SU-E-J-112: A Splatting Method to Generate DRRs for Deformed CT Volume

PURPOSE

To develop an efficient algorithm for generating high-quality digitally reconstructed radiographs (DRRs) for regularly and irregularly sampled volumes based on a splatting method with dynamic elliptical Gaussian kernels, and to evaluate this method using ray tracing.

METHODS

The traditional ray tracing method, which takes every intersected voxel into account, produces high quality DRRs but is very time consuming. Additionally, it is not suitable for handling irregularly-sampled volumes since it always requires image re-sampling, which leads to inaccuracy. We present a splatting approach to compute the 'footprint function', facilitating efficient perspective projection of elliptical Gaussian kernels at very low cost. This reported framework allows dealing with both regularly and irregularly sampled volumes effectively and efficiently. An XCAT digital phantom was used to generate 3D chest volumes at different respiratory phases, and CT projections are generated using ray tracing and the splatting method, respectively. Normalized cross correlation (NCC) is applied to evaluate the DRR similarity of two methods.

RESULTS

Respiratory Phases one and four are used as volume datasets from the 4D XCAT digital phantom as they represent the lung at end inhale and end exhale, respectively. The dataset of Phase one is the basic regularly-sampled volume while the dataset of Phase four is deformed from that of Phase one, and resampled to a regularly sampled dataset for the ray tracing method. NCC between splatting and ray tracing DRRs are 0.9980 and 0.9977 for Phase one and Phase four, respectively. The calculation speed of the splatting method is 3 times faster than that of ray tracing.

CONCLUSIONS

Our splatting approach can generate high-quality DRRs efficiently, and is a good alternative for current DRR generation techniques for deformed volume datasets. This research is supported by CPRIT Individual Investigator Award RP110329.

SU-E-J-127: An Initial Application of Evaluating Lung Tumor Motion Throughout Radiotherapy

PURPOSE

We present a novel method for evaluating lung tumor motion incorporating planning CT scan, imaging for patient localization, and during treatment delivery.

METHODS

Tumor motion is evaluated at three stages of the treatment process. Following the acquisition of 4D CT scans for treatment planning, tumors are contoured at one phase and registered to other phases to obtain tumor motion patterns. Tumor motion prior to each treatment is evaluated by identifying tumors directly on every raw projection of the daily localization CBCT scans. Finally, tumor motion during treatment is evaluated by analyzing MV treatment beam images. Every frame of image is evaluated as three components, each with separate DRRs: all non-moving structures, the tumor, and other moving structures. The locations of three components are determined though a registration process. In order to quantify tumor motion, the cumulated probability, the percentage of time when the tumor motion is equal to or smaller than a given range, is evaluated at every stage.

RESULTS

This methodology has been applied to five lung cancer patients undergoing radiotherapy. More than 5400 frames from MV treatment beam images and 24 CBCT scans were acquired from 12 fractions. Both the average tumor position and the cumulated probability with the range were evaluated. The tumor motion ranges are generally larger than those obtained from 4D CT. Significant variation in motion patterns was observed as well.

CONCLUSIONS

Information obtained from 4DCT is insufficient for motion assessment. In contrast, our novel methodology is capable of accurately characterizing lung tumor motion on a daily basis, without the need for implanted fiducial markers and without additional imaging dose. It provides daily verification that the tumor range is within prior estimates and covered by the treatment planning volume. This research is supported by CPRIT Individual Investigator Award RP110329.

Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions

Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agent’s causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.

Paroxysmal kinesigenic choreoathetosis: evidence of linkage to the pericentromeric region of chromosome 16 in four Chinese families

BACKGROUND

Paroxysmal kinesigenic choreoathetosis (PKC) is an autosomal dominant condition characterized by abnormal involuntary movements precipitated by sudden movement. The pericentromeric region of chromosome 16 has been linked to PKC by several reports. This study was to localize and identify PKC gene in four Chinese PKC families.

METHODS

Genetic linkage mapping with eight markers spanning chromosome 16p12-q13 was performed in 43 family members. Genome-wide single nucleotide polymorphism (SNP) scans were performed on four individuals in Family 1 in which infantile convulsion (IC) was co-inherited with PKC.

RESULTS

Individuals in Family 1 presented with both IC and paroxysmal choreoathetosis (ICCA), and Families 2, 3, and 4 presented only with PKC. Evidence for linkage was found with a maximum two-point LOD score of 4.89 for D16S690 (theta = 0.0) and a maximum multipoint LOD score was 5.34 between D16S3080 and D16S3136. Haplotype analysis showed the disease locus was between D16S3093 and D16S3057. A total of 84 SNPs spanned on 16q12.1-q13 was not segregated with the PKC phenotype, which defined an unlinked region from rs9933187 to rs8044753. Thus, the critical region of the PKC gene is across the pericentromeric region of chromosome 16, and most likely maps to a region of 20.5 Mb (6.2 cM) between D16S3093 and rs9933187 (16p11.2-q12.1).

CONCLUSION

The assignment of the locus for PKC to the pericentromeric region of chromosome 16 is confirmed and putatively narrowed in the present study.

U-shape association between white blood cell count and the risk of diabetes in young Chinese adults

BACKGROUND

Chronic low-grade inflammation is related to diabetes risk in population studies. Elevated levels of white blood cells (WBC) were related to the risk of diabetes in cross-sectional studies in the Chinese population. The objective of the study is to assess the prospective association between WBC and the risk of diabetes in the Chinese population.

METHODS

We examined 7445 manual workers aged 18-59 years free from diabetes at baseline. Fasting glucose concentrations and white cell count were measured at annual health examinations from 1997 to 2007. Anthropometric measurements were taken by health workers. In the present study, each participant had at least two measurements of fasting blood glucose.

RESULTS

During a mean of 4.94 years follow-up, 178 participants developed diabetes. After controlling for known risk factors for diabetes (age, gender, smoking, drinking, parental history of diabetes, body mass index, systolic blood pressure, hepatitis B surface antigen and liver function), a U-shaped association between WBC count and diabetes was found. The hazard ratios (HR) of diabetes across quartiles of WBC count were 1.87 (95% CI 1.15-3.05), 1.00, 1.46 (0.88-2.42) and 2.04 (1.28-3.25). The association was stronger among non-smokers: compared with the second quartile, the HR of diabetes for the first and fourth quartiles of WBC were 3.00 (1.28-7.03) and 3.16 (1.33-7.53). Adjusting for hepatitis B virus infection and liver function did not change the association.

CONCLUSION

Both low and high levels of WBC count were associated with an increased risk of diabetes in young workers.