Low-dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior

Adaptive prior image constrained total generalized variation for low-dose dynamic cerebral perfusion CT reconstruction

BACKGROUND

Dynamic cerebral perfusion CT (DCPCT) can provide valuable insight into cerebral hemodynamics by visualizing changes in blood within the brain. However, the associated high radiation dose of the standard DCPCT scanning protocol has been a great concern for the patient and radiation physics. Minimizing the x-ray exposure to patients has been a major effort in the DCPCT examination. A simple and cost-effective approach to achieve low-dose DCPCT imaging is to lower the x-ray tube current in data acquisition. However, the image quality of low-dose DCPCT will be degraded because of the excessive quantum noise.

OBJECTIVE

To obtain high-quality DCPCT images, we present a statistical iterative reconstruction (SIR) algorithm based on penalized weighted least squares (PWLS) using adaptive prior image constrained total generalized variation (APICTGV) regularization (PWLS-APICTGV).

METHODS

APICTGV regularization uses the precontrast scanned high-quality CT image as an adaptive structural prior for low-dose PWLS reconstruction. Thus, the image quality of low-dose DCPCT is improved while essential features of targe image are well preserved. An alternating optimization algorithm is developed to solve the cost function of the PWLS-APICTGV reconstruction.

RESULTS

PWLS-APICTGV algorithm was evaluated using a digital brain perfusion phantom and patient data. Compared to other competing algorithms, the PWLS-APICTGV algorithm shows better noise reduction and structural details preservation. Furthermore, the PWLS-APICTGV algorithm can generate more accurate cerebral blood flow (CBF) map than that of other reconstruction methods.

CONCLUSIONS

PWLS-APICTGV algorithm can significantly suppress noise while preserving the important features of the reconstructed DCPCT image, thus achieving a great improvement in low-dose DCPCT imaging.

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Motion compensation combining with local low rank regularization for low dose dynamic CT myocardial perfusion reconstruction

Dynamic CT myocardial perfusion imaging (DCT-MPI) is a reliable examination tool for the assessment of myocardium and vascular, while its special scan protocol may result in excessive radiation exposure to patients and inevitable inter-frame motion. Lowering the tube current is a simple way to reduce radiation exposure. However, low mAs will certainly cause severe image noise, thus may further impact the accuracy of functional hemodynamic parameters, which are used for the assessment of blood supply. In this work, we present a novel scheme applying motion compensation and local low rank regularization (MC-LLR) for obtaining high quality motion compensated DCT-MPI images. Specifically, motion compensation by using robust data decomposition registration (RDDR) was introduced. Robust principal component analysis coupled with optical flow-based registration algorithm were used in RDDR. Then, the local low rank constraint on the motion compensated time series images was applied for the DCT-MPI reconstruction. One healthy mini pig and two patient datasets were used to evaluate the proposed MC-LLR algorithm. Results show that the present method achieved satisfactory image quality with higher CNRs, smaller rRMSEs, and more accurate hemodynamic parameter maps.

Learning non-local perfusion textures for high-quality computed tomography perfusion imaging

Background. Computed tomography perfusion (CTP) imaging plays a critical role in the acute stroke syndrome assessment due to its widespread availability, speed of image acquisition, and relatively low cost. However, due to its repeated scanning protocol, CTP imaging involves a substantial radiation dose, which might increase potential cancer risks.Methods. In this work, we present a novel deep learning model called non-local perfusion texture learning network (NPTN) for high-quality CTP imaging at low-dose cases. Specifically, considering abundant similarities in the CTP images, i.e. latent self-similarities within the non-local region in the CTP images, we firstly search the most similar pixels from the adjacent frames within a fixed search window to obtain the non-local similarities and to construct non-local textures vector. Then, both the low-dose frame and these non-local textures from adjacent frames are fed into a convolution neural network to predict high-quality CTP images, which can help better characterize the structure details and contrast variants in the targeted CTP image rather than simply utilizing the targeted frame itself. The residual learning strategy and batch normalization are utilized to boost the performance of the convolution neural network. In the experiment, the CTP images of 31 patients with suspected stroke disease are collected to demonstrate the performance of the presented NPTN method.Results. The results show the presented NPTN method obtains superior performance compared with the competing methods. From numerical value, at all dose levels, the presented NPTN method has achieved around 3.0 dB improvement of average PSNR, an increase of around 1.4% of average SSIM, and a decrease of around 4.8% of average RMSE in the low-dose CTP reconstruction task, and also has achieved an increase of around 3.4% of average SSIM and a decrease of around 61.1% of average RMSE in the cerebral blood flow (CBF) estimation task.Conclusions. The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image and outperform the competing methods at low-dose cases.

