Bowen Meng

First Demonstration of Multiplexed X-Ray Fluorescence Computed Tomography (XFCT) Imaging

Simultaneous imaging of multiple probes or biomarkers represents a critical step toward high specificity molecular imaging. In this work, we propose to utilize the element-specific nature of the X-ray fluorescence (XRF) signal for imaging multiple elements simultaneously (multiplexing) using XRF computed tomography (XFCT). A 5-mm-diameter pencil beam produced by a polychromatic X-ray source (150 kV, 20 mA) was used to stimulate emission of XRF photons from 2% (weight/volume) gold (Au), gadolinium (Gd), and barium (Ba) embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The X-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used to isolate the K shell XRF peak and to generate sinograms for the three elements of interest. The distribution and concentration of the three elements were reconstructed with the iterative maximum likelihood expectation maximization algorithm. The linearity between the XFCT intensity and the concentrations of elements of interest was investigated. We found that measured XRF spectra showed sharp peaks characteristic of Au, Gd, and Ba. The narrow full-width at half-maximum (FWHM) of the peaks strongly supports the potential of XFCT for multiplexed imaging of Au, Gd, and Ba (FWHMAu,Kα1 = 0.619 keV, FWHMAu,Kα2=1.371 keV , FWHMGd,Kα=1.297 keV, FWHMGd,Kβ=0.974 keV , FWHMBa,Kα=0.852 keV, and FWHMBa,Kβ=0.594 keV ). The distribution of Au, Gd, and Ba in the water phantom was clearly identifiable in the reconstructed XRF images. Our results showed linear relationships between the XRF intensity of each tested element and their concentrations (R2Au=0.944 , RGd2=0.986, and RBa2=0.999), suggesting that XFCT is capable of quantitative imaging. Finally, a transmission CT image was obtained to show the potential of the approach for providing attenuation correction and morphological information. In conclusion, XFCT is a promising modality for multiplexed imaging of high atomic number probes.

Ultrafast and scalable cone‐beam CT reconstruction using MapReduce in a cloud computing environment

PURPOSE Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. METHODS In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm. RESULTS Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed successfully with identical results even when half of the nodes were manually terminated in the middle of the process. CONCLUSIONS An ultrafast, reliable and scalable 4D CBCT∕CT reconstruction method was developed using the MapReduce framework. Unlike other parallel computing approaches, the parallelization and speedup required little modification of the original reconstruction code. MapReduce provides an efficient and fault tolerant means of solving large-scale computing problems in a cloud computing environment.

Clinical Implementation of Intrafraction Cone Beam Computed Tomography Imaging During Lung Tumor Stereotactic Ablative Radiation Therapy

PURPOSE To develop and clinically evaluate a volumetric imaging technique for assessing intrafraction geometric and dosimetric accuracy of stereotactic ablative radiation therapy (SABR). METHODS AND MATERIALS Twenty patients received SABR for lung tumors using volumetric modulated arc therapy (VMAT). At the beginning of each fraction, pretreatment cone beam computed tomography (CBCT) was used to align the soft-tissue tumor position with that in the planning CT. Concurrent with dose delivery, we acquired fluoroscopic radiograph projections during VMAT using the Varian on-board imaging system. Those kilovolt projections acquired during millivolt beam-on were automatically extracted, and intrafraction CBCT images were reconstructed using the filtered backprojection technique. We determined the time-averaged target shift during VMAT by calculating the center of mass of the tumor target in the intrafraction CBCT relative to the planning CT. To estimate the dosimetric impact of the target shift during treatment, we recalculated the dose to the GTV after shifting the entire patient anatomy according to the time-averaged target shift determined earlier. RESULTS The mean target shift from intrafraction CBCT to planning CT was 1.6, 1.0, and 1.5 mm; the 95th percentile shift was 5.2, 3.1, 3.6 mm; and the maximum shift was 5.7, 3.6, and 4.9 mm along the anterior-posterior, left-right, and superior-inferior directions. Thus, the time-averaged intrafraction gross tumor volume (GTV) position was always within the planning target volume. We observed some degree of target blurring in the intrafraction CBCT, indicating imperfect breath-hold reproducibility or residual motion of the GTV during treatment. By our estimated dose recalculation, the GTV was consistently covered by the prescription dose (PD), that is, V100% above 0.97 for all patients, and minimum dose to GTV >100% PD for 18 patients and >95% PD for all patients. CONCLUSIONS Intrafraction CBCT during VMAT can provide geometric and dosimetric verification of SABR valuable for quality assurance and potentially for treatment adaptation.

