Registration using tomographic projection files

Pre-reconstruction rigid body registration for positron emission tomography: an initial validation against ground truth

Our recent adaptation to PET of the method of Fitchard et al. [1], [2], [3] for rigid body registration of CT sinograms enables motion between two temporal frames of PET data to be estimated and corrected prior to reconstruction. This avoids both the computation required by multiple reconstructions and the need to make choices regarding reconstruction methods that influence the images produced, and potentially change the estimated motion. Using realistic, simulated data with known ground truth, we report an initial investigation into the performance of the method as the number of counts and the accuracy with which the scan is divided into frames, corresponding to different positions, varies.

The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75

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.

History of tomotherapy

Tomotherapy is the delivery of intensity modulated radiation therapy using rotational delivery of a fan beam in the manner of a CT scanner. In helical tomotherapy the couch and gantry are in continuous motion akin to a helical CT scanner. Helical tomotherapy is inherently capable of acquiring CT images of the patient in treatment position and using this information for image guidance. This review documents technological advancements of the field concentrating on the conceptual beginnings through to its first clinical implementation. The history of helical tomotherapy is also a story of technology migration from academic research to a university-industrial partnership, and finally to commercialization and widespread clinical use.

Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm

We have investigated the effect of computed tomography (CT) image reconstruction algorithm on the performance of our automated lung nodule detection method. Commercial CT scanners offer a choice of several algorithms for the reconstruction of projection data into transaxial images. Different algorithms produce images with substantially different properties that are apparent not only quantitatively, but also through visual assessment. During some clinical thoracic CT examinations, patient scans are reconstructed with multiple reconstruction algorithms. Thirty-eight such cases were collected to form two databases: one with patient projection data reconstructed with the "standard" reconstruction algorithm and the other with the same patient projection data reconstructed with the "lung" reconstruction algorithm. The automated nodule detection method was applied to both databases. This method is based on gray-level-thresholding techniques to segment the lung regions from each CT section to create a segmented lung volume. Further gray-level-thresholding techniques are applied within the segmented lung volume to identify a set of lung nodule candidates. Rule-based and linear discriminant classifiers are used to differentiate between lung nodule candidates that correspond to actual nodules and those that correspond to non-nodules. The automated method that was applied to both databases was exactly the same, except that the classifiers were calibrated separately for each database. For comparison, the classifier then was trained on one database and tested independently on the other database. When applied to the databases in this manner, the automated method demonstrated overall a similar level of performance, indicating an encouraging degree of robustness.

Intensity modulated arc deliveries approximated by a large number of fixed gantry position sliding window dynamic multileaf collimator fields

The intensity modulated arc has been proposed as an alternative to tomotherapy. Treatment planing systems more typically model the conventional step and shoot or sliding window dynamic multileaf collimator (DMLC) deliveries, and may not support intensity modulated arc therapy (IMAT). As well, another potential drawback to this technique is that increasing the number of intensity levels required to achieve certain dose distributions necessitates increasing the number of gantry passes, as may occur if the desired dose distribution is complex (e.g., concave or bifurcated), potentially increasing the overall treatment time. A technique is presented here for the delivery of tomotherapy like dose distributions in a single gantry pass by the use of a large number of fields modulated by a sliding window DMLC technique from fixed equally spaced gantry positions. This serves as a good approximation to either IMAT or tomotherapy deliveries. The planning of these fields is achieved using iterative filtered back projection. Measured results of deliveries of varying degrees of complexity on a homogeneous phantom are compared to desired distributions.

A feasible method for clinical delivery verification and dose reconstruction in tomotherapy

Delivery verification is the process in which the energy fluence delivered during a treatment is verified. This verified energy fluence can be used in conjunction with an image in the treatment position to reconstruct the full three-dimensional dose deposited. A method for delivery verification that utilizes a measured database of detector signal is described in this work. This database is a function of two parameters, radiological path-length and detector-to-phantom distance, both of which are computed from a CT image taken at the time of delivery. Such a database was generated and used to perform delivery verification and dose reconstruction. Two experiments were conducted: a simulated prostate delivery on an inhomogeneous abdominal phantom, and a nasopharyngeal delivery on a dog cadaver. For both cases, it was found that the verified fluence and dose results using the database approach agreed very well with those using previously developed and proven techniques. Delivery verification with a measured database and CT image at the time of treatment is an accurate procedure for tomotherapy. The database eliminates the need for any patient-specific, pre- or post-treatment measurements. Moreover, such an approach creates an opportunity for accurate, real-time delivery verification and dose reconstruction given fast image reconstruction and dose computation tools.

