Tumor phase recognition using cone-beam computed tomography projections and external surrogate information.

PURPOSE: Directly extracting the respiratory phase pattern of the tumor using cone-beam computed tomography (CBCT) projections is challenging due to the poor tumor visibility caused by the obstruction of multiple anatomic structures on the beam's eye view. Predicting tumor phase information using external surrogate also has intrinsic difficulties as the phase patterns between surrogates and tumors are not necessary to be congruent. In this work, we developed an algorithm to accurately recover the primary oscillation components of tumor motion using the combined information from both CBCT projections and external surrogates. METHODS: The algorithm involved two steps. First, a preliminary tumor phase pattern was acquired by applying local principal component analysis (LPCA) on the cropped Amsterdam Shroud (AS) images. In this step, only the cropped image of the tumor region was used to extract the tumor phase pattern in order to minimize the impact of pattern recognition from other anatomic structures. Second, by performing multivariate singular spectrum analysis (MSSA) on the combined information containing both external surrogate signal and the original waveform acquired in the first step, the primary component of the tumor phase oscillation was recovered. For the phantom study, a QUASAR respiratory motion phantom with a removable tumor-simulator insert was employed to acquire CBCT projection images. A comparison between LPCA only and our method was assessed by power spectrum analysis. Also, the motion pattern was simulated under the phase shift or various amplitude conditions to examine the robustness of our method. Finally, anatomic obstruction scenarios were simulated by attaching a heart model, PVC tubes, and RANDO® phantom slabs to the phantom, respectively. Each scenario was tested with five real-patient breathing patterns to mimic real clinical situations. For the patient study, eight patients with various tumor locations were selected. The performance of our method was then evaluated by comparing the reference waveform with the extracted signal for overall phase discrepancy, expiration phase discrepancy, peak, and valley accuracy. RESULTS: In tests of phase shifts and amplitude variations, the overall peak and valley accuracy was -0.009 ± 0.18 sec, and no time delay was found compared to the reference. In anatomical obstruction tests, the extracted signal had 1.6 ± 1.2 % expiration phase discrepancy, -0.12 ± 0.28 sec peak accuracy, and 0.01 ± 0.15 sec valley accuracy. For patient studies, the extracted signal using our method had -1.05 ± 3.0 % overall phase discrepancy, -1.55 ± 1.45% expiration phase discrepancy, 0.04 ± 0.13 sec peak accuracy, and -0.01 ± 0.15 sec valley accuracy, compared to the reference waveforms. CONCLUSIONS: An innovative method capable of accurately recognizing tumor phase information was developed. With the aid of extra information from the external surrogate, an improvement in prediction accuracy, as compared with traditional statistical methods, was obtained. It enables us to employ it as the ground truth for 4D-CBCT reconstruction, gating treatment, and other clinic implementations that require accurate tumor phase information.

Modeling and Optimization of Time-Of-Use Electricity Pricing Systems.

Time-of-Use (TOU) pricing is an important strategy for electricity providers to manage supply and hence making the grid more efficient and for consumers to manage their costs. In this paper, we discuss a general stochastic modeling framework for consumer's power demand based on which the TOU contract characteristics can be selected, so as to minimize the mean electricity price paid by the customer. We exploit the characteristics of power demand observed in real grids to propose to model it during homogeneous peak periods as a constant level with fluctuations described by a scaled fractional Brownian motion. We analyze the exceedance process over pre-specified thresholds and use this information for formulating an optimization problem to determine the key features of the TOU contract. Due to the analytical intractability of certain expressions with the exception of short-range dependence fluctuations, the solution of the posited optimization problem requires using techniques such as Monte Carlo simulation and numerical search. The methodology for two pricing schemes is illustrated using real data.

Inferring Genetic Interactions via a Data-Driven Second Order Model.

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R(3)) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm.