Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

BACKGROUND: In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential. PURPOSE: To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition. METHODS: We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. RESULTS: The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 ± 0.7 mm in the superior-inferior (SI) direction and 0.9 ± 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, it took approximately 35 ms to generate one sDTI, and 13 ms to perform the tumor tracking using template matching. CONCLUSIONS: Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.

Transketolase drives the development of aortic dissection by impairing mitochondrial bioenergetics.

AIM: Aortic dissection (AD) is a disease with rapid onset but with no effective therapeutic drugs yet. Previous studies have suggested that glucose metabolism plays a critical role in the progression of AD. Transketolase (TKT) is an essential bridge between glycolysis and the pentose phosphate pathway. However, its role in the development of AD has not yet been elucidated. In this study, we aimed to explore the role of TKT in AD. METHODS: We collected AD patients' aortic tissues and used high-throughput proteome sequencing to analyze the main factors influencing AD development. We generated an AD model using BAPN in combination with angiotensin II (Ang II) and pharmacological inhibitors to reduce TKT expression. The effects of TKT and its downstream mediators on AD were elucidated using human aortic vascular smooth muscle cells (HAVSMCs). RESULTS: We found that glucose metabolism plays an important role in the development of AD and that TKT is upregulated in patients with AD. Western blot and immunohistochemistry confirmed that TKT expression was upregulated in mice with AD. Reduced TKT expression attenuated AD incidence and mortality, maintained the structural integrity of the aorta, aligned elastic fibers, and reduced collagen deposition. Mechanistically, TKT was positively associated with impaired mitochondrial bioenergetics by upregulating AKT/MDM2 expression, ultimately contributing to NDUFS1 downregulation. CONCLUSION: Our results provide new insights into the role of TKT in mitochondrial bioenergetics and AD progression. These findings provide new intervention options for the treatment of AD.

Patient specific prior cross attention for kV decomposition in paraspinal motion tracking.

BACKGROUND: X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy. PURPOSE: This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging. METHODS: Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information. RESULTS: The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001). CONCLUSION: The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

BACKGROUND: Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. PURPOSE: To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). METHODS: Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. RESULTS: Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. CONCLUSIONS: The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

Markerless motion tracking with simultaneous MV and kV imaging in spine SBRT treatment-a feasibility study.

Objective. Motion tracking with simultaneous MV-kV imaging has distinct advantages over single kV systems. This research is a feasibility study of utilizing this technique for spine stereotactic body radiotherapy (SBRT) through phantom and patient studies.Approach. A clinical spine SBRT plan was developed using 6xFFF beams and nine sliding-window IMRT fields. The plan was delivered to a chest phantom on a linear accelerator. Simultaneous MV-kV image pairs were acquired during beam delivery. KV images were triggered at predefined intervals, and synthetic MV images showing enlarged MLC apertures were created by combining multiple raw MV frames with corrections for scattering and intensity variation. Digitally reconstructed radiograph (DRR) templates were generated using high-resolution CBCT reconstructions (isotropic voxel size (0.243 mm)3) as the reference for 2D-2D matching. 3D shifts were calculated from triangulation of kV-to-DRR and MV-to-DRR registrations. To evaluate tracking accuracy, detected shifts were compared to known phantom shifts as introduced before treatment. The patient study included a T-spine patient and an L-spine patient. Patient datasets were retrospectively analyzed to demonstrate the performance in clinical settings.Main results. The treatment plan was delivered to the phantom in five scenarios: no shift, 2 mm shift in one of the longitudinal, lateral and vertical directions, and 2 mm shift in all the three directions. The calculated 3D shifts agreed well with the actual couch shifts, and overall, the uncertainty of 3D detection is estimated to be 0.3 mm. The patient study revealed that with clinical patient image quality, the calculated 3D motion agreed with the post-treatment cone beam CT. It is feasible to automate both kV-to-DRR and MV-to-DRR registrations using a mutual information-based method, and the difference from manual registration is generally less than 0.3 mm.Significance. The MV-kV imaging-based markerless motion tracking technique was validated through a feasibility study. It is a step forward toward effective motion tracking and accurate delivery for spinal SBRT.

