Browsing by Subject "CBCT"

Cone-beam computed tomography (CBCT) is one of the most commonly used image modalities in radiation therapy. It provides valuable information for target localization and outcome prediction throughout treatment courses. However, CBCT images suffer from various artifacts caused by scattering, beam hardening, undersampling, system hardware instability, and motions of the patient, which severely degrade the CBCT image quality. In addition, CBCT images have extremely poor soft-tissue contrast, making it almost impossible to accurately localize tumors in the soft tissue, such as liver tumors.

This dissertation presents the improvements of CBCT image quality for better outcome prediction and target localization by developing the deep learning and finite element based image enhancement model.

A deep learning based CBCT image enhancement model was developed to improve the radiomic feature accuracy. The model was trained based on 4D CBCT of ten patients and tested on three patients with different tumor sizes. The results show that 4D CBCT image quality can substantially affect the accuracy of the radiomic features, and the degree of impact is feature-dependent. The deep learning model was able to enhance the anatomical details and edge information in the 4D-CBCT as well as removing other image artifacts. This enhancement of image quality resulted in reduced errors for most radiomic

features. The average reduction of radiomics errors for 3 patients are 20.0%, 31.4%, 36.7%, 50.0%, 33.6% and 11.3% for histogram, GLCM, GLRLM, GLSZM, NGTDM and Wavelet features. And the error reduction was more significant for patients with larger tumors. To further improve the results, a patient-specific based training model has been developed. The model was trained based on the augmentation dataset of a single patient and tested on the different 4D CBCT of the same patient. Compared with a group-based model, the patient-specific training model further improved the accuracy of radiomic features, especially for features with large errors in the group-based model. For example, the 3D whole-body and ROI loss-based patient-specific model reduces the errors of the first-order median feature by 83.67%, the wavelet LLL feature maximum by 91.98%, and the wavelet HLL skewness feature by 15.0% on average for the four patients tested.

In addition, a patient-specific deep learning model is proposed to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. A key innovation is using patient-specific CBCT-MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT-MRI images were augmented using translations and rotations to generate enough patient-specific training data. A U-Net-based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset.

Synthetic MRIs were quantitatively evaluated against ground-truth MRI. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal-to-noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images on the three patients tested. To further improve the robustness of synthetic MRI generation, we developed an organ specific biomechanical model. This model registers the pretreatment MRI images to onboard CBCT images based on the organ contours, and combines the MRI organ with CBCT body to the generate hybrid MRI/CBCT. 48 registration cases were performed, which includes 18 Monte Carlo simulated cases and 30 real patient cases. We identified tumor landmarks of hybrid MRI/CBCT, onboard CBCT and planning CT, and calculated errors of landmark locations of two CBCT images. The errors were calculated based on the landmark differences of two CBCT images and ground truth planning CT. The results show that the tumor landmark localization accuracy around tumor is improved by 54.2 ± 22.2 %.

X-PACT as a novel cancer therapy utilizes kilovoltage x-ray beam, phosphor and psoralen to treat solid tumors. Since x-ray beam is not commonly used for radiation therapy treatment purposes, a lack of treatment planning tool and plan based dose calculation is hindering the development of X-PACT. In this study, we try to approach the challenge by creating a Monte Carlo model that is an accurate representation of the actual treatment. The Monte Carlo model will be validated with commissioning measurements and is applicable to clinical data.

FLUKA is used as the Monte Carlo package for our simulations. The Monte Carlo model is created based on the Variantm OBI system. The geometry is created and optimized in Flair. To improve simulation efficiency, we collected the filtered simulated 80 kVp x-ray spectrum and used that as the source file for photon simulation. This way, the x-ray tube is bypassed, and 80 kVp photon can be simulated directly.

The validation process consists of two qualities: the back-scatter factor and the percentage depth dose. The commissioning was done separately from this study [1], and the commissioning data was used to compare with simulation results. The Model shows good overall matching to the commissioning PDD data. At small or large field size, a discrepancy between the simulation PDD data and commissioning PDD data can be observed at the surface, the differences are with 3-4%.

The final step is to apply the model to the Phase I canine trial. The clinical trial includes six dog study cases. In this thesis, Monte Carlo dose calculation based on the CBCT images of each dog study was performed for all six dog studies. The result is a 3D dose matrix for the CBCT images. According to the prescription dose for each dog study, the simulated dose was normalized, and the dose distribution for each dog was generated..

