Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.
Onboard CBCT for radiation linear accelerators suffers from limited longitudinal coverage and various image quality
problems, especially at wider cone angles. Such problems prevent CBCT being applied in full potentials for many
clinical cancer sites, including head-neck, and for many quantitative applications, including tumor response evaluation
and daily radiation dose computation. We propose to use CBCT with flexible X-ray source trajectories to overcome
these limitations. The core idea is to combine gantry rotation with simultaneous couch motion. Longitudinal coverage
can therefore be extended without limitation. Image quality can be enhanced by applying advanced exact CBCT
reconstruction algorithm. However, unlike diagnostic CT where helical CBCT is widely used, LINAC onboard CBCT
because gantry can only rotate within 360 degrees and couch table cannot move during gantry rotation. To solve the
hardware problem, we program the new Varian TrueBeam LINAC machine in developer mode to realize simultaneous
gantry and couch motion so to simulate any flexible scan trajectories. We also implemented CBCT simulation
algorithms with digital phantoms to support any flexible source trajectories. We implemented and improved Katsevich
exact reconstruction algorithm for image reconstruction from projection data obtained in phantom simulations. We have
studied a few different source trajectory models including double circle, helical and saddle. The initial digital phantom
results were encouraging. The longitudinal coverage was extended. Image quality has been improved using Katsevich
reconstruction algorithm. Physics phantom studies on TrueBeam LINAC machine is our next step.
Medical imaging applications of rigid and non-rigid elastic deformable image registration are undergoing wide scale
development. Our approach determines image deformation maps through a hierarchical process, from global to local
scales. Vemuri (2000) reported a registration method, based on levelset evolution theory, to morph an image along the
motion gradient until it deforms to the reference image. We have applied this level set motion method as basis to
iteratively compute the incremental motion fields and then we approximated the field using a higher-level affine and
non-rigid motion model. In such a way, we combine sequentially the global affine motion, local affine motion and local
non-rigid motion. Our method is fully automated, computationally efficient, and is able to detect large deformations if
used together with multi-grid approaches, potentially yielding greater registration accuracy.