Automatic stent recognition using perceptual attention U-net for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy.

PURPOSE: Stent has often been used as an internal surrogate to monitor intrafraction tumor motion during pancreatic cancer radiotherapy. Based on the stent contours generated from planning CT images, the current intrafraction motion review (IMR) system on Varian TrueBeam only provides a tool to verify the stent motion visually but lacks quantitative information. The purpose of this study is to develop an automatic stent recognition method for quantitative intrafraction tumor motion monitoring in pancreatic cancer treatment. METHODS: A total of 535 IMR images from 14 pancreatic cancer patients were retrospectively selected in this study, with the manual contour of the stent on each image serving as the ground truth. We developed a deep learning-based approach that integrates two mechanisms that focus on the features of the segmentation target. The objective attention modeling was integrated into the U-net framework to deal with the optimization difficulties when training a deep network with 2D IMR images and limited training data. A perceptual loss was combined with the binary cross-entropy loss and a Dice loss for supervision. The deep neural network was trained to capture more contextual information to predict binary stent masks. A random-split test was performed, with images of ten patients (71%, 380 images) randomly selected for training, whereas the rest of four patients (29%, 155 images) were used for testing. Sevenfold cross-validation of the proposed PAUnet on the 14 patients was performed for further evaluation. RESULTS: Our stent segmentation results were compared with the manually segmented contours. For the random-split test, the trained model achieved a mean (±standard deviation) stent Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), the center-of-mass distance (CMD), and volume difference V o l d i f f $Vo{l_{diff}}$ were 0.96 (±0.01), 1.01 (±0.55) mm, 0.66 (±0.46) mm, and 3.07% (±2.37%), respectively. The sevenfold cross-validation of the proposed PAUnet had the mean (±standard deviation) of 0.96 (±0.02), 0.72 (±0.49) mm, 0.85 (±0.96) mm, and 3.47% (±3.27%) for the DSC, HD95, CMD, and V o l d i f f $Vo{l_{diff}}$ . CONCLUSION: We developed a novel deep learning-based approach to automatically segment the stent from IMR images, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for quantitative intrafraction motion monitoring in pancreatic cancer radiotherapy.

Retrospective analysis of MV-kV imaging-based fiducial tracking in prostate SBRT treatment.

PURPOSE: Motion management is critical for prostate stereotactic body radiotherapy (SBRT) due to its high fractional dose and proximity to organs at risk. This study seeks to quantify the advantages of MV-kV tracking over kV imaging alone through a retrospective analysis of over 300 patients who underwent prostate SBRT treatment using MV-kV tracking. METHODS: An MV-kV imaging-based fiducial tracking technique has been developed at our institute and become a standard clinical practice. This technique calculates three-dimensional (3D) fiducial displacement in real time from orthogonal kV and MV images acquired simultaneously. The patient will be repositioned if for two consecutive MV-kV data points, the motion is larger than a tolerance of 1.5 mm in any of the lateral, superior-inferior, and/or anterior-posterior directions. This study retrospectively analyzed detected 3D motions using an MV-kV approach of 324 patients who recently underwent prostate SBRT treatments. An algorithm was developed to recover the 2D motion components as if they were detected by kV or MV imaging alone. RESULTS: Our results indicated that out-of-tolerance motions were primarily limited to the range of 1.5-3 mm (>95%). The motions are primarily anterior-posterior and superior-inferior, with less than 14.8% of the occurrences in the lateral direction. Compared to out-of-tolerance occurrences detected by MV-kV approach, kV alone caught 46.6% of motions in all three directions, and MV alone caught 46.7%. kV alone shows an overall missing rate of 45.8% for superior-inferior motions and 38.6% for lateral motions. It is also demonstrated that the detectability of motion in specific directions greatly depends on gantry angles, as does the missing rate. CONCLUSIONS: Our study demonstrated that MV-kV imaging-based intrafraction motion tracking is superior to single kV imaging for prostate SBRT in clinical practice.

LDHA mediated degradation of extracellular matrix is a potential target for the treatment of aortic dissection.

