Radiographic image guidance has emerged as the new paradigm for patient positioning, target localization, and external beam alignment in radiotherapy. Although widely varied in modality and method, all radiographic guidance techniques have one thing in common--they can give a significant radiation dose to the patient. As with all medical uses of ionizing radiation, the general view is that this exposure should be carefully managed. The philosophy for dose management adopted by the diagnostic imaging community is summarized by the acronym ALARA, i.e., as low as reasonably achievable. But unlike the general situation with diagnostic imaging and image-guided surgery, image-guided radiotherapy (IGRT) adds the imaging dose to an already high level of therapeutic radiation. There is furthermore an interplay between increased imaging and improved therapeutic dose conformity that suggests the possibility of optimizing rather than simply minimizing the imaging dose. For this reason, the management of imaging dose during radiotherapy is a different problem than its management during routine diagnostic or image-guided surgical procedures. The imaging dose received as part of a radiotherapy treatment has long been regarded as negligible and thus has been quantified in a fairly loose manner. On the other hand, radiation oncologists examine the therapy dose distribution in minute detail. The introduction of more intensive imaging procedures for IGRT now obligates the clinician to evaluate therapeutic and imaging doses in a more balanced manner. This task group is charged with addressing the issue of radiation dose delivered via image guidance techniques during radiotherapy. The group has developed this charge into three objectives: (1) Compile an overview of image-guidance techniques and their associated radiation dose levels, to provide the clinician using a particular set of image guidance techniques with enough data to estimate the total diagnostic dose for a specific treatment scenario, (2) identify ways to reduce the total imaging dose without sacrificing essential imaging information, and (3) recommend optimization strategies to trade off imaging dose with improvements in therapeutic dose delivery. The end goal is to enable the design of image guidance regimens that are as effective and efficient as possible.

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In this paper, we present a fully automatic, fast and accurate deformable registration technique. This technique deals with free-form deformation. It minimizes an energy functional that combines both similarity and smoothness measures. By using calculus of variations, the minimization problem was represented as a set of nonlinear elliptic partial differential equations (PDEs). A Gauss-Seidel finite difference scheme is used to iteratively solve the PDE. The registration is refined by a multi-resolution approach. The whole process is fully automatic. It takes less than 3 min to register two three-dimensional (3D) image sets of size 256 x 256 x 61 using a single 933 MHz personal computer. Extensive experiments are presented. These experiments include simulations, phantom studies and clinical image studies. Experimental results show that our model and algorithm are suited for registration of temporal images of a deformable body. The registration of inspiration and expiration phases of the lung images shows that the method is able to deal with large deformations. When applied to the daily CT images of a prostate patient, the results show that registration based on iterative refinement of displacement field is appropriate to describe the local deformations in the prostate and the rectum. Similarity measures improved significantly after the registration. The target application of this paper is for radiotherapy treatment planning and evaluation that incorporates internal organ deformation throughout the course of radiation therapy. The registration method could also be equally applied in diagnostic radiology.

A megavoltage computed tomography (MVCT) system was developed on the University of Wisconsin tomotherapy benchtop. This system can operate either axially or helically, and collect transmission data without any bounds on delivered dose. Scan times as low as 12 s per slice are possible, and scans were run with linac output rates of 100 MU min(-1), although the system can be tuned to deliver arbitrarily low dose rates. Images were reconstructed with clinically reasonable doses ranging from 8 to 12 cGy. These images delineate contrasts below 2% and resolutions of 3.0 mm. Thus, the MVCT image quality of this system should be sufficient for verifying the patient's position and anatomy prior to radiotherapy. Additionally, synthetic data were used to test the potential for improved MVCT contrast using maximum-likelihood (ML) reconstruction. Specifically, the maximum-likelihood expectation-maximization (ML-EM) algorithm and a transmission ML algorithm were compared with filtered backprojection (FBP). It was found that for expected clinical MVCT doses enough imaging photons are used such that little benefit is conferred by the improved noise model of ML algorithms. For significantly lower doses, some quantitative improvement is achieved through ML reconstruction. Nonetheless, the image quality at those lower doses is not satisfactory for radiotherapy verification.

Tomotherapy is delivery of intensity-modulated, rotational radiation therapy using a fan-beam delivery. The NOMOS (Sewickley, PA) Peacock system is an example of sequential (or serial) tomotherapy that uses a fast-moving, actuator-driven multileaf collimator attached to a conventional C-arm gantry to modulate the beam intensity. In helical tomotherapy, the patient is continuously translated through a ring gantry as the fan beam rotates. The beam delivery geometry is similar to that of helical computed tomography (CT) and requires the use of slip rings to transmit power and data. A ring gantry provides a stable and accurate platform to perform tomographic verification using an unmodulated megavoltage beam. Moreover, megavoltage tomograms have adequate tissue contrast and resolution to provide setup verification. Assuming only translational and rotational offset errors, it is also possible to determine the offsets directly from tomographic projections, avoiding the time-consuming image reconstruction operation. The offsets can be used to modify the leaf delivery pattern to match the beam to the patient's anatomy on each day of a course of treatment. If tomographic representations of the patient are generated, this information can also be used to perform dose reconstruction. In this way, the actual dose distribution delivered can be superimposed onto the tomographic representation of the patient obtained at the time of treatment. The results can be compared with the planned isodose on the planning CT. This comparison may be used as an accurate basis for adaptive radiotherapy whereby the optimized delivery is modified before subsequent fractions. The verification afforded tomotherapy allows more precise conformal therapy. It also enables conformal avoidance radiotherapy, the complement to conformal therapy, for cases in which the tumor volume is ill-defined, but the locations of sensitive structures are adequately determined. A clinical tomotherapy unit is under construction at the University of Wisconsin.