Development of XFCT imaging strategy for monitoring the spatial distribution of platinum-based chemodrugs: Instrumentation and phantom validation

PURPOSE Developing an imaging method to directly monitor the spatial distribution of platinum-based (Pt) drugs at the tumor region is of critical importance for early assessment of treatment efficacy and personalized treatment. In this study, the authors investigated the feasibility of imaging platinum (Pt)-based drug distribution using x-ray fluorescence (XRF, a.k.a. characteristic x ray) CT (XFCT). METHODS A 5-mm-diameter pencil beam produced by a polychromatic x-ray source equipped with a tungsten anode was used to stimulate emission of XRF photons from Pt drug embedded within a water phantom. The phantom was translated and rotated relative to the stationary pencil beam in a first-generation CT geometry. The x-ray energy spectrum was collected for 18 s at each position using a cadmium telluride detector. The spectra were then used for the K-shell XRF peak isolation and sinogram generation for Pt. The distribution and concentration of Pt were reconstructed with an iterative maximum likelihood expectation maximization algorithm. The capability of XFCT to multiplexed imaging of Pt, gadolinium (Gd), and iodine (I) within a water phantom was also investigated. RESULTS Measured XRF spectrum showed a sharp peak characteristic of Pt with a narrow full-width at half-maximum (FWHM) (FWHMKα1 = 1.138 keV, FWHMKα2 = 1.052 keV). The distribution of Pt drug in the water phantom was clearly identifiable on the reconstructed XRF images. Our results showed a linear relationship between the XRF intensity of Pt and its concentrations (R(2) = 0.995), suggesting that XFCT is capable of quantitative imaging. A transmission CT image was also obtained to show the potential of the approach for providing attenuation correction and morphological information. Finally, the distribution of Pt, Gd, and I in the water phantom was clearly identifiable in the reconstructed images from XFCT multiplexed imaging. CONCLUSIONS XFCT is a promising modality for monitoring the spatial distribution of Pt drugs. The technique may be useful in tailoring tumor treatment regimen in the future.

Distributed MLEM: An Iterative Tomographic Image Reconstruction Algorithm for Distributed Memory Architectures

The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach.

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval.

PURPOSE X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space. METHODS The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme. RESULTS The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction. CONCLUSIONS A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

A unified framework for 3D radiation therapy and IMRT planning: plan optimization in the beamlet domain by constraining or regularizing the fluence map variations

The purpose of this work is to demonstrate that physical constraints on fluence gradients in 3D radiation therapy (RT) planning can be incorporated into beamlet optimization explicitly by direct constraint on the spatial variation of the fluence maps or implicitly by using total-variation regularization (TVR). The former method forces the fluence to vary in accordance with the known form of a wedged field and latter encourages the fluence to take the known form of the wedged field by requiring the derivatives of the fluence maps to be piece-wise constant. The performances of the proposed methods are evaluated by using a brain cancer case and a head and neck case. It is found that both approaches are capable of providing clinically sensible 3D RT solutions with monotonically varying fluence maps. For currently available 3D RT delivery schemes based on the use of customized physical or dynamic wedges, constrained optimization seems to be more useful because the optimized fields are directly deliverable. Working in the beamlet domain provides a natural way to model the spatial variation of the beam fluence. The proposed methods take advantage of the fact that 3D RT is a special form of intensity-modulated radiation therapy (IMRT) and finds the optimal plan by searching for fields with a certain type of spatial variation. The approach provides a unified framework for 3D CRT and IMRT plan optimization.

TH‐C‐103‐06: Non‐Coplanar Cone Beam CT Reconstruction with Limited Angle of Projections Using Rigid Image Registration Facilitated Prior Image Constrained Compressed Sensing

PURPOSE The purpose of this work is to develop a non-coplanar cone beam CT (CBCT) image reconstruction method from limited angle of projections. METHODS Planning CT or CBCT acquired at neutral position (without couch rotation/translation) serves as the prior images for successive reconstruction. During the course of radiation therapy, subsequent CBCT scans are acquired from limited angle of projections. An iterative reconstruction algorithm, using prior image constrained compressed sensing (PICCS) framework and rigid image registration, is incorporated to reconstruct the patient image under non-coplanar geometry. The prior reconstructed image is rotated/translated according to the nominal couch rotation/translation to serve as the prior input of the compressed sensing based iterative optimization. First, The PICCS framework can be solved iteratively using the total variation minimization step and the iterative image reconstruction step with simultaneous algebraic reconstruction technique (SART). Then, rigid image registration between the reconstructed image and the rotated/translated prior image is applied to further calibrate the rotation/translation of the prior image. The iterations of PICCS algorithm and rigid image registration continue until the registration results are below the predetermined threshold. The proposed reconstruction algorithm is evaluated with both digital phantom simulations and experimental data. RESULTS The proposed CBCT reconstruction algorithm significantly improved the image quality from projections with angular range as small as 72 degrees. It can reduce the rigid translational setup errors under from 8 mms to below 1 mm and the rigid rotational setup error from 5 degrees to below 1 degree. Compared with the original PICCS algorithm alone, the rigid registration step helps to improve the convergence of algorithm significantly. CONCLUSIONS The proposed algorithm provides an accurate remedy for solving the problem of non-coplanar CBCT reconstruction from limited angle of projections by taking advantage of the combination of PICCS framework and rigid image registration.