Aortic dissection (AD) is a disease with high mortality and lacks effective drug treatment. Recent studies have shown that the development of AD is closely related to glucose metabolism. Lactate dehydrogenase A (LDHA) is a key glycolytic enzyme and plays an important role in cardiovascular disease. However, the role of LDHA in the progression of AD remains to be elucidated. Here, we found that the level of LDHA was significantly elevated in AD patients and the mouse model established by BAPN combined with Ang II. In vitro, the knockdown of LDHA reduced the growth of human aortic vascular smooth muscle cells (HAVSMCs), glucose consumption, and lactate production induced by PDGF-BB. The overexpression of LDHA in HAVSMCs promoted the transformation of HAVSMCs from contractile phenotype to synthetic phenotype, and increased the expression of MMP2/9. Mechanistically, LDHA promoted MMP2/9 expression through the LDHA-NDRG3-ERK1/2-MMP2/9 pathway. In vivo, Oxamate, LDH and lactate inhibitor, reduced the degradation of elastic fibers and collagen deposition, inhibited the phenotypic transformation of HAVSMCs from contractile phenotype to synthetic phenotype, reduced the expression of NDRG3, p-ERK1/2, and MMP2/9, and delayed the progression of AD. To sum up, the increase of LDHA promotes the production of MMP2/9, stimulates the degradation of extracellular matrix (ECM), and promoted the transformation of HAVSMCs from contractile phenotype to synthetic phenotype. Oxamate reduced the progression of AD in mice. LDHA may be a therapeutic target for AD.

Decompose kV projection using neural network for improved motion tracking in paraspinal SBRT.

PURPOSE:  On-treatment kV images have been used in tracking patient motion. One challenge of markerless motion tracking in paraspinal SBRT is the reduced contrast when the X-ray beam needs to pass through a large portion of the patient's body, for example, from the lateral direction. Besides, due to the spine's overlapping with the surrounding moving organs in the X-ray images, auto-registration could lead to potential errors. This work aims to automatically extract the spine component from the conventional 2D X-ray images, to achieve more robust and more accurate motion management. METHODS:  A ResNet generative adversarial network (ResNetGAN) consisting of one generator and one discriminator was developed to learn the mapping between 2D kV image and the reference spine digitally reconstructed radiograph (DRR). A tailored multi-channel multi-domain loss function was used to improve the quality of the decomposed spine image. The trained model took a 2D kV image as input and learned to generate the spine component of the X-ray image. The training dataset included 1347 2D kV thoracic and lumbar region X-ray images from 20 randomly selected patients, and the corresponding matched reference spine DRR. Another 226 2D kV images from the remaining four patients were used for evaluation. The resulted decomposed spine images and the original X-ray images were registered to the reference spine DRRs, to compare the spine tracking accuracy. RESULTS:  The decomposed spine image had the mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of 60.08 and 0.99, respectively, indicating the model retained and enhanced the spine structure information in the original 2D X-ray image. The decomposed spine image matching with the reference spine DRR had submillimeter accuracy (in mm) with a mean error of 0.13, 0.12, and a maximum of 0.58, 0.49 in the x - and y -directions (in the imager coordinates), respectively. The accuracy improvement is robust in all lateral and anteroposterior X-ray beam angles. CONCLUSION:  We developed a deep learning-based approach to remove soft tissues in the kV image, leading to more accurate spine tracking in paraspinal SBRT.

Technical Note: Synthetic treatment beam imaging for motion monitoring during spine SBRT treatments - a phantom study.