Cone beam computed tomography (CBCT) is a 3D x-ray imaging technique in which the x-ray beam is transmitted to an object with wide beam geometry producing a 2D image per projection. Due to its faster image acquisition time, wide coverage length per scan, and fewer motion artifacts, the CBCT system is rapidly replacing the conventional CT system and becoming popular in diagnostic and therapeutic radiology. However, there are few studies performed in CBCT dosimetry because of the absence of a standard dosimetric protocol for CBCT. Computed tomography dose index (CTDI), a standardized metric in conventional CT dosimetry, or direct organ dose measurements have been limitedly used in the CBCT dosimetry.

This dissertation investigated the CBCT dosimetry from the CTDI method to the organ, effective dose, risk estimations with physical measurements and Monte Carlo (MC) simulations.

An On-Board Imager (OBI, Varian Medical Systems, Palo Alto, CA) was used to perform old and new CBCT scan protocols. The new CBCT protocols introduced both partial and full angle scan modes while the old CBCT protocols only used the full angle mode. A metal-oxide-semiconductor-field-effect transistor (MOSFET) and an ion chamber were employed to measure the cone beam CTDI (CTDI_CB) in CT phantoms and organ dose in a 5-year-old pediatric anthropomorphic phantom. Radiochromic film was also employed to measure the axial dose profiles. A point dose method was used in the CTDI estimation.

The BEAMnrc/EGSnrc MC system was used to simulate the CBCT scans; the MC model of the OBI x-ray tube was built into the system and validated by measurements characterizing the cone beam quality in the aspects of the x-ray spectrum, half value layer (HVL) and dose profiles for both full-fan and half-fan modes. Using the validated MC model, CTDI_CB, dose profile integral (DPI), cone beam dose length product (DLP_CB), and organ doses were calculated with voxelized MC CT phantoms or anthropomorphic phantoms. Effective dose and radiation risks were estimated from the organ dose results.

The CTDI_CB of the old protocols were found to be 84 and 45 mGy for standard dose, head and body protocols. The CTDI_CB of the new protocols were found to be 6.0, 3.2, 29.0, 25.4, 23.8, and 7.7 mGy for the standard dose head, low dose head, high quality head, pelvis, pelvis spotlight, and low dose thorax protocols respectively. The new scan protocols were found to be advantageous in reducing the patient dose while offering acceptable image quality.

The mean effective dose (ED) was found to be 37.8 ±0.7 mSv for the standard head and 8.1±0.2 mSv for the low dose head protocols (old) in the 5-year-old phantom. The lifetime attributable risk (LAR) of cancer incidence ranged from 23 to 144 cases per 100,000 exposed persons for the standard-dose mode and from five to 31 cases per 100,000 exposed persons for the low-dose mode. The relative risk (RR) of cancer incidence ranged from 1.003 to 1.054 for the standard-dose mode and from 1.001 to 1.012 for the low-dose mode.

The MC method successfully estimated the CTDI_CB, organ and effective dose despite the heavy calculation time. The point dose method was found to be capable of estimating the CBCT dose with reasonable accuracy in the clinical environment.

Purpose: Cone beam computed tomography (CBCT) plays an important role in image guidance for interventional radiology and radiation therapy by providing 3D volumetric images of the patient. However, CBCT suffers from relatively low image quality with severe image artifacts due to the nature of the image acquisition and reconstruction process. This work investigated the feasibility of using deep learning networks to substantially augment the image quality of CBCT by learning a direct mapping from the original CBCT images to their corresponding ground truth CT images. The possibility of using deep learning for scatter correction in CBCT projections was also investigated.

Methods: Two deep learning networks, i.e. a symmetric residual convolutional neural network (SR-CNN) and a U-net convolutional network, were trained to use the input CBCT images to produce high-quality CBCT images that match with the corresponding ground truth CT images. Both clinical and Monte Carlo simulated datasets were included for model training. In order to eliminate the misalignments between CBCT and the corresponding CT, rigid registration was applied to clinical database. The binary masks achieved by Otsu auto-thresholding method were applied to for Monte Carlo simulate data to avoid the negative impact of non-anatomical structures on images. After model training, a new set of CBCT images were fed into the trained network to obtain augmented CBCT images, and the performances were evaluated and compared both qualitatively and quantitatively. The augmented CBCT images were quantitatively compared to CT using the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).

Regarding the study for using deep learning for the scatter correction in CBCT, the scatter signal for each projection was acquired by Monte Carlo simulation. U-net model was trained to predict the scatter signals based on the original CBCT projections. Then the predicted scatter components were subtracted from the original CBCT projections to obtain scatter-corrected projections. CBCT image reconstructed by the scatter-corrected projections were quantitatively compared with that reconstructed by original projections.

