Modeling respiratory motion for reducing motion artifacts in 4D CT images

Application and dosimetric comparison of surface-guided deep inspiration breath-hold for lung stereotactic body radiotherapy

Respiratory motion management is the crucial challenge for safe and effective application of lung stereotactic body radiotherapy (SBRT). The present study implemented lung SBRT treatment in voluntary deep inspiration breath-hold (DIBH) with surface-guided radiotherapy (SGRT) system and evaluated the geometric and dosimetric benefits of DIBH to organs-at-risk (OARs), aiming to advising the choice between DIBH technology and conventional free breathing 4 dimensions (FB-4D) technology. Five patients of lung SBRT treated in DIBH with SGRT at our institution were retrospectively analyzed. CT scans were acquired in DIBH and FB-4D, treatment plans were generated for both respiratory phases. The geometric and dosimetry of tumor, ipsilateral lung, double lungs and heart were compared between the DIBH and FB-4D treatment plans. In terms of target coverage, utilizing DIBH significantly reduced the mean plan target volume (PTV) by 21.9% (p = 0.09) compared to FB-4D, the conformity index (CI) of DIBH and FB-4D were comparable, but the dose gradient index (DGI) of DIBH was higher. With DIBH expanding lung, the volumes of ipsilateral lung and double lungs were 2535.1 ± 403.0cm3 and 4864.3 ± 900.2cm3, separately, 62.2% (p = 0.009) and 73.1% (p = 0.009) more than volumes of ipsilateral lung (1460.03 ± 146.60cm3) and double lungs (2811.25 ± 603.64cm3) in FB-4D. The heart volume in DIBH was 700.0 ± 146.1cm3, 11.6% (p = 0.021) less than that in FB-4D. As for OARs protection, the mean dose, percent of volume receiving > 20Gy (V20) and percent of volume receiving > 5Gy (V5) of ipsilateral lung in DIBH were significantly lower by 33.2% (p = 0.020), 44.0% (p = 0.022) and 24.5% (p = 0.037) on average, separately. Double lungs also showed significant decrease by 31.1% (p = 0.019), 45.5% (p = 0.024) and 20.9% (p = 0.048) on average for mean dose, V20 and V5 in DIBH. Different from the lung, the mean dose and V5 of heart showed no consistency between DIBH and FB-4D, but lower maximum dose of heart was achieved in DIBH for all patients in this study. Appling lung SBRT in DIBH with SGRT was feasibly performed with high patient compliance. DIBH brought significant dosimetric benefits to lung, however, it caused more or less irradiated heart dose that depend on the patients' individual differences which were unpredictable.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR.

On the evaluation of mobile target trajectory between four-dimensional computer tomography and four-dimensional cone-beam computer tomography

Purpose

For mobile lung tumors, four‐dimensional computer tomography (4D CT) is often used for simulation and treatment planning. Localization accuracy remains a challenge in lung stereotactic body radiation therapy (SBRT) treatments. An attractive image guidance method to increase localization accuracy is 4D cone‐beam CT (CBCT) as it allows for visualization of tumor motion with reduced motion artifacts. However, acquisition and reconstruction of 4D CBCT differ from that of 4D CT. This study evaluates the discrepancies between the reconstructed motion of 4D CBCT and 4D CT imaging over a wide range of sine target motion parameters and patient waveforms.

Methods

A thorax motion phantom was used to examine 24 sine motions with varying amplitudes and cycle times and seven patient waveforms. Each programmed motion was imaged using 4D CT and 4D CBCT. The images were processed to auto segment the target. For sine motion, the target centroid at each phase was fitted to a sinusoidal curve to evaluate equivalence in amplitude between the two imaging modalities. The patient waveform motion was evaluated based on the average 4D data sets.

Results

The mean difference and root‐mean‐square‐error between the two modalities for sine motion were −0.35 ± 0.22 and 0.60 mm, respectively, with 4D CBCT slightly overestimating amplitude compared with 4D CT. The two imaging methods were determined to be significantly equivalent within ±1 mm based on two one‐sided t tests (p < 0.001). For patient‐specific motion, the mean difference was 1.5 ± 2.1 (0.8 ± 0.6 without outlier), 0.4 ± 0.3, and 0.8 ± 0.6 mm for superior/inferior (SI), anterior/posterior (AP), and left/right (LR), respectively.

Conclusion

In cases where 4D CT is used to image mobile tumors, 4D CBCT is an attractive localization method due to its assessment of motion with respect to 4D CT, particularly for lung SBRT treatments where accuracy is paramount.

Tissue-specific deformable image registration using a spatial-contextual filter

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Motion artifacts are a major factor that can degrade the diagnostic performance of computed tomography (CT) images. In particular, the motion artifacts become considerably more severe when an imaging system requires a long scan time such as in dental CT or cone-beam CT (CBCT) applications, where patients generate rigid and non-rigid motions. To address this problem, we proposed a new real-time technique for motion artifacts reduction that utilizes a deep residual network with an attention module. Our attention module was designed to increase the model capacity by amplifying or attenuating the residual features according to their importance. We trained and evaluated the network by creating four benchmark datasets with rigid motions or with both rigid and non-rigid motions under a step-and-shoot fan-beam CT (FBCT) or a CBCT. Each dataset provided a set of motion-corrupted CT images and their ground-truth CT image pairs. The strong modeling power of the proposed network model allowed us to successfully handle motion artifacts from the two CT systems under various motion scenarios in real-time. As a result, the proposed model demonstrated clear performance benefits. In addition, we compared our model with Wasserstein generative adversarial network (WGAN)-based models and a deep residual network (DRN)-based model, which are one of the most powerful techniques for CT denoising and natural RGB image deblurring, respectively. Based on the extensive analysis and comparisons using four benchmark datasets, we confirmed that our model outperformed the aforementioned competitors. Our benchmark datasets and implementation code are available at https://github.com/youngjun-ko/ct_mar_attention.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Non-rigid image registration of 4D-MRI data for improved delineation of moving tumors

Background

To increase the image quality of end-expiratory and end-inspiratory phases of retrospective respiratory self-gated 4D MRI data sets using non-rigid image registration for improved target delineation of moving tumors.

