Cone-beam CT (CBCT) has become the standard image guidance tool for patient setup in image-guided radiation therapy. However, due to its large illumination field, scattered photons severely degrade its image quality. While kernel-based scatter correction methods have been used routinely in the clinic, it is still desirable to develop Monte Carlo (MC) simulation-based methods due to their accuracy. However, the high computational burden of the MC method has prevented routine clinical application. This paper reports our recent development of a practical method of MC-based scatter estimation and removal for CBCT. In contrast with conventional MC approaches that estimate scatter signals using a scatter-contaminated CBCT image, our method used a planning CT image for MC simulation, which has the advantages of accurate image intensity and absence of image truncation. In our method, the planning CT was first rigidly registered with the CBCT. Scatter signals were then estimated via MC simulation. After scatter signals were removed from the raw CBCT projections, a corrected CBCT image was reconstructed. The entire workflow was implemented on a GPU platform for high computational efficiency. Strategies such as projection denoising, CT image downsampling, and interpolation along the angular direction were employed to further enhance the calculation speed. We studied the impact of key parameters in the workflow on the resulting accuracy and efficiency, based on which the optimal parameter values were determined. Our method was evaluated in numerical simulation, phantom, and real patient cases. In the simulation cases, our method reduced mean HU errors from 44 HU to 3 HU and from 78 HU to 9 HU in the full-fan and the half-fan cases, respectively. In both the phantom and the patient cases, image artifacts caused by scatter, such as ring artifacts around the bowtie area, were reduced. With all the techniques employed, we achieved computation time of less than 30 sec including the time for both the scatter estimation and CBCT reconstruction steps. The efficacy of our method and its high computational efficiency make our method attractive for clinical use.

BackgroundElderly patients are reportedly at higher risk of postoperative cognitive dysfunction (POCD) after inhalational anesthesia with sevoflurane. We hypothesized that the incidence of POCD would be higher in elderly patients undergoing major surgery under inhalational rather than intravenous anesthesia. We also measured plasma S-100β protein concentration as a biomarker of central nervous system injury, and plasma interleukin (IL)-6 and tumor necrosis factor (TNF)-α concentrations to judge the contribution of systemic inflammation to POCD.MethodsNinety patients aged 65–75 years scheduled for resection of an esophageal carcinoma were randomly assigned to one of three groups (n = 30) as follows: a group receiving sevoflurane anesthesia (Group S); a group receiving preoperative methylprednisolone before sevoflurane anesthesia (Group S + MP); and a control group maintained with intravenous propofol (Group C). The mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) were used to measure patients’ cognitive function the day before surgery, and on the first, third and seventh postoperative days. The plasma concentrations of TNF-α, IL-6 and S-100β protein were measured 10 min before anesthesia, and on the first, third and seventh postoperative days.ResultsThere were no significant differences in the demographic or clinical characteristics, or perioperative hemodynamic status, of the three groups. The MMSE and MoCA scores were significantly lower in Group S than in the propofol control (Group C) and Group S + MP on the first, third and seventh postoperative days (P <0.05). Throughout the first postoperative week the plasma concentrations of TNF-α, IL-6, and S-100β protein were significantly elevated in Group S compared with Group C (P <0.05), but were significantly lower in Group S + MP than Group S (P <0.05).ConclusionsThe incidence of POCD was higher in elderly patients undergoing major surgery under inhalational anesthesia with sevoflurane than those maintained on intravenous propofol, and lower in elderly patients pro-treating with methylprednisolone. Furthermore, we found elevated plasma concentrations of S-100β protein, TNF-α and IL-6 in those receiving sevoflurane anesthesia.Trial registrationChiCTR-IOR-15007007 (02-09-2015).

A GPU computational tool, gDRR, has been developed for the accurate and efficient simulations of x-ray projections of CBCT with realistic configurations.

Regeneration after severe spinal cord injury cannot occur naturally in mammals. Transplanting stem cells to the injury site is a highly promising method, but it faces many challenges because it relies heavily on the microenvironment provided by both the lesion site and delivery material. Although mechanical properties, biocompatibility, and biodegradability of delivery materials have been extensively explored, their permeability has rarely been recognized. Here, a DNA hydrogel is designed with extremely high permeability to repair a 2 mm spinal cord gap in Sprague–Dawley rats. The rats recover basic hindlimb function with detectable motor‐evoked potentials, and a renascent neural network is formed via the proliferation and differentiation of both implanted and endogenous stem cells. The signal at the lesion area is conveyed by, on average, 15 newly formed synapses. This hydrogel system offers great potential in clinical trials. Further, it should be easily adaptable to other tissue regeneration applications.

While compressed sensing (CS) based algorithms have been developed for low-dose cone beam CT (CBCT) reconstruction, a clear understanding on the relationship between the image quality and imaging dose at low dose levels is needed. In this paper, we qualitatively investigate this subject in a comprehensive manner with extensive experimental and simulation studies. The basic idea is to plot both the image quality and imaging dose together as functions of number of projections and mAs per projection over the whole clinically relevant range. On this basis, a clear understanding on the tradeoff between image quality and imaging dose can be achieved and optimal low-dose CBCT scan protocols can be developed to maximize the dose reduction while minimizing the image quality loss for various imaging tasks in image guided radiation therapy (IGRT). Main findings of this work include: 1) Under the CS-based reconstruction framework, image quality has little degradation over a large range of dose variation. Image quality degradation becomes evident when the imaging dose (approximated with the x-ray tube load) is decreased below 100 total mAs. An imaging dose lower than 40 total mAs leads to a dramatic image degradation, and thus should be used cautiously. Optimal low-dose CBCT scan protocols likely fall in the dose range of 40–100 total mAs, depending on the specific IGRT applications. 2) Among different scan protocols at a constant low-dose level, the super sparse-view reconstruction with projection number less than 50 is the most challenging case, even with strong regularization. Better image quality can be acquired with low mAs protocols. 3) The optimal scan protocol is the combination of a medium number of projections and a medium level of mAs/view. This is more evident when the dose is around 72.8 total mAs or below and when the ROI is a low-contrast or high-resolution object. Based on our results, the optimal number of projections is around 90 to 120. 4) The clinically acceptable lowest imaging dose level is task dependent. In our study, 72.8mAs is a safe dose level for visualizing low-contrast objects, while 12.2 total mAs is sufficient for detecting high-contrast objects of diameter greater than 3 mm.

Computed tomography (CT) to cone-beam computed tomography (CBCT) deformable image registration (DIR) is a crucial step in adaptive radiation therapy. Current intensity-based registration algorithms, such as demons, may fail in the context of CT-CBCT DIR because of inconsistent intensities between the two modalities. In this paper, we propose a variant of demons, called Deformation with Intensity Simultaneously Corrected (DISC), to deal with CT-CBCT DIR. DISC distinguishes itself from the original demons algorithm by performing an adaptive intensity correction step on the CBCT image at every iteration step of the demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the voxel intensities inside a patch around the voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities. DISC is implemented on computer graphics processing units (GPUs) in compute unified device architecture (CUDA) programming environment. The performance of DISC is evaluated on a simulated patient case and six clinical head-and-neck cancer patient data. It is found that DISC is robust against the CBCT artifacts and intensity inconsistency and significantly improves the registration accuracy when compared with the original demons.

Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principal component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method.

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