Results: The augmented CBCT images by both SR-CNN and U-net models showed substantial improvement in image quality. Compared to original CBCT, the augmented CBCT images also achieve much higher PSNR and SSIM in quantitative evaluation. U-net demonstrated better performance than SR-CNN in quantitative evaluation and computational speed for CBCT image quality augmentation.

With the scatter correction in CBCT projections predicted by U-net, the scatter-corrected CBCT images demonstrated substantial improvement of the image contrast and anatomical details compared to the original CBCT images.

Conclusion: The proposed deep learning models can effectively augment CBCT image quality by correcting artifacts and reducing scatter. Given their relatively fast computational speeds and great performance, they can potentially become valuable tools to substantially enhance the quality of CBCT to improve its precision for target localization and adaptive radiotherapy.

Cone-beam computed tomography (CBCT) is an X-ray-based imaging modality widely used in medical practices. Due to the ionizing imaging dose induced by CBCT, many studies were conducted to reduce the number of projections (sparse sampling) to lower the imaging dose while maintaining good image quality and fast reconstruction speed. Conventionally, a CBCT volume is reconstructed analytically with the Feldkamp Davis Kress (FDK) algorithm that backprojects filtered projections according to projection angles. However, the FDK algorithm requires a dense angular sampling that satisfies the Shannon-Nyquist theorem. The FDK algorithm reconstructs CBCT with a high speed but requires relatively high patient imaging dose. The iterative methods like algebraic reconstruction technique (ART) and compressed sensing (CS) methods are investigated to reduce patient imaging dose. These iterative methods update estimated images iteratively and the CS methods apply penalty terms to award desired features. Yet these methods are limited by the iterative design with substantially increased computation time and consumption of computation power. Scholars have also conducted research on bypassing the limit of Shannon-Nyquist theorem by interpolating densely sampled CBCT projections from sparsely sampled projections. However, blurred structures in reconstructed images remain to be a concern for analytical interpolation methods. As such, previous research indicates that it is hard to achieve the three goals of lowered patient imaging dose, good image quality, and fast reconstruction speed all at once.

As deep learning (DL) gained popularity in fields like computer vision and data science, scholars also applied DL techniques in medical image processing. Studies on DL-based CT image reconstruction have yielded encouraging results, but GPU memory limitation made it challenging to apply DL techniques on CBCT reconstruction.

In this dissertation, we hypothesize that the image quality of CBCT reconstructed from under-sampled projections (low-dose) using deep learning techniques can be comparable to that of CBCT reconstructed from fully sampled projections for treatment verification in radiation therapy. This dissertation proposes that by applying DL techniques in pre-processing, reconstruction, and post-processing stages, the challenge of improving CBCT image quality with low imaging dose and fast reconstruction speed can be mitigated.

The dissertation proposed a geometry-guided deep learning (GDL) technique, which is as the first technique to perform end-to-end CBCT reconstruction from sparsely sampled projections and demonstrated its feasibility for CBCT reconstruction from real patient projection data. In this study, we have found that incorporating geometry information into the DL technique can effectively reduce the model size, mitigating the memory limitation in CBCT reconstruction. The novel GDL technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The additional deep learning post-processing module further improves image quality after reconstruction.

This dissertation further optimizes the number of beamlets used in the GDL technique through a geometry-guided multi-beamlet deep learning (GMDL) technique. In addition to connecting each pixel in the projection domain to beamlet points along the central beamlet in the image domain as GDL does, these smaller fully connected layers in GMDL connect each pixel to beamlets peripheral to the central beamlet based on the CT projection geometry. Due to the limitation of GPU VRAM, the proposed technique is demonstrated through low-dose CT image reconstruction and is compared with the GDL technique and a large fully connected layer-based reconstruction method.

In addition, the dissertation also investigates deep learning-based CBCT projection interpolation and proposes a patient-independent deep learning projection interpolation technique for CBCT reconstruction. Different from previous studies that interpolate phantom or simulated data, the proposed technique is demonstrated to work on real patient projection data with unevenly distributed projection angles. The proposed technique re-slices the stack of interpolated projections axially, and each acquired slice is processed by a deep residual U-Net (DRU) model to augment the slice’s image quality. The resulting slices are reassembled into a stack of densely-sampled projections to be reconstructed into a CBCT volume. A second DRU model further post-processes the reconstructed CBCT volume to improve the image quality.