Methods

End-expiratory and end-inspiratory phases of volunteer and patient 4D MRI data sets are used as targets for non-rigid image registration of all other phases using two different registration schemes: In the first, all phases are registered directly (dir-Reg) while next neighbors are successively registered until the target is reached in the second (nn-Reg). Resulting data sets are quantitatively compared using diaphragm and tumor sharpness and the coefficient of variation of regions of interest in the lung, liver, and heart. Qualitative assessment of the patient data regarding noise level, tumor delineation, and overall image quality was performed by blinded reading based on a 4 point Likert scale.

Results

The median coefficient of variation was lower for both registration schemes compared to the target. Median dir-Reg coefficient of variation of all ROIs was 5.6% lower for expiration and 7.0% lower for inspiration compared with nn-Reg. Statistical significant differences between the two schemes were found in all comparisons. Median sharpness in inspiration is lower compared to expiration sharpness in all cases. Registered data sets were rated better compared to the targets in all categories. Over all categories, mean expiration scores were 2.92 ± 0.18 for the target, 3.19 ± 0.22 for nn-Reg and 3.56 ± 0.14 for dir-Reg and mean inspiration scores 2.25 ± 0.12 for the target, 2.72 ± 215 0.04 for nn-Reg and 3.78 ± 0.04 for dir-Reg.

Conclusions

In this work, end-expiratory and inspiratory phases of a 4D MRI data sets are used as targets for non-rigid image registration of all other phases. It is qualitatively and quantitatively shown that image quality of the targets can be significantly enhanced leading to improved target delineation of moving tumors.

Anatomic change over the course of treatment for non-small cell lung cancer patients and its impact on intensity-modulated radiation therapy and passive-scattering proton therapy deliveries

Purpose

To quantify tumor anatomic change of non-small cell lung cancer (NSCLC) patients given passive-scattering proton therapy (PSPT) and intensity-modulated radiation therapy (IMRT) through 6–7 weeks of treatment, and analyze the correlation between anatomic change and the need to adopt adaptive radiotherapy (ART).

Materials and methods

Weekly 4D CT sets of 32 patients (8/8 IMRT with/without ART, 8/8 PSPT with/without ART) acquired during treatment, were registered to the planning CT using an in-house developed deformable registration algorithm. The anatomic change was quantified as the mean variation of the region of interest (ROI) relative to the planning CT by averaging the magnitude of deformation vectors of all voxels within the ROI contour. Mean variations of GTV and CTV were compared between subgroups classified by ART status and treatment modality using the independent t-test. Logistic regression analysis was performed to clarify the effect of anatomic change on the probability of ART adoption.

Results

There was no significant difference (p = 0.679) for the time-averaged mean CTV variations from the planning CT between IMRT (7.61 ± 2.80 mm) and PSPT (7.21 ± 2.67 mm) patients. However, a significant difference (p = 0.001) was observed between ART (8.93 ± 2.19 mm) and non-ART (5.90 ± 2.33 mm) patients, when treatment modality was not considered. Mean CTV variation from the planning CT in all patients increases significantly (p < 0.001), with a changing rate of 1.77 mm per week. Findings for the GTV change was similar. The logistic regression model correctly predicted 71.9% of cases in ART adoption. The correlation is stronger in the PSPT group with a pseudo R² value of 0.782, compared to that in the IMRT group (pseudo R² = 0.182).

Conclusion

The magnitude of target volume variation over time could be greater than the usual treatment margin. Mean target volume variation from the planning position can be used to identify lung cancer patients that may need ART.

Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate

We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.

Technical Note: Density correction to improve CT number mapping in thoracic deformable image registration

PURPOSE

To improve the accuracy of computed tomography (CT) number mapping inside the lung in deformable image registration with large differences in lung volume for applications in vertical CT imaging and adaptive radiotherapy.

METHODS

The deep inspiration breath hold (DIBH) CT image and the end of exhalation (EE) phase image in four-dimensional CT of 14 thoracic cancer patients were used in this study. Lung volumes were manually delineated. A Demons-based deformable registration was first applied to register the EE CT to the DIBH CT for each patient, and the resulting deformation vector field deformed the EE CT image to the DIBH CT space. Given that the mass of the lung remains the same during respiration, we created a mass-preserving model to correlate lung density variations with volumetric changes, which were characterized by the Jacobian derived from the deformation field. The Jacobian determinant was used to correct the lung CT numbers transferred from the EE CT image. The absolute intensity differences created by subtracting the deformed EE CT from the DIBH CT with and without density correction were compared.

RESULTS

The ratio of DIBH CT to EE CT lung volumes was 1.6 on average. The deformable registration registered the lung shape well, but the appearance of voxel intensities inside the lung was different, demonstrating the need for density correction. Without density correction, the mean and standard deviation of the absolute intensity difference between the deformed EE CT and the DIBH CT inside the lung were 54.5 ± 45.5 for all cases. After density correction, these numbers decreased to 18.1 ± 34.9, demonstrating greater accuracy. The cumulative histogram of the intensity difference also showed that density correction improved CT number mapping greatly.