PURPOSE: One of the biggest challenges in applying megavoltage (MV) treatment beam imaging for monitoring spine motion in stereotactic body radiotherapy (SBRT) is the small beam apertures in the images due to strong beam modulations in IMRT planning. The purpose of this study is to investigate the feasibility of a markerless motion tracking method in spine SBRT delivery using a novel enhanced synthetic treatment beam (ESTB) imaging technique. METHODS: Three clinical spine SBRT plans using 6XFFF beams and sliding window IMRT technique were transferred to a thorax phantom and delivered by a TrueBeam machine. Before delivery, the phantom was aligned to the plan isocenter using CBCT setup and verified with a second CBCT, and then, 2 mm shifts were introduced in both the craniocaudal (CC) and the left-right (LR) directions with the couch. During beam delivery, MV images were continuously taken with an electronic portal imaging device (EPID) and automatically grabbed by Varian iTools Capture software with a frame rate of 11.6 Hz. After preprocessing for scatter correction and beam intensity compensation, every 50 frames of MV images were combined to generate a series of ESTB images for each beam. The ESTB images were then registered to the projections of the verification CBCT at the matched beam angles to detect the 2 mm shifts. RESULTS: Compared to snapshot MV images, the ESTB images had significantly enlarged fields of view (FOVs) and improved image quality. Based on two-dimensional (2D) rigid registration, the ESTB image to CBCT projection matching showed submillimeter accuracy in detecting motion. Specifically, the root mean square errors in detecting the LR/CC shifts were 0.35/0.28, 0.32/0.35, 0.63/0.44, 0.55/0.51, and 0.69/0.42 mm at gantry angles 180, 160, 140, 120, and 100, respectively. CONCLUSION: Our results in the phantom study suggest that ESTB images from a sliding window IMRT plan can be used to detect spine motion, with submillimeter precision in the 2D plane perpendicular to the beam.

Quantifying day-to-day variations in 4DCBCT-based PCA motion models.

The aim of this paper is to quantify the day-to-day variations of motion models derived from pre-treatment 4-dimensional cone beam CT (4DCBCT) fractions for lung cancer stereotactic body radiotherapy (SBRT) patients. Motion models are built by (1) applying deformable image registration (DIR) on each 4DCBCT image with respect to a reference image from that day, resulting in a set of displacement vector fields (DVFs), and (2) applying principal component analysis (PCA) on the DVFs to obtain principal components representing a motion model. Variations were quantified by comparing the PCA eigenvectors of the motion model built from the first day of treatment to the corresponding eigenvectors of the other motion models built from each successive day of treatment. Three metrics were used to quantify the variations: root mean squared (RMS) difference in the vectors, directional similarity, and an introduced metric called the Euclidean Model Norm (EMN). EMN quantifies the degree to which a motion model derived from the first fraction can represent the motion models of subsequent fractions. Twenty-one 4DCBCT scans from five SBRT patient treatments were used in this retrospective study. Experimental results demonstrated that the first two eigenvectors of motion models across all fractions have smaller RMS (0.00017), larger directional similarity (0.528), and larger EMN (0.678) than the last three eigenvectors (RMS: 0.00025, directional similarity: 0.041, and EMN: 0.212). The study concluded that, while the motion model eigenvectors varied from fraction to fraction, the first few eigenvectors were shown to be more stable across treatment fractions than others. This supports the notion that a pre-treatment motion model built from the first few PCA eigenvectors may remain valid throughout a treatment course. Future work is necessary to quantify how day-to-day variations in these models will affect motion reconstruction accuracy for specific clinical tasks.

Semi-automated IGRT QA using a cone-shaped scintillator screen detector for proton pencil beam scanning treatments.

To promote accurate image-guided radiotherapy (IGRT) for a proton pencil beam scanning (PBS) system, a new quality assurance (QA) procedure employing a cone-shaped scintillator detector has been developed for multiple QA tasks in a semi-automatic manner. The cone-shaped scintillator detector (XRV-124, Logos Systems, CA) is sensitive to both x-ray and proton beams. It records scintillation on the cone surface as a 2D image, from which the geometry of the radiation field that enters and exits the cone can be extracted. Utilizing this feature, QA parameters that are essential to PBS IGRT treatment were measured and analyzed. The first applications provided coincidence checks of laser, imaging and radiation isocenters, and dependencies on gantry angle and beam energies. The analysis of the Winston-Lutz test was made available by combining the centricity measurements of the x-ray beam and the pencil beam. The accuracy of the gantry angle was validated against console readings provided by the digital encoder and an agreement of less than 0.2° was found. The accuracy of the position measurement was assessed with a robotic patient positioning system (PPS) and an agreement of less than 0.5 mm was obtained. The centricity of the two onboard x-ray imaging systems agreed well with that from the routinely used Digital Imaging Positioning System (DIPS), up to a consistent small shift of (-0.5 mm, 0.0 mm, -0.3 mm). The pencil beam spot size, in terms of σ of Gaussian fitting, agreed within 0.2 mm for most energies when compared to the conventional measurements by a 2D ion-chamber array (MatriXX-PT, IBA Dosimetry, Belgium). The cone-shaped scintillator system showed advantages in making multi-purpose measurements with a single setup. The in-house algorithms were successfully implemented to measure and analyze key QA parameters in a semi-automatic manner. This study presents an alternative and more efficient approach for IGRT QA for PBS and potentially for linear accelerators.

