of dependency on the DIR algorithm employed.KeywordsLung FunctionDeformable Image RegistrationHigh ResolutionVentilation Imaging

Purpose: Three methods of calculating ventilation from 4D CT image sets have been explored by several research groups. This study is to investigate the differences of these three local ventilation calculations. Methods: Optical flow (OF) deformable image registration of the normal end expiration and inspiration phases of 4D‐CT images was used to correlate the voxels between the two phases. The OF was validated using a 4D pixel‐ based and point‐validated breathing thorax model, consisting of a 4D‐CT image data set along with associated landmarks. Ventilation derived from 4D‐CTs from 20 esophageal patients were retrospectively analyzed. Differences between the ventilation images generated by three methods, the Jacobian, the DeltaV, and the HU, were examined on a voxel‐to‐voxel basis. The Jacobian method uses the first derivative of the deformation field to approximate the change in volume of voxels. The DeltaV method directly calculates the volume change. The HU method uses the change in Houndsfield Units (HUs) of corresponding voxels to calculate ventilation. Results: The target registration error (TRE) for the deformable image registration was an average of 1.6±0.68 mm and maximum of 3.1 mm. Average difference between the DeltaV and the Jacobian ventilation as a percentage of the maximum ventilation value was 0.51±0.3% (range 0.33% to 1.32%). Average difference between the DeltaV and HU ventilation was 2.4±4.5 % (range 0.4% to 19.2 %). A small number of voxels show significant differences. We speculate that the larger differences were due to some image registration variances. Regions of highest and lowest ventilation matched well for all methods. Conclusions: Highs and lows in ventilation were more pronounced in the DeltaV method compared to the Jacobian. In general the differences between the two ventilation methods were small. However, the differences between the DeltaV and the HU methods were considerably larger.

The aim of this study is to evaluate anatomic lung tumor changes and dosimetric consequences utilizing the deformable daily kilovolt (KV) cone-beam computer tomography (CBCT) image registration. Five patients diagnosed with NSCLC were treated with three-dimensional conformal radiotherapy (3D CRT) and 10 daily KV CBCT image sets were acquired for each patient. Each CBCT image and plan CT were imported into the deformable image registration (DIR) system. The plan CT image was deformed by the DIR system and a new contour on CBCT was obtained by using the auto-contouring function of the DIR. These contours were individually marked as CBCT f1, CBCT f2,..., and CBCT f10, and imported into a treatment planning system (TPS). The daily CBCT plan was individually generated with the same planning criteria based on new contours. These plans were individually marked as CBCTp1, CBCTp2,..., and CBCTp10, followed by generating a dose accumulation plan (DA plan) in original pCT image contour sets by adding all CBCT plans using Varian Eclipse TPS. The maximum, minimum and mean doses to the plan target volume (PTV) in the 5 DA plans were the same with the CT plans. However, the volume of radiation 5, 10, 20, 30, and 50 Gy of the total lungs in DA plans were less than those of the CT plans. The maximum dose of the spinal cord in the DA plans were average 27.96% less than the CT plans. The mean dose for the left, right, and total lungs in the DA plans were reduced by 13.80%, 23.65%, and 12.96%, respectively. The adaptive 3D CRT based on the deformable registration can reduce the dose to the lung and the spinal cord with the same PTV dose coverage. Moreover, it provides a method for further adaptive radiotherapy exploration.

Deformable image registration (DIR) is widely utilized for dose accumulation, but errors in image registration can compromise its accuracy. This study evaluated the precision of DIR in dose accumulation for parotid gland and proposed a method to correct errors induced by DIR. This retrospective study included 123 patients with head and neck cancer. For each patient, the accumulated mean dose (Dmean) to the parotid gland was obtained by manual segmentation (ground truth) and two DIR strategies: contour propagation and dose mapping. The normal tissue complication probability (NTCP) model predicting xerostomia was employed to translate accumulated Dmean into NTCP values. Comparisons were made between ground truth and DIR for accumulated Dmean and NTCP. The maximum discrepancy of accumulated Dmean and NTCP between DIR and ground truth was 4.87Gy and 3.94%, respectively. The discrepancies of accumulated Dmean between DIR and ground truth were significantly correlated with the discrepancies between accumulated Dmean of ground truth and nominal Dmean. Mid-treatment weekly Dmean discrepancies between contour propagation and manual segmentation showed the capability to correct the accumulated Dmean of DIR and decrease the error of NTCP prediction from 2.89% and 3.81% to 1.26% and 2.04% for baseline xerostomia Grade 0 and Grade 1-2, respectively. Significant discrepancies were observed in accumulated Dmean of parotid glands between DIR and manual segmentation in candidates for adaptive radiotherapy. Utilizing mid-treatment CT scans offers a practical solution to correct DIR-induced errors, improving the accuracy of dose accumulation.

Deformable image registration (DIR) is a crucial step in adaptive radiation therapy (ART) to deform the planning CT to the current CBCT for dose calculation and for contour propagation. CBCT images are sometimes truncated in the axial plane due to limited field of view (FOV). DIR between CT and truncated CBCT often leads to unphysical results, especially in and near missing regions, which may result in significant errors in dose calculation afterwards. The purpose of this work is to develop and evaluate a method to improve existing DIR algorithms to solve the truncation problem. The radius of FOV of CBCT is first estimated. A recently developed robust CT-CBCT DIR algorithm, called Deformation with Intensity Simultaneously Corrected (DISC), is used for deformation field calculation. At each iteration of DISC, the calculated deformation vector field outside the FOV is replaced by a smooth propagation of the deformation field inside the FOV. Six head-and-neck cancer cases and two prostate cancer cases are used for evaluation. Dose calculation is performed to test the impacts on the resulting dose distribution. In terms of DIR accuracy, it is found that the average normalized mutual information (NMI), normalized cross correlation (NCC) and feature similarity index (FSIM) increase from 0.638, 0.948 and 0.917, to 0.641, 0.951 and 0.919, respectively, when compared with DISC without propagation. In terms of dose distribution, the relative L2 distance of dose (inside the FOV) between the ground truth and that calculated on deformed CT with propagation reduces from 9.25%to 1.41%, compared with those without propagation. We have developed a deformation field propagation method for DIR to register the planning CT and the CBCT image with small FOV. Tests on head-and-neck and prostate cancer cases have demonstrated that our algorithm can generate more accurate registration results for dose calculation. This work is supported in part by the University of California Lab Fees Research Program, the Master Research Agreement from Varian Medical Systems, Inc., and the grants from the National Natural Science Foundation of China (No.30970866).

Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity-modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas-based auto-segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure-based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross-validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method-based patient selection (previous study method, by Acosta et al.), and the Waterman's method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. The CLD in the entire prostatic urethra was 2.09±0.89mm (our proposed method), 2.77±0.99mm (Acosta et al., P=0.022), and 3.47±1.19mm (Waterman et al., P<0.001); our proposed method showed the highest accuracy. In segmented CLD, CLD in the top 1/3 segment was highly improved from that of Waterman et.al. and was slightly improved from that of Acosta et.al., with results of 2.49±1.78mm (our proposed method), 2.95±1.75mm (Acosta et al., P=0.42), and 5.76±3.09mm (Waterman et al., P<0.001). We developed a DIR accuracy prediction model-based multiatlas-based auto-segmentation method for prostatic urethra identification. Our method identified prostatic urethra with mean error of 2.09mm, likely due to combined effects of SVR model employment in patient selection, modified atlas dataset characteristics and DIR algorithm. Our method has potential utility in prostate cancer IMRT and can replace use of temporary indwelling urinary catheters.

Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible. This work aims to improve the efficiency of 4D-CBCT MoCo reconstruction using DL-based registration for the rapid generation of a motion model prior to treatment. &#xD;Approach. An artifact-reduction DL model was first used to improve the initial 4D-CBCT reconstruction by reducing streaking artifacts. Based on the artifact-reduced phase images, a groupwise DIR employing DL was used to estimate the inter-phase motion model. Two DL DIR models using different learning strategies were employed: 1) a patient-specific one-shot DIR model which was trained from scratch only using the images to be registered, and 2) a population DIR model which was pre-trained using collected 4D-CT images from 35 patients. The registration accuracy of two DL DIR models was assessed and compared to a conventional groupwise DIR approach implemented in the Elastix toolbox using the publicly available DIR-Lab dataset, a Monte Carlo simulation dataset from the SPARE challenge, and two clinical cases. &#xD;Main results. The patient-specific DIR model and the population DIR model demonstrated registration accuracy comparable to the conventional state-of-the-art methods on the DIR-Lab dataset. No significant difference in image quality was observed between the final MoCo reconstructions using the patient-specific model and population model for motion modeling, compared to using the conventional approach. The average runtime (hh:mm:ss) of the entire MoCo reconstruction on SPARE dataset was reduced from 01:37:26 using conventional DIR method to 00:10:59 using patient-specific model and 00:01:05 using the pre-trained population model. &#xD;Significance. DL-based registration methods can improve the efficiency in generating motion models for 4D-CBCT without compromising the performance of final MoCo reconstruction.

Purpose: To assess the effect of average CT (ACT) used for attenuation correction of PET data on SUVmax and gross tumor volume (GTV) in liver lesions of colorectal cancer patients. Method and Materials: We studied 34 colorectal cancer patients with metastasis in the liver. A routine PET/CT scan was immediately followed by a cine CT scan of the liver region for ACT. Patients were free‐breathing during the helical CT (HCT) and ACT acquisitions. Both HCT and ACT were used for attenuation correction of the PET data. 59 liver lesions were included in the study and SUVmax for each lesion was measured on PETHCT and PETACT. Both PETdata sets were imported to Pinnacle3 7.6 treatment planning system. GTVs for PETimages were generated based on the SUV thresholds, normally used for assessing tumor response to therapy. Percentage volume change, centroid shift and concordance index were defined for each lesion. Results: 67% of the studies showed respiratory artifacts in PETHCT while 15% demonstrated a difference in SUVmax above 20%. The effect of misregistration between HCT and PET data was more evident for smaller lesions with the most pronounced case showing a 29% increase in SUVmax for PETACT, 246% increase in GTV, 2mm shift in GTV centroid location and concordance index value of 0.34. We analyzed correlation between the variations and the lesion size with a non‐paired t‐test at 0.05 p‐value level. The means of concordance indexes in two distributions of the lesions normalized to their size (less and greater than 5cm3) were shown to be significantly different (p=0.007). Conclusion: ACT is effective in improving the registration between the CT and PET data in PET/CT, which is important when using PETimages for treatment planning. Misregistration is more pronounced for small lesions and should be considered in the generation of the GTVs.

To measure the sensitivity of deformable image registration to image noise. Deformable image registration can be used to map organ contours and other treatment planning data from one CT to another. These CT studies can be acquired with either conventional fan-beam CT systems or more novel cone-beam CT techniques. However, cone-beam CT images can have higher noise levels than fan-beam CT, which might reduce registration accuracy. We have investigated the effect of image quality differences on the deformable registration of fan-beam CTs and CTs with simulated cone-beam noise. Our study used three CT studies for each of five prostate patients. Each CT was contoured by three experienced radiation oncologists. For each patient, one CT was designated the source image and the other two were target images. A deformable image registration process was used to register each source CT to each target CT and then transfer the manually drawn treatment planning contours from the source CT to the target CTs. The accuracy of the automatically transferred contours (and thus of the deformable registration process) was assessed by comparing them to the manual contours on the target CTs, with the differences evaluated with respect to interobserver variability in the manual contours. Then each of the target CTs was modified to include increased noise characteristic of cone-beam CT and the tests were repeated. Changes in registration accuracy due to increased noise were detected by monitoring changes in the automatically transferred contours. We found that the additional noise caused no significant loss of registration accuracy at magnitudes that exceeded what would normally be found in an actual cone-beam CT. We conclude that noise levels in cone-beam CTs that might reduce manual contouring accuracy do not reduce image registration and automatic contouring accuracy.