CONCLUSION

Density correction improves CT number mapping inside the lung in deformable image registration for difficult cases with large lung volume differences.

Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis

Background Current imaging methods are not sensitive to changes in pulmonary function resulting from fibrosis. MRI with ultrashort echo time can be used to image the lung parenchyma and lung motion. Purpose To evaluate elastic registration of inspiratory-to-expiratory lung MRI for the assessment of pulmonary fibrosis in study participants with systemic sclerosis (SSc). Materials and Methods This prospective study was performed from September 2017 to March 2018 and recruited healthy volunteers and participants with SSc and high-resolution CT (within the previous 3 months) of the chest for lung MRI. Two breath-hold, coronal, three-dimensional, ultrashort-echo-time, gradient-echo sequences of the lungs were acquired after full inspiration and expiration with a 3.0-T unit. Images were registered from inspiration to expiration by using an elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Similarity between areas with marked shrinkage and logarithm of Jacobian determinants less than -0.15 were compared between healthy volunteers and study participants with SSc. Receiver operating characteristic curve analysis was performed to determine the best Dice similarity coefficient threshold for diagnosis of fibrosis. Results Sixteen participants with SSc (seven with pulmonary fibrosis at high-resolution CT) and 11 healthy volunteers were evaluated. Areas of marked shrinkage during expiration with logarithm of Jacobian determinants less than -0.15 were found in the posterior lung bases of healthy volunteers and in participants with SSc without fibrosis, but not in participants with fibrosis. The sensitivity and specificity of MRI for presence of fibrosis at high-resolution CT were 86% and 75%, respectively (area under the curve, 0.81; P = .04) by using a threshold of 0.36 for Dice similarity coefficient. Conclusion Elastic registration of inspiratory-to-expiratory MRI shows less lung base respiratory deformation in study participants with systemic sclerosis-related pulmonary fibrosis compared with participants without fibrosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Biederer in this issue.

Technical Note: Solving the "Chinese postman problem" for effective contour deformation

PURPOSE

To develop a practical approach for accurate contour deformation when deformable image registration (DIR) is used for atlas-based segmentation or contour propagation in image-guided radiotherapy.

METHODS

We developed a contour deformation approach based on 3D mesh operations. The 2D contours represented by a series of points in each slice were first converted to a 3D triangular mesh, which was deformed by the deformation vectors resulting from DIR. A set of parallel 2D planes then cut through the deformed 3D mesh, generating unordered points and line segments, to be reorganized into a set of 2D contour points. The reorganization problem was equivalent to solving the "Chinese postman problem" (CPP) by traversing a graph built from the unordered points with the least cost. Alternatively, deformation could be applied to a binary image converted from the original contours. The deformed binary image was then converted back into contours at the CT slice locations. We validated the mesh-based contour deformation approach using lung and heart contours from 10 patients with thoracic cancer.

RESULTS

DIR could change the 3D mesh considerably, complicating 2D contour representations after deformation. CPP could effectively reorganize the points in 2D planes regardless of how complicated the 2D contours were. Among the 10 patients, the Dice similarity coefficient between the mesh-based contour and binary image-based contour was 97.6% ± 0.3% for lung and 97.5% ± 0.7% for heart, and the Hausdoroff distance between them was 19.8 ± 5.1 mm for lung and 6.1 ± 2.2 mm for heart. Subjective evaluation showed that the mesh-based approach could keep fine details, especially for the lung. The image-based approach seemed to overprocess contours and suffered from image resolution limits.

CONCLUSION

We developed a practical approach for accurate contour deformation and demonstrated its effectiveness for both clinical and research applications.

Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis

Accurate tracking and modeling of internal and external respiratory motion in the thoracic and abdominal regions of a human body is a highly discussed topic in external beam radiotherapy treatment. Errors in target/normal tissue delineation and dose calculation and the increment of the healthy tissues being exposed to high radiation doses are some of the unsolicited problems caused due to inaccurate tracking of the respiratory motion. Many related works have been introduced for respiratory motion modeling, but a majority of them highly depend on radiography/fluoroscopy imaging, wearable markers or surgical node implanting techniques. We, in this article, propose a new respiratory motion tracking approach by exploiting the advantages of an RGB-D camera. First, we create a patient-specific respiratory motion model using principal component analysis (PCA) removing the spatial and temporal noise of the input depth data. Then, this model is utilized for real-time external respiratory motion measurement with high accuracy. Additionally, we introduce a marker-based depth frame registration technique to limit the measuring area into an anatomically consistent region that helps to handle the patient movements during the treatment. We achieved a 0.97 correlation comparing to a spirometer and 0.53 mm average error considering a laser line scanning result as the ground truth. As future work, we will use this accurate measurement of external respiratory motion to generate a correlated motion model that describes the movements of internal tumors.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

PURPOSE

Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error.

METHODS

The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error.

RESULTS

The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images.