Evaluation of differential phase contrast cone beam CT imaging system.

Grating-based differential phase contrast (DPC) imaging enables the use of a hospital-grade X-ray tube, but compromises the image quality due to insufficiently coherent illumination. In this research, a bench-top DPC cone beam CT (DPC-CBCT) was systematically evaluated and compared with the traditional attenuation-based CBCT in terms of contrast to noise ratio, noise property, and contrast resolution through phantom studies. In order to evaluate DPC-CBCT for soft tissue imaging, breast specimen and small animal studies were carried out. Phantom studies indicate that phase image has lower-frequency noise, higher CNR, and improved contrast resolution. However, phase image quality was degraded in soft tissue imaging due to coherence loss caused by small-angle scattering. Hence dark-field imaging was introduced to quantitatively investigate small-angle scattering caused by an object. Experimental results indicate that inhomogeneous objects affect phase contrast imaging, phase image is more sensitive to noise, and its performance is material dependent. Dark-field imaging could also be used to locate and reduce phase image noise and artifact caused by small-angle scattering.

Reducing radiation dose in grating based x-ray phase contrast CT with twin-peaks in its phase stepping curves.

PURPOSE: Differential phase contrast CT has been recognized as an x-ray imaging method with the potential to greatly improve the differentiation of soft tissues. Talbot interferometry has been one of the promising solutions allowing implementation with commercially available x-ray tubes with a polychromatic spectrum. Mainly due to imperfections in grating fabrication and the polychromatic spectrum of x-ray beam, a twin-peaks phenomenon may exist in phase stepping curves (PSCs) and degrade the performance of phase retrieval. The authors have previously proposed a Fourier analysis based method for phase retrieval in the scenario wherein the twin-peaks phenomenon occurs in PSCs. In this work, the authors propose a 5-step algebraic method for phase retrieval and investigate the potential of reducing radiation dose while both the Fourier and algebraic methods are being utilized for phase retrieval. METHODS: The algebraic method to deal with the twin-peaks phenomenon, in which a set of linear equations with five unknown variables is needed for phase retrieval, is an extension of the so-called 3-step method that has been used in the scenario wherein only single-peak exists in the PSCs. In addition to a numerical phantom, two sets of experimental data (a phantom made of organic materials and a small animal) acquired by a prototype differential phase contrast CT system are employed to evaluate the performance of the Fourier and algebraic phase retrieval methods and their potential in radiation dose reduction. RESULTS: The evaluation by both numerical phantom and experimental data shows that the algebraic method works as well as the Fourier method in phase retrieval if the twin-peaks phenomenon in the PSCs is appropriately dealt with. In addition, while the radiation dose associated with data acquisition is being reduced via fewer phase shifting steps, the algebraic method can maintain a better performance compared to the Fourier method. CONCLUSIONS: Along with the Fourier method, the proposed 5-step algebraic method can cope with the twin-peaks phenomenon in phase retrieval. With decreased phase shifting steps and thus radiation dose, the proposed algebraic method performs better than the Fourier method, providing a practical solution for implementation of grating based differential phase contrast CT.

Grating-based x-ray differential phase contrast imaging with twin peaks in phase-stepping curves-phase retrieval and dewrapping.

