Jacob Hinkle - Publications

Abstract: Four-dimensional (4D) respiratory correlated computed tomography (RCCT) has been widely used for studying organ motion. Most current RCCT imaging algorithms use binning techniques that are susceptible to artifacts and challenge the quantitative analysis of organ motion. In this paper, we develop an algorithm for analyzing organ motion which uses the raw, time-stamped imaging data to reconstruct images while simultaneously estimating deformation in the subject's anatomy. This results in reduction of artifacts and facilitates a reduction in dose to the patient during scanning while providing equivalent or better image quality as compared to RCCT. The framework also incorporates fundamental physical properties of organ motion, such as the conservation of local tissue volume. We demonstrate that this approach is accurate and robust against noise and irregular breathing patterns. We present results for a simulated cone beam CT phantom, as well as a detailed real porcine liver phantom study demonstrating accuracy and robustness of the algorithm. An example of applying this algorithm to real patient image data is also presented.

Abstract: State of the art radiation treatment methods such as hypo-fractionated stereotactic body radiation therapy (SBRT) can successfully destroy tumor cells and avoid damaging healthy tissue by delivering high-level radiation dose that precisely conforms to the tumor shape. Though these methods work well for stationary tumors, SBRT dose delivery is particularly susceptible to organ motion, and few techniques capable of resolving and compensating for respiratory-induced organ motion have reached clinical practice. The current treatment pipeline cannot accurately predict nor account for respiratory-induced motion in the abdomen that may result in significant displacement of target lesions during the breathing cycle. Sensitivity of dose deposition to respiratory-induced organ motion represents a significant challenge and may account for observed discrepancies between predictive treatment plan indicators and clinical patient outcomes.
Improved treatment-planning and delivery of SBRT requires an accurate prediction of dose deposition uncertainties resulting from respiratory motion. To accomplish this goal, we developed a framework that models both organ displacement in response to respiration and the underlying random variations in patient-specific breathing patterns. Our organ deformation model is a four-dimensional maximum a posteriori (MAP) estimation of tissue deformation as a function of chest wall amplitudes computed from clinically obtained respiratory-correlated computed tomography (RCCT) images. We characterize patient-specific respiration as the probability density function (PDF) of chest wall amplitudes and model patient breathing patterns as a random process. We then combine the patient-specific organ motion and stochastic breathing models to calculate the resulting variability in radiation dose accumulation. This process allows us to predict uncertainties in dose delivery in the presence of organ motion and identify tissues at risk of receiving insufficient or harmful levels of radiation.

Abstract: The study of diffeomorphism groups is fundamental to computational anatomy, and in particular to image registration. One of the most developed frameworks employs a Riemannian-geometric approach using right-invariant Sobolev metrics. To date, the computation of the Riemannian log and exponential maps on the diffeomorphism group have been defined implicitly via an infinite-dimensional optimization problem. In this paper we the employ Brenier's (1991) polar factorization to decompose a diffeomorphism $h$ as $h(x)=S\circ\psi(x)$, where $\psi=\nabla \rho$ is the gradient of a convex function $\rho$ and $S\in\SDiff(\R^d)$ is a volume-preserving diffeomorphism. We show that all such mappings $\psi$ form a submanifold, which we term $\IDiff(\R^d)$, generated by irrotational flows from the identity. Using the natural metric, the manifold $\IDiff(\R^d)$ is flat. This allows us to calculate the Riemannian log map on this submanifold of diffeomorphisms in closed form, and develop extremely efficient metric-based image registration algorithms. This result has far-reaching implications in terms of the statistical analysis of anatomical variability within the framework of computational anatomy.

Abstract: In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds. The theory enables parametric analysis in a wide range of applications, including rigid and non-rigid kinematics as well as shape change of organs due to growth and aging. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein and the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.

Abstract: Conventional MRI reconstruction techniques are susceptible to artifacts when imaging moving organs. In this paper, a reconstruction algorithm is developed that accommodates respiratory motion instead of using only navigator-gated data. The maximum a posteriori (MAP) algorithm uses the raw k-space time-stamped data and the 1D diaphragm navigator signal to reconstruct the images and estimate deformations in anatomy simultaneously. The algorithm eliminates blurring due to binning the data and increases signal-to-noise ratio (SNR) by using all of the collected data. The algorithm is tested in a simulated torso phantom and is shown to increase image quality by dramatically reducing motion artifacts.