In summary, a geometry-guided deep learning (GDL) technique was proposed as the first deep learning technique for end-to-end CBCT reconstruction from sparsely sampled real patient projection data. The geometry-guided multi-beamlet deep learning (GMDL) technique further optimizes the number of beamlets based on the GDL technique. A patient-independent deep learning projection interpolation technique was also proposed for the pre-processing and post-processing stage of CBCT reconstruction.

In conclusion, the work presented in this dissertation demonstrates the feasibility of improving CBCT image quality with low imaging dose and fast reconstruction speed. The techniques developed in this dissertation also have great potential for clinical applications to enhance CBCT imaging for radiation therapy.

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

This thesis consists of two studies. Study 1 is an assessment of dose-volume metrics of an 18F-FDG PET adaptive radiation therapy for vulvar and cervical cancer patients.Study 2 is an evaluation of cumulative dose distributions from external beam radiation therapy using CT-to-CBCT deformable image registration (DIR) for cervical cancer patients.

Study 1: Assessment of dose-volume metrics of an 18F-FDG PET adaptive radiation therapy for vulvar and cervical cancer patientsPurpose: Adaptive radiation therapy (ART) enables treatment to be modified with the goal of improving the dose distribution to the patient due to changes in anatomy. Fluorodeoxyglucose positron emission tomography and computed tomography (FDG-PET/CT) is used for staging, treatment planning, and assessing treatment response, but can also be used to adapt treatment. In an adaptive PET/CT study, an additional PET/CT scan is acquired for planning purposes after a certain prescribed dose has been delivered. The intratreatment PET/CT is used to re-contour the volumes and create a new treatment plan that is used to deliver the remaining dose for the treatment. The goal of adaptive radiation therapy (ART) is to reduce the dose to normal tissues while maintaining the prescribed dose to the adapted PTV. Materials and Methods: In this IRB-approved protocol, patients with vulvar and cervical cancer received a planning PET/CT and an intratreatment PET/CT. Radiation therapy consisted of either intensity modulated radiotherapy (IMRT) or volumetric modulated arc therapy (VMAT) with 1.8 Gy once daily to a total of 45 to 50.4 Gy and simultaneous integrated boosts (SIB) to involved pelvic or para-aortic (PA) lymph nodes. The primary tumor was treated to 64.4 to 66.4 Gy with sequential boosts for the vulvar cancer patients. Cervical cancer patients were boosted with brachytherapy. SIB dose ranged from a total of 64.4 Gy to 66.4 Gy in 25 fractions determined by the treating physician and organs-at risk (OAR) tolerance. An intratreatment PET/CT was obtained at 12-20 fractions when the delivered dose was between 30 to 36 Gy. All patients were re-planned with revised OAR, gross tumor volume (GTV) and planning target volume (PTV) contours. The same dose goals remained on the adapted plan. Dosimetric metrics for OARs were compared using the Wilcoxon signed rank test. The criteria for determining statistical significance was established as a p-value less than 0.05. Results: In the vulvar analysis, out of 20 eligible patients, ART resulted in significant reductions in OAR doses. For bladder, max dose (Dmax) median reduction (MR) was 1.1 Gy ((IQR 0.48 – 2.3 Gy), p < 0.001) and for D2cc MR was 1.5 Gy ((IQR 0.51 – 2.1 Gy), p < 0.001). For bowel, Dmax MR was 1.0 Gy ((IQR 0.11 – 2.9 Gy), p < 0.001), for D2cc MR was 0.39 Gy ((IQR 0.023 – 1.7 Gy), p < 0.001), and for D15cc MR was 0.19 Gy ((IQR 0.026 – 0.47 Gy), p = 0.002)). For rectum, mean dose (Dmean) MR was 0.66 Gy ((IQR 0.17 – 1.7 Gy) p = 0.006) and for D2cc MR was 0.46 Gy ((IQR 0.17 – 0.80 Gy), p = 0.006). Thirty-seven cervical patients were analyzed. ART resulted in significant reductions in OAR doses. For bladder, max dose (Dmax) median reduction (MR) was 0.89 Gy ((IQR 0.23 – 2.14 Gy), p = 0.001) and for D2cc MR was 0.38 Gy ((IQR 0.12 – 1.36 Gy), p<0.0001). For bowel, Dmax MR was 3.27 Gy ((IQR 0.50 – 5.41 Gy), p < 0.0001). For D2cc MR was 2.09 Gy ((IQR 0.30 – 4.97 Gy), p < 0.0001), and for D15cc MR was 0.57 Gy (IQR 0.22 – 2.07 Gy)). For rectum, Dmean MR was 0.13 Gy ((IQR 0.09 – 0.24 Gy) p = 0.0025), and for D2cc MR was 0.44 Gy ((IQR 0.14 – 1.02 Gy), p < 0.0001). Conclusions: Based on the analysis and response to ART of 20 eligible patients with vulvar cancer and 37 eligible patients with cervical cancer, it can be concluded that ART resulted in a significant reduction in OAR doses, including bladder, bowel, and rectum. Overall, these findings suggest that ART can effectively reduce the radiation dose to OARs and improve treatment outcomes for patients with gynecological cancers.

Study 2: Evaluation of cumulative dose distributions from external beam radiation therapy using CT-to-CBCT deformable image registration (DIR) for cervical cancer patients Purpose: Organ motion during radiation therapy in the pelvic region can potentially lead to uncertainties with the dose delivered to critical organs during fractionated treatment. The purpose of this study is to investigate, by means of using deformable image registration (DIR) and dose summation techniques, the differences between the planning dose and the delivered dose as calculated from the longitudinal cone-beam CT (CBCT) images for cervical cancer patients. Materials and Methods: Cervical cancer patients treated with external beam radiation therapy (EBRT) received a planning CT (pCT) and five CBCTs, once every five fractions of treatment. The “Merged CBCT” feature in MIM Maestro (MIM Software, Cleveland, OH) was performed between the pCT and each CBCT to generate an extended field-of-view (FOV) CBCT (mCBCT). A free-form multi-modality DIR was then performed between the pCT and the mCBCT to deform the pCT structures onto the mCBCT. DIR-generated bladder and rectum contours were further adjusted by a physician, and Dice Similarity Coefficients (DSC) were calculated between the two. After deformation, the investigated doses on the mCBCT were: 1) recalculated in Eclipse TPS (Varian Medical Systems, Palo Alto, CA) using original plan parameters (ecD), and 2) deformed from planning dose (pD) using the deformation matrix (mdD). Dose summation was performed to the first week’s mCBCT. Bladder D2cc, Dmax, Dmean, V45, and D50, rectum D2cc, Dmax, Dmean, and D50, and PTV45 D90 and D98 were compared between the three calculated doses. Dose distributions were compared in terms of dose volume histograms (DVHs) and gamma analysis. The Wilcoxon signed rank test was used to compare dosimetric metrics with statistical significance defined at p < 0.05. Results: For the ten patients analyzed, the average DSC were 0.72 ± 0.15 for bladder and 0.80 ± 0.11 for rectum. For most cases, only the superior and inferior slices were edited by physician. Regardless of the method of dose calculation (ecD or mdD), D2cc (bladder and rectum), and D90 and D98 (PTV45) were within 5% of pD for at least 9 out of 10 patients. For one patient each for bladder, rectum, and PTV45, the agreement was worse than 5%, with the largest difference of 15.3% for bladder D2cc in a patient with large bladder filling differences. For the Eclipse calculated dose on the merged CBCT (ecD) and t;he MIM deformed dose on merged CBCT (mdD), the bladder Dmax was within 5% for 8 out of 10 patients, and rectum Dmax was within 5% for 7 out of 10 patients. All 10 patients for ecD and mdD were >5% for bladder V45 due to the large variations in bladder volume throughout treatment. Statistically significant differences for bladder D2cc between the ecD and the mdD (p = 0.047). For bladder D50, significant differences between pD and ecD (p = 0.009) and ecD and mdD (p = 0.005). Statistically significant differences for rectum D2cc between the pD and ecD (p = 0.028) as well as ecD and mdD (p = 0.005). Statistically significant differences for D98 between the pD and ecD (p = 0.028) and pD and mdD (p = 0.007). The gamma analysis between the ecD and pD matched 90% of the voxels for 3 out of 10 patients and between the mdD and pD for 1 out of 10 patients. Conclusions: In this study, we evaluated cumulative doses based on weekly CBCTs using a commercially-available DIR software. Using DIR and the new Merged CBCT feature, we determined that reporting the initial planning dose would not introduce a more than 5% difference in 90% of cases studied. Our results indicate that the mdD produces similar dose values as the ecD for the OARs and PTV. The proposed workflow should be used on a case-by-case basis when the weekly CBCT shows marked difference in organs-at-risk from the planning CT.