CONCLUSIONS

The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

New prospective 4D-CT for mitigating the effects of irregular respiratory motion

Artifact caused by irregular respiration is a major source of error in 4D-CT imaging. We propose a new prospective 4D-CT to mitigate this source of error without new hardware, software or off-line data-processing on the GE CT scanner. We utilize the cine CT scan in the design of the new prospective 4D-CT. The cine CT scan at each position can be stopped by the operator when an irregular respiration occurs, and resumed when the respiration becomes regular. This process can be repeated at one or multiple scan positions. After the scan, a retrospective reconstruction is initiated on the CT console to reconstruct only the images corresponding to the regular respiratory cycles. The end result is a 4D-CT free of irregular respiration. To prove feasibility, we conducted a phantom and six patient studies. The artifacts associated with the irregular respiratory cycles could be removed from both the phantom and patient studies. A new prospective 4D-CT scanning and processing technique to mitigate the impact of irregular respiration in 4D-CT has been demonstrated. This technique can save radiation dose because the repeat scans are only at the scan positions where an irregular respiration occurs. Current practice is to repeat the scans at all positions. There is no cost to apply this technique because it is applicable on the GE CT scanner without new hardware, software or off-line data-processing.

Quantitative evaluation by measurement and modeling of the variations in dose distributions deposited in mobile targets

The objective of this study is to quantitatively evaluate variations of dose distributions deposited in mobile target by measurement and modeling. The effects of variation in dose distribution induced by motion on tumor dose coverage and sparing of normal tissues were investigated quantitatively. The dose distributions with motion artifacts were modeled considering different motion patterns that include (a) motion with constant speed and (b) sinusoidal motion. The model predictions of the dose distributions with motion artifacts were verified with measurement where the dose distributions from various plans that included three-dimensional conformal and intensity-modulated fields were measured with a multiple-diode-array detector (MapCheck2), which was mounted on a mobile platform that moves with adjustable motion parameters. For each plan, the dose distributions were then measured with MapCHECK2 using different motion amplitudes from 0-25 mm. In addition, mathematical modeling was developed to predict the variations in the dose distributions and their dependence on the motion parameters that included amplitude, frequency and phase for sinusoidal motions. The dose distributions varied with motion and depended on the motion pattern particularly the sinusoidal motion, which spread out along the direction of motion. Study results showed that in the dose region between isocenter and the 50% isodose line, the dose profile decreased with increase of the motion amplitude. As the range of motion became larger than the field length along the direction of motion, the dose profiles changes overall including the central axis dose and 50% isodose line. If the total dose was delivered over a time much longer than the periodic time of motion, variations in motion frequency and phase do not affect the dose profiles. As a result, the motion dose modeling developed in this study provided quantitative characterization of variation in the dose distributions induced by motion, which can be employed in radiation therapy to quantitatively determine the margins needed for treatment planning considering dose spillage to normal tissue.

Automated identification and reduction of artifacts in cine four-dimensional computed tomography (4DCT) images using respiratory motion model

PURPOSE

Four-dimensional computed tomography (4DCT) images are often marred by artifacts that substantially degrade image quality and confound image interpretation. Human observation remains the standard method of 4DCT artifact evaluation, which is time-consuming and subjective. We develop a method to automatically identify and reduce artifacts in cine 4DCT images.

METHODS

We proposed an algorithm that consisted of two main stages: deformable image registration and respiratory motion simulation. Specifically, each 4DCT phase image was registered to the breath-holding CT image using the block-matching method, with erroneous spatial matches removed by the least median of squares filter and the full displacement vector field generated by the moving least squares interpolation. The lung's respiratory motion trajectory was then recovered from the displacement vector field using the parameterized polynomial function, with fitting parameters estimated by combinatorial optimization. In this way, artifacts were located according to deviations between image points and their motion trajectories and further corrected based on position prediction.

RESULTS

The mean spatial error (standard deviation) was 1.00 (0.85) mm after registration as opposed to 6.96 (4.61) mm before registration. In addition, we took human observation conducted by medical experts as the gold standard. The average sensitivity, specificity, and accuracy of the proposed method in artifact identification were 0.97, 0.84, and 0.89, respectively.

CONCLUSIONS

The proposed method identified and reduced artifacts accurately and automatically, providing an alternative way to analyze 4DCT image quality and to correct problematic images for radiation therapy.

Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT

We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L₁ norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold σ. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.

Impact of Real-Time Image Gating on Spot Scanning Proton Therapy for Lung Tumors: A Simulation Study

PURPOSE

To investigate the effectiveness of real-time-image gated proton beam therapy for lung tumors and to establish a suitable size for the gating window (GW).

METHODS AND MATERIALS

A proton beam gated by a fiducial marker entering a preassigned GW (as monitored by 2 fluoroscopy units) was used with 7 lung cancer patients. Seven treatment plans were generated: real-time-image gated proton beam therapy with GW sizes of ±1, 2, 3, 4, 5, and 8 mm and free-breathing proton therapy. The prescribed dose was 70 Gy (relative biological effectiveness)/10 fractions to 99% of the target. Each of the 3-dimensional marker positions in the time series was associated with the appropriate 4-dimensional computed tomography phase. The 4-dimensional dose calculations were performed. The dose distribution in each respiratory phase was deformed into the end-exhale computed tomography image. The D99 and D5 to D95 of the clinical target volume scaled by the prescribed dose with criteria of D99 >95% and D5 to D95 <5%, V20 for the normal lung, and treatment times were evaluated.

RESULTS

Gating windows ≤ ±2 mm fulfilled the CTV criteria for all patients (whereas the criteria were not always met for GWs ≥ ±3 mm) and gave an average reduction in V20 of more than 17.2% relative to free-breathing proton therapy (whereas GWs ≥ ±4 mm resulted in similar or increased V20). The average (maximum) irradiation times were 384 seconds (818 seconds) for the ±1-mm GW, but less than 226 seconds (292 seconds) for the ±2-mm GW. The maximum increased considerably at ±1-mm GW.