PURPOSE: X-ray differential phase contrast CT implemented with Talbot interferometry employs phase-stepping to extract information of x-ray attenuation, phase shift, and small-angle scattering. Since inaccuracy may exist in the absorption grating G2 due to an imperfect fabrication, the effective period of G2 can be as large as twice the nominal period, leading to a phenomenon of twin peaks that differ remarkably in their heights. In this work, the authors investigate how to retrieve and dewrap the phase signal from the phase-stepping curve (PSC) with the feature of twin peaks for x-ray phase contrast imaging. METHODS: Based on the paraxial Fresnel-Kirchhoff theory, the analytical formulae to characterize the phenomenon of twin peaks in the PSC are derived. Then an approach to dewrap the retrieved phase signal by jointly using the phases of the first- and second-order Fourier components is proposed. Through an experimental investigation using a prototype x-ray phase contrast imaging system implemented with Talbot interferometry, the authors evaluate and verify the derived analytic formulae and the proposed approach for phase retrieval and dewrapping. RESULTS: According to theoretical analysis, the twin-peak phenomenon in PSC is a consequence of combined effects, including the inaccuracy in absorption grating G2, mismatch between phase grating and x-ray source spectrum, and finite size of x-ray tube's focal spot. The proposed approach is experimentally evaluated by scanning a phantom consisting of organic materials and a lab mouse. The preliminary data show that compared to scanning G2 over only one single nominal period and correcting the measured phase signal with an intuitive phase dewrapping method that is being used in the field, stepping G2 over twice its nominal period and dewrapping the measured phase signal with the proposed approach can significantly improve the quality of x-ray differential phase contrast imaging in both radiograph and CT. CONCLUSIONS: Using the phase retrieval and dewrapping methods proposed to deal with the phenomenon of twin peaks in PSCs and phase wrapping, the performance of grating-based x-ray differential phase contrast radiography and CT can be significantly improved.

4D cone beam CT-based dose assessment for SBRT lung cancer treatment.

The purpose of this research is to develop a 4DCBCT-based dose assessment method for calculating actual delivered dose for patients with significant respiratory motion or anatomical changes during the course of SBRT. To address the limitation of 4DCT-based dose assessment, we propose to calculate the delivered dose using time-varying ('fluoroscopic') 3D patient images generated from a 4DCBCT-based motion model. The method includes four steps: (1) before each treatment, 4DCBCT data is acquired with the patient in treatment position, based on which a patient-specific motion model is created using a principal components analysis algorithm. (2) During treatment, 2D time-varying kV projection images are continuously acquired, from which time-varying 'fluoroscopic' 3D images of the patient are reconstructed using the motion model. (3) Lateral truncation artifacts are corrected using planning 4DCT images. (4) The 3D dose distribution is computed for each timepoint in the set of 3D fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach is validated using six modified XCAT phantoms with lung tumors and different respiratory motions derived from patient data. The estimated doses are compared to that calculated using ground-truth XCAT phantoms. For each XCAT phantom, the calculated delivered tumor dose values generally follow the same trend as that of the ground truth and at most timepoints the difference is less than 5%. For the overall delivered dose, the normalized error of calculated 3D dose distribution is generally less than 3% and the tumor D95 error is less than 1.5%. XCAT phantom studies indicate the potential of the proposed method to accurately estimate 3D tumor dose distributions for SBRT lung treatment based on 4DCBCT imaging and motion modeling. Further research is necessary to investigate its performance for clinical patient data.

3D delivered dose assessment using a 4DCT-based motion model.

PURPOSE: The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course of stereotactic body radiation therapy (SBRT). METHODS: A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient-specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basis DVFs. (2) During the treatment procedure, 2D time-varying projection images (either kV or MV projections) are acquired, from which time-varying "fluoroscopic" 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time-varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio-torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT-based planning and to the actual delivered dose that was calculated using "ground truth" XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario. RESULTS: For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction-based dose estimates. CONCLUSIONS: With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern.

Cone beam breast CT with multiplanar and three dimensional visualization in differentiating breast masses compared with mammography.