Abstract: Four-dimensional respiratory correlated computed tomography (4D RCCT) has been widely used for studying organ motion. Most current algorithms use binning techniques which introduce artifacts that can seriously hamper quantitative motion analysis. In this paper, we develop an algorithm for tracking organ motion which uses raw time-stamped data and simultaneously reconstructs images and estimates deformations in anatomy. This results in a reduction of artifacts and an increase in signal-to-noise ratio (SNR). In the case of CT, the increased SNR enables a reduction in dose to the patient during scanning. This framework also facilitates the incorporation of fundamental physical properties of organ motion, such as the conservation of local tissue volume. We show in this paper that this approach is accurate and robust against noise and irregular breathing for tracking organ motion. A detailed phantom study is presented, demonstrating accuracy and robustness of the algorithm. An example of applying this algorithm to real patient image data is also presented, demonstrating the utility of the algorithm in reducing artifacts.

Abstract:
Purpose: Deformable image registration has been proven to be useful in tracking organ motion for dose calculation using artifact-free 4D RCCT images. Such methods are challenged in the presence of image artifacts. We present an alternative method which avoids binning artifacts by directly estimating organ deformation during the reconstruction process.

Method and Materials: We have developed a maximum a posteriori (MAP) algorithm for tracking organ motion that uses raw time-stamped data to reconstruct the images and estimate deformations in anatomy simultaneously. Since the algorithm does not rely on a binning process, binning artifacts are avoided. Signal-to-noise ratio (SNR) is also increased since the algorithm uses all of the collected data. The increased SNR provides the opportunity to reduce dose to the patient during scanning. This framework also facilitates the incorporation of fundamental physical properties such as the conservation of local tissue volume during the estimation of organ motion.

In order to validate the accuracy of the 4D reconstruction algorithm, a phantom study was performed using the CIRS anthropomorphic thorax phantom in a CT scanner. An improvement in image quality was also demonstrated by application of the algorithm to data from a real liver stereotactic body radiation therapy (SBRT) patient.

Results: The algorithm accurately estimated the known motion of the anthropomorphic phantom. Additionally, a significant SNR increase was observed when using 4D reconstruction over binning, even for a scan with X-ray tube current reduced to 10%.

Conclusion: A novel method of fully four dimensional CT reconstruction was presented. The geometric accuracy of the estimated deformation was validated in phantom. A marked improvement in image quality was observed when applying the algorithm to image data from a real liver SBRT patient. The method allows reduction of X-ray tube current during scanning while simultaneously improving motion estimates for use in dose calculation.

Abstract: Recent developments in hypo-fractionated Stereotactic Body Radiation Therapy (SBRT) of liver are allowing for the first time the delivery of extremely high doses of highly conformal radiation for the treatment of liver cancer. The precision and dose conformity with which SBRT treatments can be delivered makes the technique particularly susceptible to normal, respiratory-induced motion of both the targeted lesion and also of the surrounding healthy tissues which the method strives to spare. Failure to appropriately accommodate such motion can lead to unacceptable under-dosing of the target and dangerous overdosing of surrounding healthy tissue. Incorporation of motion into the treatment planning process has the potential to reduce these complications.

Recently, technology known as 4D Respiratory Correlated CT (4D RCCT) has been developed which tracks a patient's breathing during CT image acquisition by monitoring a chest marker. Slices are tagged according to the patient's chest wall height, which is used to determine the point in the breathing cycle at which the slice was acquired. These tags are then used to sort slices and create full 3D images which represent the patient's anatomy at various breathing phases.

Using 4D RCCT imaging and deformable image registration techniques, anatomical images from all phases of breathing are brought into correspondence. This enables direct comparison of dose grids computed for different breathing phases during treatment planning. By summing these doses according to the amount of time the patient spends in each breathing phase, the total energy deposited at each point in the body is predicted. This provides an accurate representation of the end result of the treatment in the presence of patient breathing, allowing for more meaningful evaluation of treatment plans and the development of more effective therapies. Results showing the impact of respiratory-induced organ motion on dose calculation will be presented for patients undergoing SBRT treatment at the Huntsman Cancer Institute.

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