CONCLUSION

Real-time-image gated proton beam therapy with a GW of ±2 mm was demonstrated to be suitable, providing good dose distribution without greatly extending treatment time.

Modeling the Nonlinear Motion of the Rat Central Airways

Advances in volumetric medical imaging techniques allowed the subject-specific modeling of the bronchial flow through the first few generations of the central airways using computational fluid dynamics (CFD). However, a reliable CFD prediction of the bronchial flow requires modeling of the inhomogeneous deformation of the central airways during breathing. This paper addresses this issue by introducing two models of the central airways motion. The first model utilizes a node-to-node mapping between the discretized geometries of the central airways generated from a number of successive computed tomography (CT) images acquired dynamically (without breath hold) over the breathing cycle of two Sprague-Dawley rats. The second model uses a node-to-node mapping between only two discretized airway geometries generated from the CT images acquired at end-exhale and at end-inhale along with the ventilator measurement of the lung volume change. The advantage of this second model is that it uses just one pair of CT images, which more readily complies with the radiation dosage restrictions for humans. Three-dimensional computer aided design geometries of the central airways generated from the dynamic-CT images were used as benchmarks to validate the output from the two models at sampled time-points over the breathing cycle. The central airway geometries deformed by the first model showed good agreement to the benchmark geometries within a tolerance of 4%. The central airway geometry deformed by the second model better approximated the benchmark geometries than previous approaches that used a linear or harmonic motion model.

Breathing guidance in radiation oncology and radiology: A systematic review of patient and healthy volunteer studies

PURPOSE

The advent of image-guided radiation therapy has led to dramatic improvements in the accuracy of treatment delivery in radiotherapy. Such advancements have highlighted the deleterious impact tumor motion can have on both image quality and radiation treatment delivery. One approach to reducing tumor motion irregularities is the use of breathing guidance systems during imaging and treatment. These systems aim to facilitate regular respiratory motion which in turn improves image quality and radiation treatment accuracy. A review of such research has yet to be performed; it was therefore their aim to perform a systematic review of breathing guidance interventions within the fields of radiation oncology and radiology.

METHODS

From August 1-14, 2014, the following online databases were searched: Medline, Embase, PubMed, and Web of Science. Results of these searches were filtered in accordance to a set of eligibility criteria. The search, filtration, and analysis of articles were conducted in accordance with preferred reporting items for systematic reviews and meta-analyses. Reference lists of included articles, and repeat authors of included articles, were hand-searched.

RESULTS

The systematic search yielded a total of 480 articles, which were filtered down to 27 relevant articles in accordance to the eligibility criteria. These 27 articles detailed the intervention of breathing guidance strategies in controlled studies assessing its impact on such outcomes as breathing regularity, image quality, target coverage, and treatment margins, recruiting either healthy adult volunteers or patients with thoracic or abdominal lesions. In 21/27 studies, significant (p < 0.05) improvements from the use of breathing guidance were observed.

CONCLUSIONS

There is a trend toward the number of breathing guidance studies increasing with time, indicating a growing clinical interest. The results found here indicate that further clinical studies are warranted that quantify the clinical impact of breathing guidance, along with the health technology assessment to determine the advantages and disadvantages of breathing guidance.

Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs

Respiratory motion is one major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation and compensation methods typically rely on breathing motion models learned from certain training data, and therefore may not be able to effectively handle intra-subject and/or inter-subject variations of respiratory motion. In this paper, we propose a respiratory motion compensation framework that directly recovers motion fields from sparsely spaced and efficiently acquired dynamic 2-D MRIs without using a learned respiratory motion model. We present a scatter-to-volume deformable registration algorithm to register dynamic 2-D MRIs with a static 3-D MRI to recover dense deformation fields. Practical considerations and approximations are provided to solve the scatter-to-volume registration problem efficiently. The performance of the proposed method was investigated on both synthetic and real MRI datasets, and the results showed significant improvements over the state-of-art respiratory motion modeling methods. We also demonstrated a potential application of the proposed method on MRI-based motion corrected PET imaging using hybrid PET/MRI.

Learning anatomy changes from patient populations to create artificial CT images for voxel-level validation of deformable image registration

The purpose of this study was to develop an approach to generate artificial computed tomography (CT) images with known deformation by learning the anatomy changes in a patient population for voxel‐level validation of deformable image registration. Using a dataset of CT images representing anatomy changes during the course of radiation therapy, we selected a reference image and registered the remaining images to it, either directly or indirectly, using deformable registration. The resulting deformation vector fields (DVFs) represented the anatomy variations in that patient population. The mean deformation, computed from the DVFs, and the most prominent variations, which were captured using principal component analysis (PCA), composed an active shape model that could generate random known deformations with realistic anatomy changes based on those learned from the patient population. This approach was applied to a set of 12 head and neck patients who received intensity‐modulated radiation therapy for validation. Artificial planning CT and daily CT images were generated to simulate a patient with known anatomy changes over the course of treatment and used to validate the deformable image registration between them. These artificial CT images potentially simulated the actual patients' anatomies and also showed realistic anatomy changes between different daily CT images. They were used to successfully validate deformable image registration applied to intrapatient deformation.

PACS number: 87.57.nj

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning

To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients (f_v) to account for most of the spectrum energy (Σf_v²). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves (R = 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.

