Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model

Zhang, Ruoqiao; Thibault, Jean‐Baptiste; Bouman, Charles A.; Sauer, Ken D.; Hsieh, Jiang

doi:10.1109/tmi.2013.2282370

Cited by 101 publications

(48 citation statements)

References 56 publications

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“…Methods in the post-log category can leverage existing pre-correction steps used by FBP reconstruction, and the numerical optimization of the WLS cost function is relatively straightforward. Post-log statistical models are widely encountered in CT MBIR literature and have been implemented with various optimization methods [14]–[20], regularization methods [21]–[23], applied to dual-energy CT [24], [25], interior tomography [26], and combined with data-driven prior information [27]–[29]. …”

Section: Introductionmentioning

confidence: 99%

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Lee

Kim

et al. 2017

IEEE Trans. Med. Imaging

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X-ray detectors in clinical computed tomography (CT) usually operate in current-integrating mode. Their complicated signal statistics often lead to intractable likelihood functions for practical use in model-based image reconstruction (MBIR). It is therefore desirable to design simplified statistical models without losing the essential factors. Depending on whether the CT transmission data are logarithmically transformed, pre-log and post-log models are two major categories of choices in CT MBIR. Both being approximations, it remains an open question whether one model can notably improve image quality over the other on real scanners. In this study, we develop and compare several pre-log and post-log MBIR algorithms under a unified framework. Their reconstruction accuracy based on simulation and clinical datasets are evaluated. The results show that pre-log MBIR can achieve notably better quantitative accuracy than post-log MBIR in ultra-low-dose CT, although in less extreme cases, post-log MBIR with handcrafted pre-processing remains a competitive alternative. Pre-log MBIR could play a growing role in emerging ultra-low-dose CT applications.

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Section: Introductionmentioning

confidence: 99%

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Lee

Kim

et al. 2017

IEEE Trans. Med. Imaging

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“…Up to now, many considerable efforts to suppress noise-induced artifacts in DECT images and material-decomposed images have been reported (Rutherford et al 1976, Kalender et al 1988, Warp et al 2003, Leng et al 2011, Zeng et al 2016a, Niu et al 2014, Clark et al 2014, Dong et al 2014, Sukovic et al 2000, Petrongolo et al 2015, Zhang et al 2014, Long et al 2014, Zhang et al 2016a, Zhang et al 2016b, Szczykutowicz et al 2011, Liu et al 2016). Among them, projection or image domain denoising approaches were proposed to improve low-dose DECT images quality (Rutherford et al 1976, Kalender et al 1988, Warp et al 2003, Leng et al 2011, Zeng et al 2016a, Niu et al 2014, Clark et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Although these approaches can suppress the noise to some extent, they often result in spatial resolution loss because the noise in DECT images does not obey a uniform distribution. By better modeling the projection data and the image geometry in the DECT imaging, statistical iterative reconstruction (SIR) algorithms have shown to be more robust than FBP algorithm in regard to the presence of noise-induced artifacts (Dong et al 2014, Sukovic et al 2000, Petrongolo et al 2015, Zhang et al 2014, Long et al 2014, Zhang et al 2016a, Szczykutowicz et al 2011, Liu et al 2016). Based on the maximum a posterior (MAP) estimation criteria, the SIR algorithms can be mathematically formulated with a cost function.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, designing effective regularization term to reﬂect the image priors is at the core of DECT images reconstruction. One classic family of the regularization term is based on total variation (TV) model, which does take into consideration the information that the DECT images are blocky and discontinuous and measure the discontinuities in the objective data (Dong et al 2014, Zhang et al 2016a, Szczykutowicz et al 2011). These regularizations are generally defined to work in a small local neighborhood.…”

Section: Introductionmentioning

confidence: 99%

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Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

et al. 2017

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Reducing radiation dose in dual energy computed tomography (DECT) is highly desirable but it may lead to excessive noise in the filtered backprojection (FBP) reconstructed DECT images, which can inevitably increase the diagnostic uncertainty. To obtain clinically acceptable DECT images from low-mAs acquisitions, in this work we develop a novel scheme based on measurement of DECT data. In this scheme, inspired by the success of edge-preserving non-local means (NLM) filtering in CT imaging and the intrinsic characteristics underlying DECT images, i.e., global correlation and non-local similarity, an averaged image induced NLM-based (aviNLM) regularization is incorporated into the penalized weighted least-squares (PWLS) framework. Specifically, the presented NLM-based regularization is designed by averaging the acquired DECT images, which takes the image similarity within the two energies into consideration. In addition, the weighted least-squares term takes into account DECT data-dependent variance. For simplicity, the presented scheme was termed as “PWLS-aviNLM”. The performance of the presented PWLS-aviNLM algorithm was validated and evaluated on digital phantom, physical phantom and patient data. The extensive experiments validated that the presented PWLS-aviNLM algorithm outperforms the FBP, PWLS-TV and PWLS-NLM algorithms quantitatively. More importantly, it delivers the best qualitative results with the finest details and the fewest noise-induced artifacts, due to the aviNLM regularization learned fromDECT images. This study demonstrated the feasibility and efficacy of the presented PWLS-aviNLM algorithm to improve the DECT reconstruction and resulting material decomposition.

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“…An alternate approach to improving reconstruction quality is to use more advanced model-based iterative reconstruction (MBIR) methods, which are based on the estimation of a reconstruction which best fits models of both the sensor measurements (i.e., the forward model) and the object (i.e., prior model) [5], [28]- [30]. These 3D reconstruction methods have been shown to be very effective when the angular range is limited [31] and also when the number of views is less than that required by Nyquist sampling criterion [5].…”

mentioning

confidence: 99%

TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

Mohan

Venkatakrishnan

Gibbs

et al. 2015

IEEE Trans. Comput. Imaging

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Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement non-idealities also tend to introduce ring and streak artifacts into the 4D reconstructions.In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition which distributes the view angles more evenly in time. The second innovation is a 4D model-based iterative reconstruction algorithm (MBIR) which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement non-idealities. We present reconstructions of both simulated and real X-ray synchrotron data which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.

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Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model

Cited by 101 publications

References 56 publications

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Comparison Between Pre-Log and Post-Log Statistical Models in Ultra-Low-Dose CT Reconstruction

Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

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