TU-A-213CD-05: Single-Scan Scatter Correction in Cone Beam CT Using Stationary Boundary Blockers and Compressed Sensing

Purpose: In this work, a novel scatter correction algorithm is proposed and implemented that couples the use of horizontal boundary stationary beam blockers with a compressed sensing scatter estimation technique to enable simultaneous acquisition of image and scatter information in a single scan. Methods: Two how horizontal boundary beam blockers are designed to simultaneously acquiring the image data in the center unblocked region and the scatter data at the boundary region. Hybrid scatter estimation model that combines the scatterinterpolation model and our proposed scatter convolution model is pre‐computed as the initial scatter estimation for the central region. Based on the low frequency characteristic of scatter signal, compressed sensing optimization penalizes the L1 norm of the discrete cosine transform of scatter signal to reconstruct the scatter map. Estimated scatter is subtracted from the projection data by soft‐thresholding, and the scatter‐correctedimage is obtained by conventional FDK reconstruction algorithm. The proposed method was evaluated with two phantom studies on a Varian TrueBeam STx on‐board imaging system. Results: The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the constructed error to 13 Hounsfield units, 10% ofthat without scatter correction, and increased the image contrast by a factor of two in high‐contrast regions. On the Rando phantom, the spatial nonuniformity decreased from 10.8 % to 6.8% after correction. The proposed method also outperforms the scatterinterpolation correction method in both studies. Conclusions: A novel scatter correction method is proposed and validated in this work. The method enables image acquisition and scatter correction in a single scan, allows for unobstructed volumetric reconstruction, and avoids the additional requirements such as prior images, multiple scans, moving blockers or full‐fan only mode.

TH‐A‐213CD‐02: BEST IN PHYSICS (IMAGING) ‐ The Feasibility of Multiplexed Biomarker Detection Using X‐Ray Stimulated Fluorescence Imaging

Purpose: A feasible way of enhancing diagnosis reliability is to detect multiple biomarkers at the same time via simultaneously using multiple probes to achieve multiplexed molecular imaging. However, no routine approach to directly measure multiple biomarkers currently exists via X‐ray CT, which is the most commonly used imaging modality in the clinic. In this study, we investigated the feasibility of multiplexed biomarker detection using X‐ray stimulated fluorescenceCT (XSF‐CT). Methods: This method utilizes high atomic number (Z) nanoprobes, i.e. Gold(Au),Gadolinium (Gd) and Barium (Ba), that emit XSF photons when excited by ionizing photons. Since the XSF spectrum is unique to each element, this provides a means of detecting the presence of multiple nanoparticles coated with antibodies or peptides that specifically bind to biomarkers found only in tumor. In this study, a polychromatic X‐ray source was used to stimulate emission of XSF photons from the probes. XSF‐CT used a first‐generation CT geometry. The data were collected using a cadmium telluride detector to sort out a set of spectra. The spectra were then used to generate sinogram. The biodistribution and concentration of each element were reconstructed with the ML‐EM algorithm. Results: The acquired XSF spectra showed sharp peaks emitted from Au, Gd and Ba molecules with small FWHM values, which supports the potential of XSF‐CT for multiplexed imaging. The distributions of Au, Gd and Ba molecules in the phantom were clearly identifiable on the reconstructed XSF images. In addition, our results also showed the linear relationships between the XSF intensity of each tested element and their concentrations, respectively. Conclusions: XSF imaging would transform the CT landscape into a multiplexed biomarker detection feature for early detection of cancer and/or imaging the intervention processes in vivo. It could have major predictive value for the clinical outcome in the long term patient care. This work was supported by NIH/NCI grants (CA133474, CA153587), an NSF grant (0854492) and a grant from the Friends for an Earlier Breast Cancer Test Foundation.