A Method for Assessing Ground-Truth Accuracy of the 5DCT Technique

PURPOSE

To develop a technique that assesses the accuracy of the breathing phase-specific volume image generation process by patient-specific breathing motion model using the original free-breathing computed tomographic (CT) scans as ground truths.

METHODS

Sixteen lung cancer patients underwent a previously published protocol in which 25 free-breathing fast helical CT scans were acquired with a simultaneous breathing surrogate. A patient-specific motion model was constructed based on the tissue displacements determined by a state-of-the-art deformable image registration. The first image was arbitrarily selected as the reference image. The motion model was used, along with the free-breathing phase information of the original 25 image datasets, to generate a set of deformation vector fields that mapped the reference image to the 24 nonreference images. The high-pitch helically acquired original scans served as ground truths because they captured the instantaneous tissue positions during free breathing. Image similarity between the simulated and the original scans was assessed using deformable registration that evaluated the pointwise discordance throughout the lungs.

RESULTS

Qualitative comparisons using image overlays showed excellent agreement between the simulated images and the original images. Even large 2-cm diaphragm displacements were very well modeled, as was sliding motion across the lung-chest wall boundary. The mean error across the patient cohort was 1.15 ± 0.37 mm, and the mean 95th percentile error was 2.47 ± 0.78 mm.

CONCLUSION

The proposed ground truth-based technique provided voxel-by-voxel accuracy analysis that could identify organ-specific or tumor-specific motion modeling errors for treatment planning. Despite a large variety of breathing patterns and lung deformations during the free-breathing scanning session, the 5-dimensionl CT technique was able to accurately reproduce the original helical CT scans, suggesting its applicability to a wide range of patients.

Rapid estimation of 4DCT motion-artifact severity based on 1D breathing-surrogate periodicity

PURPOSE

Motion artifacts are common in patient four-dimensional computed tomography (4DCT) images, leading to an ill-defined tumor volume with large variations for radiotherapy treatment and a poor foundation with low imaging fidelity for studying respiratory motion. The authors developed a method to estimate 4DCT image quality by establishing a correlation between the severity of motion artifacts in 4DCT images and the periodicity of the corresponding 1D respiratory waveform (1DRW) used for phase binning in 4DCT reconstruction.

METHODS

Discrete Fourier transformation (DFT) was applied to analyze 1DRW periodicity. The breathing periodicity index (BPI) was defined as the sum of the largest five Fourier coefficients, ranging from 0 to 1. Distortional motion artifacts (excluding blurring) of cine-scan 4DCT at the junctions of adjacent couch positions around the diaphragm were classified in three categories: incomplete, overlapping, and duplicate anatomies. To quantify these artifacts, discontinuity of the diaphragm at the junctions was measured in distance and averaged along six directions in three orthogonal views. Artifacts per junction (APJ) across the entire diaphragm were calculated in each breathing phase and phase-averaged APJ¯, defined as motion-artifact severity (MAS), was obtained for each patient. To make MAS independent of patient-specific motion amplitude, two new MAS quantities were defined: MAS(D) is normalized to the maximum diaphragmatic displacement and MAS(V) is normalized to the mean diaphragmatic velocity (the breathing period was obtained from DFT analysis of 1DRW). Twenty-six patients' free-breathing 4DCT images and corresponding 1DRW data were studied.

RESULTS

Higher APJ values were found around midventilation and full inhalation while the lowest APJ values were around full exhalation. The distribution of MAS is close to Poisson distribution with a mean of 2.2 mm. The BPI among the 26 patients was calculated with a value ranging from 0.25 to 0.93. The DFT calculation was within 3 s per 1DRW. Correlations were found between 1DRW periodicity and 4DCT artifact severity: -0.71 for MAS(D) and -0.73 for MAS(V). A BPI greater than 0.85 in a 1DRW suggests minimal motion artifacts in the corresponding 4DCT images.

CONCLUSIONS

The breathing periodicity index and motion-artifact severity index are introduced to assess the relationship between 1DRW and 4DCT. A correlation between 1DRW periodicity and 4DCT artifact severity has been established. The 1DRW periodicity provides a rapid means to estimate 4DCT image quality. The rapid 1DRW analysis and the correlative relationship can be applied prospectively to identify irregular breathers as candidates for breath coaching prior to 4DCT scan and retrospectively to select high-quality 4DCT images for clinical motion-management research.

Reconstruction of four-dimensional computed tomography lung images by applying spatial and temporal anatomical constraints using a Bayesian model

Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.

A method for volumetric imaging in radiotherapy using single x-ray projection

PURPOSE

It is an intriguing problem to generate an instantaneous volumetric image based on the corresponding x-ray projection. The purpose of this study is to develop a new method to achieve this goal via a sparse learning approach.

METHODS

To extract motion information hidden in projection images, the authors partitioned a projection image into small rectangular patches. The authors utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients was built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are then further used to generate a motion vector field and hence a volumetric image. The authors have also proposed an intensity baseline correction method based on the partitioned projection, in which the first and the second moments of pixel intensities at a patch in a simulated projection image are matched with those in a measured one via a linear transformation. The proposed method has been validated in both simulated data and real phantom data.

RESULTS

The algorithm is able to identify patches that contain relevant motion information such as the diaphragm region. It is found that an intensity baseline correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced the prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning was not used, and the 95th percentile error for the predicted motion vector was reduced from 2.40 to 0.92 mm. In the phantom case with a regular tumor motion, the predicted tumor trajectory was successfully reconstructed with a 0.82 mm error for tumor center localization compared to a 1.66 mm error without using the sparse learning method. When the tumor motion was driven by a real patient breathing signal with irregular periods and amplitudes, the average tumor center error was 0.6 mm. The algorithm robustness with respect to sparsity level, patch size, and presence or absence of diaphragm, as well as computation time, has also been studied.

