Abstract:Portable and transportable instrumentation complexes based on pulsed x-ray generators with more stringent requirements for the spatio-temporal structure of the radiation field are needed for effective fast analysis of the state of the components of main pipelines. Specifically, aside from having small dimensions (maximum linear size ≤0.5 m), the radiation source must provide at distance 0.5 m from the target an exposure dose rate of at least ~0.1 mSv/sec with minimal area of the emitting surface of the target … Show more
“…Lowering the mAs level to reduce the patient radiation dose can easily be done on existing commercial CT scanners, while the other method proposed in our paper, namely reducing the number of projections to reduce the dose, is not straightforward on currently available commercial CT scanners due to the use of continuous x-ray generation mode. However, technically it is possible to modify the scanners to operate in high-frequency pulsed mode (Kang et al 2010, Myagkov et al 2009, Yue et al 2002 if there is a clinical need (such as the one suggested in this paper).…”
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.
“…Lowering the mAs level to reduce the patient radiation dose can easily be done on existing commercial CT scanners, while the other method proposed in our paper, namely reducing the number of projections to reduce the dose, is not straightforward on currently available commercial CT scanners due to the use of continuous x-ray generation mode. However, technically it is possible to modify the scanners to operate in high-frequency pulsed mode (Kang et al 2010, Myagkov et al 2009, Yue et al 2002 if there is a clinical need (such as the one suggested in this paper).…”
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.
“…Our idea of the TNLM function is inspired by the so called Non-local Means (NLM) method 16 originated in image processing † Decreasing the number of projections is not straightforward on currently available commercial CT scanners due to the use of continuous x-ray generation mode. However, technically it is possible to modify the scanners to operate in high-frequency pulsed mode [13][14][15] , if there is a clinical need (such as the one suggested in this paper).…”
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.
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