To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems. TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results. The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt® and Velocity™. The DIR algorithms of SmartAdapt® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.

In deep brain stimulation surgery the most important step is the correct location of the neurostimulator device. Here, the medical specialist needs to robustly locate the basal ganglia area (i.e. subthalamic nucleus) to implant the neurostim-ulator. 3D brain atlas reconstruction methods have become in useful tools for building image guide surgery systems due to the benefits for 3D brain structure location in an interactive way. In this paper we proposes a 3D brain atlas reconstruction using deformable medical image registration for applications in deep brain stimulation surgeries. To quantitatively evaluate the performance of the registration process, we vary the registration parameters such as optimizers, similarity metrics and interpolators. The experimental results shows that deformable image registration in 3D medical images can efficiently reconstructs a 3D brain atlas from volumetric data previously labeled (Brain atlas labeled for a baseline patient) and can be used in image guide surgery systems.

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

Motion Freeze for Respiration Motion Correction in PET/CT: A Preliminary Investigation With Lung Cancer Patient Data

Purpose. Respiratory motion presents significant challenges for accurate PET/CT. It often introduces apparent increase of lesion size, reduction of measured standardized uptake value (SUV), and the mismatch in PET/CT fusion images. In this study, we developed the motion freeze method to use 100% of the counts collected by recombining the counts acquired from all phases of gated PET data into a single 3D PET data, with correction of respiration by deformable image registration. Methods. Six patients with diagnosis of lung cancer confirmed by oncologists were recruited. PET/CT scans were performed with Discovery STE system. The 4D PET/CT with the Varian real-time position management for respiratory motion tracking was followed by a clinical 3D PET/CT scan procedure in the static mode. Motion freeze applies the deformation matrices calculated by optical flow method to generate a single 3D effective PET image using the data from all the 4D PET phases. Results. The increase in SUV and decrease in tumor size with motion freeze for all lesions compared to the results from 3D and 4D was observed in the preliminary data of lung cancer patients. In addition, motion freeze substantially reduced tumor mismatch between the CT image and the corresponding PET images. Conclusion. Motion freeze integrating 100% of the PET counts has the potential to eliminate the influences induced by respiratory motion in PET data.

In normal free-breathing PET/CT imaging protocol, a single CT image of the patient is obtained before the PET scan, which is inadequate for attenuation correction of the free-breathing PET data. The aim of this study is to design an automatic workflow to correct mismatch and reduce motion blurring of free-breathing PET and single-scan CT data. First, the PET data was divided into four equal-count respiratory frames using data-driven respiratory gating method. Modified attenuation map was used in during reconstruction to obtain mismatch-free gated PET images. Second, the respiratory phase of the original CT image was identified automatically using mutual information (MI) as the similarity metric. Two regions of interests (ROIs) near the lungs were segmented from CT image. The mutual information (MI) inside the ROIs of each gated PET reconstruction and the CT image was measured for all gates and the phase with highest MI value is identified as the correct respiratory phase of CT image. Third, using the respiratory phase of the CT image as the reference gate, MVFs from every other gate to the reference gate was estimated using a B-spline based multi-resolution image registration algorithm. Fourth, the original CT image with identified respiratory phase was transformed to every other frame using estimated MVFs to obtain attenuation maps for all respiratory frames. Finally, image reconstruction of gated PET data was repeated using phase-matched gated PET-CT data pairs. MVFs estimated in step 3 were applied to this set of gated PET images to obtain motion compensated PET image. This method was applied to clinical datasets using both F18-FDG and Ga68 -DOTA-NOC tracer. The proposed method successfully identified the respiratory phase of the CT image and generated phase-matched CT images for all respiratory phases of PET data. Final reconstructed PET data using the proposed method showed much-reduced mismatch artifacts and motion blurring, as well as improved statistics.