On the accuracy and effectiveness of dose reconstruction for tomotherapy

Dose reconstruction is a process that re-creates the treatment-time dose deposited in a patient provided there is knowledge of the delivered energy fluence and the patient's anatomy at the time of treatment. A method for reconstructing dose is presented. The process starts with delivery verification, in which the incident energy fluence from a treatment is computed using the exit detector signal and a transfer matrix to convert the detector signal to energy fluence. With the verified energy fluence and a CT image of the patient in the treatment position, the treatment-time dose distribution is computed using any model-based algorithm such as convolution/superposition or Monte Carlo. The accuracy of dose reconstruction and the ability of the process to reveal delivery errors are presented. Regarding accuracy, a reconstructed dose distribution was compared with a measured film distribution for a simulated breast treatment carried out on a thorax phantom. It was found that the reconstructed dose distribution agreed well with the dose distribution measured using film: the majority of the voxels were within the low and high dose-gradient tolerances of 3% and 3 mm respectively. Concerning delivery errors, it was found that errors associated with the accelerator, the multileaf collimator and patient positioning might be detected in the verified energy fluence and are readily apparent in the reconstructed dose. For the cases in which errors appear in the reconstructed dose, the possibility for adaptive radiotherapy is discussed.

Megavoltage CT imaging as a by-product of multileaf collimator leakage

In addition to their potential for the delivery of highly conformal radiation therapy treatments, tomotherapeutic treatments also feature increased potential for verification. For example, megavoltage CT allows one to use the megavoltage linac to generate tomographic images of the patient in the treatment position. This is typically done before or after radiation therapy treatments. However, it is also possible to collect MVCT images entirely during the treatment itself. This process utilizes the leakage radiation through the closed leaves of the Nomos MIMiC MLC, along with slight inefficiencies in treatment delivery, to generate MVCT images during treatment that require neither additional time nor dose. The image quality is limited, yet sufficient to see a patient's external boundary, density differences over 8% for 25.0 mm objects and resolutions of 3.0 mm for high-contrast objects. Such images can potentially be viewed during treatment, used to flag additional CT immediately after the treatment and provide a representation of the patient's exact position during treatment for use with dose reconstruction.

Megavoltage CT on a tomotherapy system

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.

Image/patient registration from (partial) projection data by the Fourier phase matching method

A technique for 2D or 3D image/patient registration, PFPM (projection based Fourier phase matching method), is proposed. This technique provides image/patient registration directly from sequential tomographic projection data. The method can also deal with image files by generating 2D Radon transforms slice by slice. The registration in projection space is done by calculating a Fourier invariant (FI) descriptor for each one-dimensional projection datum, and then registering the FI descriptor by the Fourier phase matching (FPM) method. The algorithm has been tested on both synthetic and experimental data. When dealing with translated, rotated and uniformly scaled 2D image registration, the performance of the PFPM method is comparable to that of the IFPM (image based Fourier phase matching) method in robustness, efficiency, insensitivity to the offset between images, and registration time. The advantages of the former are that subpixel resolution is feasible, and it is more insensitive to image noise due to the averaging effect of the projection acquisition. Furthermore, the PFPM method offers the ability to generalize to 3D image/patient registration and to register partial projection data. By applying patient registration directly from tomographic projection data, image reconstruction is not needed in the therapy set-up verification, thus reducing computational time and artefacts. In addition, real time registration is feasible. Registration from partial projection data meets the geometry and dose requirements in many application cases and makes dynamic set-up verification possible in tomotherapy.

Delivery verification in sequential and helical tomotherapy

Conformal and conformal avoidance radiation therapy are new therapeutic techniques that are generally characterized by high dose gradients. The success of this kind of treatment relies on quality assurance procedures in order to verify the delivery of the treatment. A delivery verification technique should consider quality assurance procedures for patient positioning and radiation delivery verification. A methodology for radiation delivery verification was developed and tested with our tomotherapy workbench. The procedure was investigated for two cases. The first treatment using a torus-shaped target was optimized for 72 beam directions and sequentially delivered as a single slice to a 33 cm diameter cylinder of homogeneous solid water. For the second treatment, a random pattern of energy fluence was helically delivered for two slices to a 9.0 cm diameter phantom containing inhomogeneities. The presented process provides the energy fluence (or a related quantity) delivered through the multileaf collimator (MLC) using the signal measured at the exit detector during the treatment delivery. As this information is created for every pulse of the accelerator, the energy fluence and state for each MLC leaf were verified on a pulse-by-pulse basis. The pulse-by-pulse results were averaged to obtain projection-by-projection information to allow for a comparison with the planned delivery. The errors between the planned and delivered energy fluences were concentrated between +/-2.0%, with none beyond +/-3.5%. In addition to accurately achieving radiation delivery verification, the process is fast, which could translate to radiation delivery verification in real time. This technique can also be extended to reconstruct the dose actually deposited in the patient or phantom (dose reconstruction).