Generation of digitally reconstructed radiographs (DRRs) is computationally expensive and is typically the rate-limiting step in the execution time of intensity-based two-dimensional to three-dimensional (2D-3D) registration algorithms. We address this computational issue by extending the technique of light field rendering from the computer graphics community. The extension of light fields, which we call attenuation fields (AFs), allows most of the DRR computation to be performed in a preprocessing step; after this precomputation step, DRRs can be generated substantially faster than with conventional ray casting. We derive expressions for the physical sizes of the two planes of an AF necessary to generate DRRs for a given X-ray camera geometry and all possible object motion within a specified range. Because an AF is a ray-based data structure, it is substantially more memory efficient than a huge table of precomputed DRRs because it eliminates the redundancy of replicated rays. Nonetheless, an AF can require substantial memory, which we address by compressing it using vector quantization. We compare DRRs generated using AFs (AF-DRRs) to those generated using ray casting (RC-DRRs) for a typical C-arm geometry and computed tomography images of several anatomic regions. They are quantitatively very similar: the median peak signal-to-noise ratio of AF-DRRs versus RC-DRRs is greater than 43 dB in all cases. We perform intensity-based 2D-3D registration using AF-DRRs and RC-DRRs and evaluate registration accuracy using gold-standard clinical spine image data from four patients. The registration accuracy and robustness of the two methods is virtually identical whereas the execution speed using AF-DRRs is an order of magnitude faster.

Radiosurgery is a treatment for brain tumours and other lesions which employs a moving beam of photon radiation. In robotic radiosurgery, the radiation source is moved by a six degree-of-freedom robotic arm. A treatment planning system for robotic radiosurgery was developed. This planning system combines collision avoidance techniques and geometric algorithms for finding appropriate robot paths and beam activation profiles. The following design goals characterize our application: 1) ability to generate conformal 3D dose distributions; 2) transparency of the planning process; and 3) full use of the motion flexibility provided by the robotic arm. The new planning system is in clinical use in several leading medical institutions. Results obtained from the studies suggest that highly conformal distributions can be generated in a very effective way.

This paper describes a semi-automatic system for registering and visualizing CT (computer tomography) and MR (magnetic resonance) images. This system has been designed for images of the brain and augmented to include images of the spine. Registration requires identifying similar objects or structures in each image set. This task is complicated because these imaging modalities provide complementary information of the anatomy. Following structure identification, the mapping transformation from one image set to the other can be determined using a nonlinear optimization procedure. The final step is visualization of the results of the registered images sets.>

In radiosurgery a beam of radiation is used as an ablative surgical instrument to destroy brain tumors. Treatment planning consists of computing a sequence of beam configurations for delivering a necrotic dose to the tumor, without damaging healthy tissue or particularly critical structures. In current systems, kinematic limitations severely constrain beam motion. This often results in inappropriate dose distributions. A new radiosurgical system has been implemented to overcome this disadvantage. In this system, a compact radiation source of high energy is moved by a 6-dof robotic arm. We describe algorithms for computing a motion with specified characteristics for this new system. Treatment plans used at test sites with earlier systems are compared to plans computed with the described algorithms. The experience reported shows that full kinematic flexibility combined with treatment planning algorithms allows for better protection of healthy tissue and higher dosage in tumors.>

In radiosurgery a moving beam of radiation is used as an ablative surgical instrument to destroy brain tumors. A new camera-guided system capable of tracking patient motion during treatment has been built. The radiation source is moved by a six degree-of-freedom robotic arm. In addition to offering a more cost effective, less invasive, and less painful treatment, the robotic gantry allows for arbitrary spatial motion of the radiation source. Based on this feature we can treat non-spherical lesions with accuracies unachievable with classical radiosurgical systems. The system introduces a new class of radiosurgical procedures, called non-stereotactic, or image-guided radiosurgery. At the heart of these procedures are algorithms for planning both a treatment and the corresponding beam motion, given the geometric description of the tumor shape and relative locations in the particular case.>

In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data sets with spatial filters so they can be directly matched. The matching is done by a direct optimization technique using a gradient based descent approach and a coarse-to-fine control strategy over a 4D pyramid. We present results on registering the head and spine data by matching 3D edges and results on registering cranial ventricle data by matching images filtered by a Laplacian of a Gaussian.>

The authors present a new in vivo method to correct the nonlinear, object-shape-dependent and material-dependent spatial distortion in magnetic resonance (MR) images caused by magnetic susceptibility variations. This distortion across the air/tissue interface before and after the correction is quantified using a phantom. The results are compared to the distortion-free computed tomography (CT) images of the same phantom by fusing CT and MR images using fiducials, with a registration accuracy of better than a millimeter. The distortion at the bone/tissue boundary is negligible compared to the typical MRI (MR imaging) resolution of 1 mm, while that at the air/tissue boundary creates displacements of about 2 mm (for G(x) 3.13 mT/m). This is a significant value if MRI is to provide highly accurate geometric measurements, as in the case of target localization for stereotaxic surgery. The correction scheme provides MR images with accuracy similar to that of CT: 1 mm. A new method to estimate the magnetic susceptibility of materials from MR images is presented. The magnetic susceptibility of cortical bone is measured using a SQUID magnetometer, and is found to be -8.86 ppm (with respect to air), which is quite similar to that of tissue (-9 ppm).

Stereotaxic radiosurgery is a procedure that uses a beam of radiation as an ablative surgical instrument to destroy brain tumors (or more generally, brain lesions). The beam is produced by a linear accelerator that is moved by a mechanical gantry. Radiation is concentrated by crossfiring at the tumor from multiple directions, to reduce the amount of energy deposited in healthy tissues. Because access to and exit from the tumor is obstructed along some directions by critical structures (e.g., brain stem, optic nerves), planning the path of the beam is often difficult and time-consuming. This paper describes a computer-based planner developed to interactively assist the surgeon generate a satisfactory treatment, given the spatial distribution of the brain tissues obtained with medical imaging. This planner has been experimented on using 11 cases previously processed at the Stanford Medical Center. A dosimetric comparison with the manually generated plans for these cases was carried but. Although still limited, results indicate that automatic planning can significantly improve energy deposition. It can also shorten the overall treatment, hence reducing the patient's pain and allowing the radiosurgery equipment to be used for more patients. Stereotaxic radiosurgery is an example of so-called "bloodless surgery." Computer-based planning techniques are expected to facilitate further development of this safer, less painful, and more cost effective type of surgery.>

A computer-based tool is being developed to assist neurosurgeons in both preoperative planning and surgery. This tool is called the Stereotaxic Planning and Surgery System (SPSS). SPSS utilizes a localization device coupled with two-dimensional and three-dimensional visualization techniques, and it provides useful information to the surgeon when locating and removing diseased tissue. With SPSS, a new surgical technique referred to as guided frameless stereotaxic surgery is also being developed.>