High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original total variation norm. During the reconstruction process, the pixels at edges would be gradually identified and given small penalty weight. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low contrast structures and therefore maintain acceptable spatial resolution.
A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning.
Two D-A-type molecules, 4-N-[4-(9-phenylcarbazole)]-3,5-bis(4-diphenylamine)phenyl-4H-1,2,4-triazole and 4,4'-(9-(4-(1-phenyl-1H-phenanthro[9,10-d]imidazol-2-yl)phenyl)-9H-carbazole-3,6-diyl) bis-(N,N-diphenylaniline), are designed and synthesized. Organic lightemitting diodes based on them exhibit deep-blue emission and the singlet formation ratios are higher than the simple spin-statistics of 25%. A triplet-polaroninteraction-induced upconversion from triplet to singlet through a one-electron transfer mechanism is proposed, and is proven by magnetocurrent measurement and quantum-chemistry computation.
Purpose: Four-dimensional cone beam computed tomography (4D-CBCT) has been developed to provide respiratory phase-resolved volumetric imaging in image guided radiation therapy. Conventionally, it is reconstructed by first sorting the x-ray projections into multiple respiratory phase bins according to a breathing signal extracted either from the projection images or some external surrogates, and then reconstructing a 3D CBCT image in each phase bin independently using FDK algorithm. This method requires adequate number of projections for each phase, which can be achieved using a low gantry rotation or multiple gantry rotations. Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. 4D-CBCT images at different breathing phases share a lot of redundant information, because they represent the same anatomy captured at slightly different temporal points. Taking this redundancy along the temporal dimension into account can in principle facilitate the reconstruction in the situation of inadequate number of projection images. In this work, the authors propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. Methods: The authors define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms implementation on GPU is designed to avoid redundant and uncoalesced memory access, in order to ensure a high computational efficiency. Our algorithms have been tested on a digital NURBS-based cardiac-torso phantom and a clinical patient case. Results: The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56-3.13 and our enhancement method increases the CNR by 2.75-3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is 509-683 s for the reconstruction algorithm and 524-540 s for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card. Conclusions: By innovatively taking the temporal redundancy among 4D-CBCT images into consideration, the proposed algorithms can produce high quality 4D-CBCT images with much less streak artifacts than the FDK results, in the situa...
Purpose Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI‐CT registration errors. We developed a deep attention‐based segmentation strategy on CT‐based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. Methods and materials We developed a prostate segmentation strategy which employs an sMRI‐aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave‐one‐out experiments and validated with an additional 50 patients by hold‐out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed MRI‐defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave‐one‐out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold‐out test. Conclusions We have proposed a novel CT‐only prostate segmentation strategy using CT‐based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.
We investigate the photophysical property for 1,1,2,3,4,5-hexaphenylsilole (HPS) through combined quantum mechanical and molecular mechanical (QM/MM) simulations. Under the displaced harmonic oscillator approximation with consideration of the Duschinsky rotation effect (DRE), the radiative and nonradiative rates of the excited-state decay processes for HPS are calculated by using the analytical vibration correlation function approach coupled with first-principles calculations. The intermolecular packing effect is incorporated through electrostatic interaction modeled by a force field. We find that from the gas phase to the solid state (i) the side phenyl ring at the 5-position becomes coplanar with the central silacycle, which increases the degree of conjugation, thus accelerating the radiative decay process, and (ii) the rotation of the side phenyl ring at the 2-position is restricted, which blocks the excited-state nonradiative decay channels. Such a synergetic effect largely enhances the solid-state luminescence quantum efficiency through reducing the nonradiative decay rate by about 4 orders of magnitude, leading to the radiative decay overwhelming the nonradiatvie decay. In addition, the calculated solid-phase absorption and emission optical spectra of HPS are found to be in agreement with the experiment.
Purpose: Four-dimensional computed tomography (4DCT) has been widely 25 used in cancer radiotherapy for accurate target delineation and motion measurement for tumors in thorax and upper abdomen areas. However, its prolonged scanning duration causes a considerably increase of radiation dose compared with the conventional CT, which is a major concern in its clinical application. This work is to develop a new algorithm to reconstruct 4DCT 30 images from undersampled projections acquired at low mAs levels in order to reduce the imaging dose. Methods: Conventionally, each phase of 4DCT is reconstructed independently using the filtered backprojection (FBP) algorithm. The basic idea of our new algorithm is that, by utilizing the common information among different phases, 35 the input information required to reconstruct image of high quality, and thus the imaging dose, can be reduced. We proposed a temporal non-local means (TNLM) method to explore the inter-phase similarity. All phases of the 4DCT images are reconstructed simultaneously by minimizing a cost function consisting of a data fidelity term and a TNLM regularization term. We utilized a modified forward-40 † Zhen Tian and Xun Jia have contributed equally to this work and should be considered co-first authors. a) Author to whom correspondence should be addressed. Electronic mail: sbjiang@ucsd.edu 2 Z. Tian et al. 2backward splitting algorithm and a Gauss-Jacobi iteration method to efficiently solve the minimization problem. The algorithm was also implemented on graphics processing unit (GPU) to improve the computational speed. Our reconstruction algorithm has been tested on a digital NCAT thorax phantom in three low dose scenarios: all projections with low mAs level, undersampled 45 projections with high mAs level and undersampled projections with low mAs level.Results: In all three low dose scenarios, our new algorithm generates visually much better CT images containing less image noise and streaking artifacts compared with the standard FBP algorithm. Quantitative analysis shows that, by 50 comparing our TNLM algorithm with the standard FBP algorithm, the contrastto-noise ratio has been improved by a factor of 3.9-10.2 and the signal-to-noise ratio has been improved by a factor of 2.1-5.9, depending on the cases. In the situation of undersampled projection data, the majority of the streaks in the images reconstructed by FBP can be suppressed using our algorithm. The total 55 reconstruction time for all 10 phases of a slice ranges from 40 to 90 seconds on an NVIDIA Tesla C1060 GPU card. Conclusions:The experimental results indicate that our new algorithm outperforms the conventional FBP algorithm in effectively reducing the image artifacts due to undersampling and suppressing the image noise due to the low 60 mAs level.
4D computed tomography (4D-CT) is an important modality in medical imaging due to its ability to resolve patient anatomy motion in each respiratory phase. Conventionally 4D-CT is accomplished by performing the reconstruction for each phase independently as in a CT reconstruction problem. We propose a new 4D-CT reconstruction algorithm that explicitly takes into account the temporal regularization in a non-local fashion. By imposing a regularization of a temporal non-local means (TNLM) form, 4D-CT images at all phases can be reconstructed simultaneously based on extremely under-sampled x-ray projections. Our algorithm is validated in one digital NCAT thorax phantom and two real patient cases. It is found that our TNLM algorithm is capable of reconstructing the 4D-CT images with great accuracy. The experiments also show that our approach outperforms standard 4D-CT reconstruction methods with spatial regularization of total variation or tight frames.
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