A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cerviño, Laura; Jiang, Steve B; Jia, Xun

doi:10.1118/1.4881326

Cited by 34 publications

(25 citation statements)

References 59 publications

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“…Alternatively, newer reconstruction methods can also be employed (instead of slow gantry rotation speed) to improve the 4DCBCT image quality with standard gantry rotation speed. 37,38 The measured ITVs of the average 4DCBCT matched the average 4DCT closer than the 4DCT-derived ITVs (all 10 phases); by contrast, if individual 4DCBCT phases would be used for ITV segmentation, the percentage deviation with 4DCT would be lesser than what we observed in this study. But, the XVI version used in this investigation did not allow exporting all 10 phases of 4DCBCT.…”

Section: Discussioncontrasting

confidence: 54%

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

Yoganathan¹,

Das²,

Ali³

et al. 2016

BJR

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Section: Discussioncontrasting

confidence: 54%

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

Yoganathan¹,

Das²,

Ali³

et al. 2016

BJR

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“…There are other approaches that have been published in the literature to improve image quality with the most common approaches using iterative reconstruction techniques after the raw data has been acquired, see (Bian et al 2010), (Bergner et al 2009), (Bergner et al 2010), (Leng et al 2008), (Li et al 2007), (Brehm et al 2012), (Brehm et al 2013), and (Mckinnon & Bates 1981) or techniques that attempt to reconstruct 4DCBCT from a one minute 3DCBCT (Yan et al 2014). RMG-4DCBCT can be used in conjunction with these approaches but it has the added benefit that it is the only technique that addresses image quality and imaging dose issues at the source of the problem; i.e., during image acquisition.…”

Section: Discussionmentioning

confidence: 99%

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

O’Brien¹,

Stankovic²,

Sonke³

et al. 2017

Phys. Med. Biol.

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Abstract. Four dimensional cone beam computed tomography (4DCBCT) uses a constant gantry speed and imaging frequency that are independent of the patient's breathing rate. Using a technique called Respiratory Motion Guided 4DCBCT (RMG-4DCBCT), we have previously demonstrated that by varying the gantry speed and imaging frequency, in response to changes in the patient's real-time respiratory signal, the imaging dose can be reduced by 50-70%. RMG-4DCBCT optimally computes a patient specific gantry trajectory to eliminate streaking artefacts and projection clustering that is inherent in 4DCBCT imaging. The gantry trajectory is continuously updated as projection data is acquired and the patient's breathing changes. The aim of this study was to realise RMG-4DCBCT for the first time on a linear accelerator.To change the gantry speed in real-time a potentiometer under microcontroller control was used to adjust the current supplied to an Elekta Synergy's gantry motor. A real-time feedback loop was developed on the microcontroller to modulate the gantry speed and projection acquisition in response to the real-time respiratory signal so that either 40, RMG-4DCBCT 40 , or 60, RMG-4DCBCT 60 , uniformly spaced projections were acquired in 10 phase bins. Images of the CIRS dynamic Thorax phantom were acquired with sinusoidal breathing periods ranging from 2s to 8s together with two breathing traces from lung cancer patients. Image quality was assessed using the contrast to noise ratio (CNR) and edge response width (ERW).For the average patient, with a 3.8s breathing period, the imaging time and image dose were reduced by 37% and 70% respectively. Across all respiratory rates, RMG-4DCBCT 40 had a CNR in the range of 6.5 to 7.5, and RMG-4DCBCT 60 had a CNR between 8.7 and 9.7, indicating that RMG-4DCBCT allows consistent and controllable CNR. In comparison, the CNR for conventional 4DCBCT drops from 20.4 to 6.2 as the breathing rate increases from 2s to 8s. With RMG-4DCBCT, the ERW in the direction of motion of the imaging insert decreases from 2.1mm to 1.1mm as the breathing rate increases from 2s to 8s while for conventional 4DCBCT the ERW increases from 1.9mm to 2.5mm.Image quality can be controlled during 4DCBCT acquisition by varying the gantry speed and the projection acquisition in response to the patient's real-time respiratory signal. However, although the image sharpness, i.e., ERW, is improved with RMG-4DCBCT, the ERW depends on the patient's breathing rate and breathing regularity.

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“…The other one is via the low‐rank model, such as nuclear norm and matrix factorization . The recent developments also involve regularization methods in the motion space, such as deformation vector field, PCA model, and 5D model …”

Section: Introductionmentioning

confidence: 99%

“…10 The other one is via the low-rank model, such as nuclear norm [11][12][13] and matrix factorization. 14 The recent developments also involve regularization methods in the motion space, such as deformation vector field, [15][16][17] PCA model, 18,19 and 5D model. 20 In our previous work, 14 the matrix factorization was utilized for an explicit low-rank representation of 4D images as a product of spatial principal components and temporal motion coefficients.…”

Section: Introductionmentioning

confidence: 99%

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy

et al. 2017

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Purpose: This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. Methods: In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. Results: The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. Conclusion: With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components.

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A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

Cited by 34 publications

References 59 publications

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

Reducing 4DCBCT imaging time and dose: the first implementation of variable gantry speed 4DCBCT on a linear accelerator

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy

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