Browsing by Subject "IGRT"

Purpose: Ensuring that tumor motion is within the radiation field for high-dose and high-precision radiosurgery in areas greatly influenced by respiratory motion. Therefore tracking the target or gating the radiation beam by using real-time imaging and surrogate motion monitoring methods are employed. However, these methods cannot be used to depict the effect of respiratory motion on tumor deviation. Therefore, an investigation of parameters for method predicting the tumor motion induced by respiratory motion multiple steps ahead of real time is performed. Currently, algorithms exist to make predictions about future real-time events, however these methods are tedious or unable to predict far enough in advance.

Methods and Materials: The algorithm takes data collected from the Varian RPM$ System, which is a one-dimensional (1D) surrogate signal of amplitude versus time. After the 1D surrogate signal is obtained, the algorithm determines on average what an approximate respiratory cycle is over the entire signal using a rising edge function. The signal is further dividing it into three components: (a) training component is the core portion of the data set which is further divided into subcomponents of length equal to the input component; (b) input component serves as the parameter searched for throughout the training component, (c) analysis component used as a validation against the prediction. The prediction algorithm consists of three major steps: (1) extracting top-ranked subcomponents from training component which best-match the input component; (2) calculating weighting factors from these best-matched subcomponents; (3) collecting the proceeding optimal subcomponent and fusing them with assigned weighting factors to form prediction. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation and root mean square error (RMSE) between prediction and known output.

Results: Respiratory motion data was simulated for 30 cases and 555 patients and phantoms using the RPM system. Simulations were used to optimize prediction algorithm parameters. The simulation cases were used to determine optimal filters for smoothing and number of top-ranked subcomponents to determine optimal subcomponents for prediction. Summed difference results in a value of 0.4770 for the 15 Point Savitzky-Golay filter.

After determining the proper filter for data preprocessing the number of required top-ranked subcomponents for each method was determine. Equal Weighting has a maximum average correlation, c=0.997 when using 1 Subcomponent, Relative Weighting has a maximum average correlation, c=0.997 when using 2 Subcomponents, Pattern Weighting has a maximum average correlation c=0.915 when using 1 subcomponent, Derivative Equal Weighting has a maximum average correlation c=0.976 when using 2 Subcomponents, and Derivative Relative Weighting has a maximum average correlation of c=0.976 when using 5 Subcomponents.

The correlation coefficient and RMSE of prediction versus analysis component distributions demonstrate an improvement during optimization for simulations. This is true for both the full and half cycle prediction. However, when moving to the clinical data the distribution of prediction data, both correlation coefficient and RMSE, there is not an improvement as the optimization occurs. Therefore, a comparison of the clinical data using the 5 Pt moving filter and arbitrarily chosen number of subcomponents was performed. In the clinical data, average correlation coefficient between prediction and analysis component 0.721+/-0.390, 0.727+/-0.383, 0.535+/-0.454, 0.725+/-0.397, and 0.725+/-0.398 for full respiratory cycle prediction and 0.789+/-0.398, 0.800+/-0.385, 0.426+/-0.562, 0.784+/-0.389, and 0.784+/-0.389 for half respiratory cycle prediction for equal weighting, relative weighting, pattern, derivative equal and derivative relative weighting methods, respectively. Additionally, the clinical data average RMSE between prediction and analysis component 0.196+/-0.174, 0.189+/-0.161, 0.302+/-0.162, 0.200+/-0.169, and 0.202+/-0.181 for full respiratory cycle prediction and 0.155+/-0.171, 0.149+/-0.138, 0.528+/-0.179, 0.174+/-0.150, and 0.173+/-0.149 for half respiratory cycle prediction for equal weighting, relative weighting, pattern, derivative equal and derivative relative weighting methods, respectively. The half cycle prediction displays higher accuracy over the full cycle prediction. Wilcoxon signed-rank test reveals statistically highly significant values (p<0.1%) for 4 out of 5 algorithms favoring the half cycle prediction (Equal, Relative, Derivative Equal, and Derivative Relative Weighting Methods). In this method, the relative weighting method has the most correlations coefficients with values greater than 0.9 and also yields the largest number of highest correlations over other prediction methods.