Temporal feature prior-aided separated reconstruction method for low-dose dynamic myocardial perfusion computed tomography

Dynamic myocardial perfusion computed tomography (DMP-CT) is an effective medical imaging technique for coronary artery disease diagnosis and therapy guidance. However, the radiation dose received by the patient during repeated CT scans is a widespread concern of radiologists because of the increased risk of cancer. The sparse few-view CT scanning protocol can be a feasible approach to reduce the radiation dose of DMP-CT imaging; however, an advanced reconstruction algorithm is needed. In this paper, a temporal feature prior-aided separated reconstruction method (TFP-SR) for low-dose DMP-CT images reconstruction from sparse few-view sinograms is proposed. To implement the proposed method, the objective perfusion image is divided into the baseline fraction and the enhancement fraction introduced by the arrival of the contrast agent. The core of the proposed TFP-SR method is the utilization of the temporal evolution information that naturally exists in the DMP-CT image sequence to aid the enhancement image reconstruction from limited data. The temporal feature vector of an image pixel is defined by the intensities of this pixel in the pre-reconstructed enhancement sequence, and the connection between two related features is calculated via a zero-mean Gaussian function. A prior matrix is constructed based on the connections between the extracted temporal features and used in the iterative reconstruction of the enhancement images. To evaluate the proposed method, the conventional filtered back-projection algorithm, the total variation regularized PWLS (PWLS-TV) and the prior image constrained compressed sensing are compared in this paper based on studies on a digital extended cardiac-torso (XCAT) thoracic phantom and a preclinical porcine DMP-CT data set that take image misregistration into account. The experimental results demonstrate that the proposed TFP-SR method has superior performance in sparse DMP-CT images reconstruction in terms of image quality and the analyses of the time attenuation curve and hemodynamic parameters.

An iterative image reconstruction algorithm combined with forward and backward diffusion filtering for in-line X-ray phase-contrast computed tomography

In-line X-ray phase-contrast computed tomography (IL-PCCT) can reveal fine inner structures for low-Z materials (e.g. biological soft tissues), and shows high potential to become clinically applicable. Typically, IL-PCCT utilizes filtered back-projection (FBP) as the standard reconstruction algorithm. However, the FBP algorithm requires a large amount of projection data, and subsequently a large radiation dose is needed to reconstruct a high-quality image, which hampers its clinical application in IL-PCCT. In this study, an iterative reconstruction algorithm for IL-PCCT was proposed by combining the simultaneous algebraic reconstruction technique (SART) with eight-neighbour forward and backward (FAB8) diffusion filtering, and the reconstruction was performed using the Shepp-Logan phantom simulation and a real synchrotron IL-PCCT experiment. The results showed that the proposed algorithm was able to produce high-quality computed tomography images from few-view projections while improving the convergence rate of the computed tomography reconstruction, indicating that the proposed algorithm is an effective method of dose reduction for IL-PCCT.

[Redundancy information-induced image reconstruction for low-dose myocardial perfusion computed tomography]

OBJECTIVE

In the clinic, myocardial perfusion computed tomography (MPCT) imaging is commonly used to detect and assess myocardial ischemia quantitatively. However, repeated scanning on the myocardial region in the cine mode will increase the radiation dose for patients. With lowering radiation dose, the quality of images are degraded by noise induced artifact, which hampers the diagnostic accuracy. Therefore, in this paper, we propose a redundancy information induced iterative reconstruction framework for high quality MPCT images at the case of low dose.

METHODS

MPCT images have redundant structural information within frames and highly similarity between adjacent frames. Inspired by the two properties, in this work we propose a penalized weighted least-squares (PWLS) model incorporating NLM and TV based hybrid constraints, which is referred to as PWLS-aviNLM-TV for simplicity. The proposed algorithm can effectively eliminate noise and artifacts by taking into account the similarity between adjacent frames and redundancy information within frames, which also can improve spatial resolution within frames and maintain temporal resolution.

RESULTS

The experimental results on the 4D extended cardiac-torso (XCAT) phantom and preclinical porcine dataset demonstrates that the PWLS-aviNLM-TV algorithm obtains better performance in terms of noise reduction and artifacts suppression than the PWLS-TV and PWLSaviNLM algorithm. Moreover, the proposed algorithm can preserve the edges and detail information thereby efficiently differentiate ischemia from myocardium.

CONCLUSIONS

The present redundancy information induced reconstruction algorithm can reconstruct high-quality images from low-dose MPCT for better clinical imaging diagnosis.