OBJECTIVE: This pilot study was to evaluate cone beam breast computed tomography (CBBCT) with multiplanar and three dimensional (3D) visualization in differentiating breast masses in comparison with two-view mammograms. METHODS: Sixty-five consecutive female patients (67 breasts) were scanned by CBBCT after conventional two-view mammography (Hologic, Motarget, compression factor 0.8). For CBBCT imaging, three hundred (1024 × 768 × 16b) two-dimensional (2D) projection images were acquired by rotating the x-ray tube and a flat panel detector (FPD) 360 degree around one breast. Three-dimensional CBBCT images were reconstructed from the 2D projections. Visage CS 3.0 and Amira 5.2.2 were used to visualize reconstructed CBBCT images. RESULTS: Eighty-five breast masses in this study were evaluated and categorized under the breast imaging reporting and data system (BI-RADS) according to plain CBBCT images and two-view mammograms, respectively, prior to biopsy. BI-RADS category of each breast was compared with biopsy histopathology. The results showed that CBBCT with multiplanar and 3D visualization would be helpful to identify the margin and characteristics of breast masses. The category variance ratios for CBBCT under the BI-RADS were 23.5% for malignant tumors (MTs) and 27.3% for benign lesions in comparison with pathology, which were evidently closer to the histopathology results than those of two-view mammograms, p value <0.01. With the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) of CBBCT was 0.911, larger than that (AUC 0.827) of two-view mammograms, p value <0.01. CONCLUSION: CBBCT will be a distinctive noninvasive technology in differentiating and categorizing breast masses under BI-RADS. CBBCT may be considerably more effective to identify breast masses, especially some small, uncertain or multifocal masses than conventional two-view mammography.

Enhancement of breast calcification visualization and detection using a modified PG method in Cone Beam Breast CT.

Cone Beam Breast CT is a promising diagnostic modality in breast imaging. Its isotropic 3D spatial resolution enhances the characterization of micro-calcifications in breasts that might not be easily distinguishable in mammography. However, due to dose level considerations, it is beneficial to further enhance the visualization of calcifications in Cone Beam Breast CT images that might be masked by noise. In this work, the Papoulis-Gerchberg method was modified and implemented in Cone Beam Breast CT images to improve the visualization and detectability of calcifications. First, the PG method was modified and applied to the projections acquired during the scanning process; its effects on the reconstructed images were analyzed by measuring the Modulation Transfer Function and the Noise Power Spectrum. Second, Cone Beam Breast CT images acquired at different dose levels were pre-processed using this technique to enhance the visualization of calcification. Finally, a computer-aided diagnostic algorithm was utilized to evaluate the efficacy of this method to improve calcification detectability. The results demonstrated that this technique can effectively improve image quality by improving the Modulation Transfer Function with a minor increase in noise level. Consequently, the visualization and detectability of calcifications were improved in Cone Beam Breast CT images. This technique was also proved to be useful in reducing the x-ray dose without degrading visualization and detectability of calcifications.

Dynamic cone beam CT angiography of carotid and cerebral arteries using canine model.

PURPOSE: This research is designed to develop and evaluate a flat-panel detector-based dynamic cone beam CT system for dynamic angiography imaging, which is able to provide both dynamic functional information and dynamic anatomic information from one multirevolution cone beam CT scan. METHODS: A dynamic cone beam CT scan acquired projections over four revolutions within a time window of 40 s after contrast agent injection through a femoral vein to cover the entire wash-in and wash-out phases. A dynamic cone beam CT reconstruction algorithm was utilized and a novel recovery method was developed to correct the time-enhancement curve of contrast flow. From the same data set, both projection-based subtraction and reconstruction-based subtraction approaches were utilized and compared to remove the background tissues and visualize the 3D vascular structure to provide the dynamic anatomic information. RESULTS: Through computer simulations, the new recovery algorithm for dynamic time-enhancement curves was optimized and showed excellent accuracy to recover the actual contrast flow. Canine model experiments also indicated that the recovered time-enhancement curves from dynamic cone beam CT imaging agreed well with that of an IV-digital subtraction angiography (DSA) study. The dynamic vascular structures reconstructed using both projection-based subtraction and reconstruction-based subtraction were almost identical as the differences between them were comparable to the background noise level. At the enhancement peak, all the major carotid and cerebral arteries and the Circle of Willis could be clearly observed. CONCLUSIONS: The proposed dynamic cone beam CT approach can accurately recover the actual contrast flow, and dynamic anatomic imaging can be obtained with high isotropic 3D resolution. This approach is promising for diagnosis and treatment planning of vascular diseases and strokes.