Conclusions: In conclusion, the number of subcomponents used for prediction may be better determined based on individual breathing pattern. The prediction accuracy using patient data is better using half cycle prediction over full cycle prediction for all algorithms for the majority of methods tested. Finally, relative weighting method performed better than other methods.

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Accurate delineation of tumor target and organs at risk (OARs) is critically important in modern radiation therapy for optimal treatment planning and precise treatment delivery. Conventionally, radiotherapy treatment planning uses 2D X-ray or 3D CT images to assist the delineation of tumor volumes and OARs, often under free-breathing condition. In modern radiation therapy, four-dimensional (4D) imaging is the state-of-art technique for imaging respiratory motion of thoracic and abdominal cancers. However, its application for abdominal cancers has been limited due to the low soft-tissue contrast of CT. Recently, a number of 4D magnetic resonance imaging (4D-MRI) techniques have been developed to overcome the limitations of 4D-CT in abdominal cancer applications. In contrast to 4D-CT, 4D-MRI imposes no ionizing radiation dose to the patient and offers superior soft-tissue contrast.

However, current 4D-MRI techniques also have limitations that prevent them from being widely adapted in routine clinical practices. For example, current techniques are all developed based on one particular MR sequence with a particular imaging contrast, which may not be optimal or consistent across patients. Furthermore, current 4D-MRI techniques are often affected by patients’ breathing variations, leading to significant motion artifacts in 4D-MRI images and adversely affects its image quality and thus clinical applicability.

The overall goal of this dissertation is to develop a novel 5D-MRI technique to overcome current limitations of 4D-MRI. The 5D-MRI technique is developed by synergizing three main technical components: multi-source MRI fusion (MSMF), 4D-MRI, and deformable image registration (DIR). 5D-MRI is an extension of 4D-MRI, embedding all the characterizations of 4D-MRI with an additional dimension of “image contrast”. The MSMF method consists of five key components: input MR images, image pre-processing, fusion algorithm, fusion adaptation, and output fused MR images. A linear-weighting fusion algorithm was implemented for MSMF in this study to demonstrate the proof of concept. Fusion options (weighting parameters and image features) are pre-determined with given input MR images and saved in a database for fast fusion adaption. 5D-MRI images were generated by applying the 4D displacement vector fields (4D-DVF) determined from the original 4D-MRI via DIR onto each of the fused MR images.

The 5D-MRI technique was tested on a digital human phantom (XCAT) and a liver tumor patient. 5D-MRI images were qualitatively evaluated for image contrast versatility, and quantitatively evaluated for image quality and motion accuracy. For the latter, motion trajectories of the liver tumor in the superior-inferior (SI) direction were determined from each of the 4D-MRI image sets of 5D-MRI and compared with those tracked on the single-slice sagittal cine MR images. Mean difference in displacement (D) and correlation coefficient (CC) were determined for each comparison.

For both the XCAT phantom and the liver tumor patient, the MSMF method produced more than a large number of fused MR images with versatile image contrasts and improved image quality, each presenting a unique set of anatomical and image features. Among twenty-four liver cancer patients, the average tumor CNR was significantly improved from a range of -1.80 to 8.05 in the four single contrast source MR images, to 22.51 in the fused MR images. The standard deviation of tumor CNR among the 24 patients was 0.87, which suggested high level of consistency. 5D-MRI images clearly demonstrated the respiratory motion during breathing in each 4D-MRI set. Liver tumor motion trajectories measured from 5D-MRI images closely matched with that from the reference cine MR images: D ranged from 0.402 to 0.561 mm (mean=0.463 mm) and CC ranged from 0.980 to 0.987 (mean=0.986).

In this dissertation, beyond the developmental work for the 5D-MRI technique, a novel organ segmentation method, which is an extension of the multi-source MRI fusion technique, and an image mutual-information (MI) based 4D-MRI improvement technique are also presented.

In summary, a novel 5D-MRI technique was developed and its feasibility on digital human phantom and liver tumor patients is demonstrated. Two exploratory projects extending and improving the 5D-MRI technique are also presented. The developed 5D-MRI technique is capable of producing a large number of synthetic 4D-MRI images with versatile image contrasts and improved image quality, holding great promises in enhancing MRI applications for radiation therapy.