CONCLUSIONS

The authors have developed a new method that automatically identifies motion information from an x-ray projection, based on which a volumetric image is generated.

Retracted: Reducing motion artifacts in 4D MR images using principal component analysis (PCA) combined with linear polynomial fitting model

We have previously developed a retrospective 4D-MRI technique using body area as the respiratory surrogate, but generally, the reconstructed 4D MR images suffer from severe or mild artifacts mainly caused by irregular motion during image acquisition. Those image artifacts may potentially affect the accuracy of tumor target delineation or the shape representation of surrounding nontarget tissues and organs. So the purpose of this study is to propose an approach employing principal component analysis (PCA), combined with a linear polynomial fitting model, to remodel the displacement vector fields (DVFs) obtained from deformable image registration (DIR), with the main goal of reducing the motion artifacts in 4D MR images. Seven patients with hepatocellular carcinoma (2/7) or liver metastases (5/7) in the liver, as well as a patient with non-small cell lung cancer (NSCLC), were enrolled in an IRB-approved prospective study. Both CT and MR simulations were performed for each patient for treatment planning. Multiple-slice, multiple-phase, cine-MRI images were acquired in the axial plane for 4D-MRI reconstruction. Single-slice 2D cine-MR images were acquired across the center of the tumor in axial, coronal, and sagittal planes. For a 4D MR image dataset, the DVFs in three orthogonal direction (inferior–superior (SI), anterior–posterior (AP), and medial–lateral (ML)) relative to a specific reference phase were calculated using an in-house DIR algorithm. The DVFs were preprocessed in three temporal and spatial dimensions using a polynomial fitting model, with the goal of correcting the potential registration errors introduced by three-dimensional DIR. Then PCA was used to decompose each fitted DVF into a linear combination of three principal motion bases whose spanned subspaces combined with their projections had been validated to be sufficient to represent the regular respiratory motion. By wrapping the reference MR image using the remodeled DVFs, 'synthetic' MR images with reduced motion artifacts were generated at selected phase. Tumor motion trajectories derived from cine-MRI, 4D CT, original 4D MRI, and 'synthetic' 4D MRI were analyzed in the SI, AP, and ML directions, respectively. Their correlation coefficient (CC) and difference (D) in motion amplitude were calculated for comparison. Of all the patients, the means and standard deviations (SDs) of CC comparing 'synthetic' 4D MRI and cine-MRI were 0.98 ± 0.01, 0.98 ± 0.01, and 0.99 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.59 ± 0.09 mm, 0.29± 0.10 mm, and 0.15 ± 0.05 mm in SI, AP and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and 4D CT were 0.96 ± 0.01, 0.95± 0.01, and 0.95 ± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.76 ± 0.20 mm, 0.33 ± 0.14 mm, and 0.19± 0.07 mm in SI, AP, and ML directions, respectively. The means and SDs of CC comparing 'synthetic' 4D MRI and original 4D MRI were 0.98 ± 0.01, 0.98± 0.01, and 0.97± 0.01 in SI, AP, and ML directions, respectively. The mean ± SD Ds were 0.58 ± 0.10 mm, 0.30 ± 0.09mm, and 0.17 ± 0.04 mm in SI, AP, and ML directions, respectively. In this study we have proposed an approach employing PCA combined with a linear polynomial fitting model to capture the regular respiratory motion from a 4D MR image dataset. And its potential usefulness in reducing motion artifacts and improving image quality has been demonstrated by the preliminary results in oncological patients.

Evaluation of 4D CT acquisition methods designed to reduce artifacts

Four-dimensional computed tomography (4D CT) is used to account for respiratory motion in radiation treatment planning, but artifacts resulting from the acquisition and postprocessing limit its accuracy. We investigated the efficacy of three experimental 4D CT acquisition methods to reduce artifacts in a prospective institutional review board approved study. Eighteen thoracic patients scheduled to undergo radiation therapy received standard clinical 4D CT scans followed by each of the alternative 4D CT acquisitions: 1) data oversampling, 2) beam gating with breathing irregularities, and 3) rescanning the clinical acquisition acquired during irregular breathing. Relative values of a validated correlation-based artifact metric (CM) determined the best acquisition method per patient. Each 4D CT was processed by an extended phase sorting approach that optimizes the quantitative artifact metric (CM sorting). The clinical acquisitions were also postprocessed by phase sorting for artifact comparison of our current clinical implementation with the experimental methods. The oversampling acquisition achieved the lowest artifact presence among all acquisitions, achieving a 27% reduction from the current clinical 4D CT implementation (95% confidence interval = 34-20). The rescan method presented a significantly higher artifact presence from the clinical acquisition (37%; p < 0.002), the gating acquisition (26%; p < 0.005), and the oversampling acquisition (31%; p < 0.001), while the data lacked evidence of a significant difference between the clinical, gating, and oversampling methods. The oversampling acquisition reduced artifact presence from the current clinical 4D CT implementation to the largest degree and provided the simplest and most reproducible implementation. The rescan acquisition increased artifact presence significantly, compared to all acquisitions, and suffered from combination of data from independent scans over which large internal anatomic shifts occurred.

Generation of fluoroscopic 3D images with a respiratory motion model based on an external surrogate signal

Respiratory motion during radiotherapy can cause uncertainties in definition of the target volume and in estimation of the dose delivered to the target and healthy tissue. In this paper, we generate volumetric images of the internal patient anatomy during treatment using only the motion of a surrogate signal. Pre-treatment four-dimensional CT imaging is used to create a patient-specific model correlating internal respiratory motion with the trajectory of an external surrogate placed on the chest. The performance of this model is assessed with digital and physical phantoms reproducing measured irregular patient breathing patterns. Ten patient breathing patterns are incorporated in a digital phantom. For each patient breathing pattern, the model is used to generate images over the course of thirty seconds. The tumor position predicted by the model is compared to ground truth information from the digital phantom. Over the ten patient breathing patterns, the average absolute error in the tumor centroid position predicted by the motion model is 1.4 mm. The corresponding error for one patient breathing pattern implemented in an anthropomorphic physical phantom was 0.6 mm. The global voxel intensity error was used to compare the full image to the ground truth and demonstrates good agreement between predicted and true images. The model also generates accurate predictions for breathing patterns with irregular phases or amplitudes.

Evaluation of accuracy of B-spline transformation-based deformable image registration with different parameter settings for thoracic images

Deformable image registration (DIR) is fundamental technique for adaptive radiotherapy and image-guided radiotherapy. However, further improvement of DIR is still needed. We evaluated the accuracy of B-spline transformation-based DIR implemented in elastix. This registration package is largely based on the Insight Segmentation and Registration Toolkit (ITK), and several new functions were implemented to achieve high DIR accuracy. The purpose of this study was to clarify whether new functions implemented in elastix are useful for improving DIR accuracy. Thoracic 4D computed tomography images of ten patients with esophageal or lung cancer were studied. Datasets for these patients were provided by DIR-lab (dir-lab.com) and included a coordinate list of anatomical landmarks that had been manually identified. DIR between peak-inhale and peak-exhale images was performed with four types of parameter settings. The first one represents original ITK (Parameter 1). The second employs the new function of elastix (Parameter 2), and the third was created to verify whether new functions improve DIR accuracy while keeping computational time (Parameter 3). The last one partially employs a new function (Parameter 4). Registration errors for these parameter settings were calculated using the manually determined landmark pairs. 3D registration errors with standard deviation over all cases were 1.78 (1.57), 1.28 (1.10), 1.44 (1.09) and 1.36 (1.35) mm for Parameter 1, 2, 3 and 4, respectively, indicating that the new functions are useful for improving DIR accuracy, even while maintaining the computational time, and this B-spline-based DIR could be used clinically to achieve high-accuracy adaptive radiotherapy.

Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion

Conventional in-room cone-beam computed tomography (CBCT) lacks explicit representation of patient respiratory motion and usually has poor image quality and inaccurate CT numbers for target delineation and/or adaptive treatment planning. In-room four-dimensional (4D) CBCT image acquisition is still time consuming and suffers the same issue of poor image quality. To overcome this limitation, we developed a computational framework to digitally synthesize high-quality daily 4D CBCT images using the prior knowledge of motion and appearance learned from the planning 4D CT dataset. A patient-specific respiratory motion model was first constructed from the planning 4D CT images using principal component analysis of displacement vector fields across different respiratory phases. Subsequently, the respiratory motion model as well as the image content of the planning CT was spatially mapped onto the daily CBCT using deformable image registration. The synthesized 4D images possess explicit patient motion while maintaining the geometric accuracy of patient's anatomy at the time of treatment. We validated our model by quantitatively comparing the synthesized 4D CBCT against the 4D CT dataset acquired in the same day from protocol patients undergoing daily in-room CBCT setup and weekly 4D CT for treatment evaluation. Our preliminary results have demonstrated good agreement of contours in different motion phases between the synthesized and acquired scans. Various imaging artifacts were also suppressed and soft-tissue visibility was enhanced.

Assessment of a quantitative metric for 4D CT artifact evaluation by observer consensus

The benefits of four-dimensional computed tomography (4D CT) are limited by the presence of artifacts that remain difficult to quantify. A correlation-based metric previously proposed for ciné 4D CT artifact identification was further validated as an independent artifact evaluator by using a novel qualitative assessment featuring a group of observers reaching a consensus decision on artifact location and magnitude. The consensus group evaluated ten ciné 4D CT scans for artifacts over each breathing phase of coronal lung views assuming one artifact per couch location. Each artifact was assigned a magnitude score of 1-5, 1 indicating lowest severity and 5 indicating highest severity. Consensus group results served as the ground truth for assessment of the correlation metric. The ten patients were split into two cohorts; cohort 1 generated an artifact identification threshold derived from receiver operating characteristic analysis using the Youden Index, while cohort 2 generated sensitivity and specificity values from application of the artifact threshold. The Pearson correlation coefficient was calculated between the correlation metric values and the consensus group scores for both cohorts. The average sensitivity and specificity values found with application of the artifact threshold were 0.703 and 0.476, respectively. The correlation coefficients of artifact magnitudes for cohort 1 and 2 were 0.80 and 0.61, respectively, (p < 0.001 for both); these correlation coefficients included a few scans with only two of the five possible magnitude scores. Artifact incidence was associated with breathing phase (p < 0.002), with presentation less likely near maximum exhale. Overall, the correlation metric allowed accurate and automated artifact identification. The consensus group evaluation resulted in efficient qualitative scoring, reduced interobserver variation, and provided consistent identification of artifact location and magnitudes.