Investigation of a phase-only correlation technique for anatomical alignment of portal images in radiation therapy

Phase correlation applied to the 3D registration of CT and CBCT image volumes

PURPOSE

In this study, a 3D phase correlation algorithm was investigated to test feasibility for use in determining the anatomical changes that occur throughout a patient's radiotherapy treatment. The algorithm determines the transformations between two image volumes through analysis in the Fourier domain and has not previously been used in radiotherapy for 3D registration of CT and CBCT volumes.

METHODS

Various known transformations were applied to a patient's prostate CT image volume to create 12 different test cases. The mean absolute error and standard deviation were determined by evaluating the difference between the known contours and those calculated from the registration process on a point-by-point basis. Similar evaluations were performed on images with increasing levels of noise added. The improvement in structure overlap offered by the algorithm in registering clinical CBCT to CT images was evaluated using the Dice Similarity Coefficient (DSC).

RESULTS

A mean error of 2.35 (σ = 1.54) mm was calculated for the 12 deformations applied. When increasing levels of noise were introduced to the images, the mean errors were observed to rise up to a maximum increase of 1.77 mm. For CBCT to CT registration, maximum improvements in the DSC of 0.09 and 0.46 were observed for the bladder and rectum, respectively.

CONCLUSIONS

The Fourier-based 3D phase correlation registration algorithm investigated displayed promising results in CT to CT and CT to CBCT registration, offers potential in terms of efficiency and robustness to noise, and is suitable for use in radiotherapy for monitoring patient anatomy throughout treatment.

Automated 2D-3D registration of portal images and CT data using line-segment enhancement

In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage (MV) portal images acquired during treatment to a reference 3D kilovoltage (kV) CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on (1) accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior (AP) view acquired at 15 different poses and (2) a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean (and standard deviation) of the absolute registration error for rotations around the right-lateral (RL), inferior-superior (IS), and posterior-anterior (PA) axes were 0.212 degree (0.214 degree), 0.055 degree (0.033 degree) and 0.041 degree (0.039 degree), respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161 (0.131) mm, 0.096 (0.033) mm, and 0.612 (0.485) mm. The mean (and standard deviation) of the total registration error was 0.778 (0.543) mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.

ESTERR-PRO: a setup verification software system using electronic portal imaging

The purpose of the paper is to present and evaluate the performance of a new software-based registration system for patient setup verification, during radiotherapy, using electronic portal images. The estimation of setup errors, using the proposed system, can be accomplished by means of two alternate registration methods. (a) The portal image of the current fraction of the treatment is registered directly with the reference image (digitally reconstructed radiograph (DRR) or simulator image) using a modified manual technique. (b) The portal image of the current fraction of the treatment is registered with the portal image of the first fraction of the treatment (reference portal image) by applying a nearly automated technique based on self-organizing maps, whereas the reference portal has already been registered with a DRR or a simulator image. The proposed system was tested on phantom data and on data from six patients. The root mean square error (RMSE) of the setup estimates was 0.8 ± 0.3 (mean value ± standard deviation) for the phantom data and 0.3 ± 0.3 for the patient data, respectively, by applying the two methodologies. Furthermore, statistical analysis by means of the Wilcoxon nonparametric signed test showed that the results that were obtained by the two methods did not differ significantly (P value > 0.05).

Automated 2D-3D registration of a radiograph and a cone beam CT using line-segment enhancement

The objective of this study was to develop a fully automated two-dimensional (2D)-three-dimensional (3D) registration framework to quantify setup deviations in prostate radiation therapy from cone beam CT (CBCT) data and a single AP radiograph. A kilovoltage CBCT image and kilovoltage AP radiograph of an anthropomorphic phantom of the pelvis were acquired at 14 accurately known positions. The shifts in the phantom position were subsequently estimated by registering digitally reconstructed radiographs (DRRs) from the 3D CBCT scan to the AP radiographs through the correlation of enhanced linear image features mainly representing bony ridges. Linear features were enhanced by filtering the images with "sticks," short line segments which are varied in orientation to achieve the maximum projection value at every pixel in the image. The mean (and standard deviations) of the absolute errors in estimating translations along the three orthogonal axes in millimeters were 0.134 (0.096) AP(out-of-plane), 0.021 (0.023) ML and 0.020 (0.020) SI. The corresponding errors for rotations in degrees were 0.011 (0.009) AP, 0.029 (0.016) ML (out-of-plane), and 0.030 (0.028) SI (out-of-plane). Preliminary results with megavoltage patient data have also been reported. The results suggest that it may be possible to enhance anatomic features that are common to DRRs from a CBCT image and a single AP radiography of the pelvis for use in a completely automated and accurate 2D-3D registration framework for setup verification in prostate radiotherapy. This technique is theoretically applicable to other rigid bony structures such as the cranial vault or skull base and piecewise rigid structures such as the spine.

Spectral phase based medical image processing

RATIONALE AND OBJECTIVES

To exploit the spectral phase characteristics of digital or digitized mammograms for early detection of microcalcifications, shape, and sizes of suspected lesions and to demonstrate its use for training radiologists to discriminate signal features in different spatially varying backgrounds.

MATERIALS AND METHODS

We propose two algorithms: in the phase-only image (POI) reconstruction algorithm the spectral phase of the digital mammogram is extracted from its Fourier spectrum. This is coupled with unit magnitude and inverse Fourier transformed to reconstruct the POI thus enhancing the features of interest such as microcalcifications, shape, and sizes of suspected lesions. In the algorithm for image reconstruction from a priori phase-only information, spectral phase is used to extract signal features of the digital mammogram and then this is combined with spectral magnitude that is extracted and averaged over an ensemble of unrelated digital mammograms.

RESULTS

The results for several digital phantoms and mammograms show that POI reconstructs only high spatial frequencies related to the features such as microcalcifications, shape, and size of masses like cysts and tumors. The results on image reconstruction from a priori phase-only information demonstrate the changes in the visibility of signal features when buried in a wide variety of real world mammogram backgrounds with different densities.

CONCLUSION

The POI can aid radiologists in early detection of microcalcifications, lesions, and other masses of interest in digital mammograms. This reconstruction method is self-adaptive to changes in the background. The image reconstruction from a priori phase-only information can help the radiologist as a training tool in his decision-making process. Preliminary experiments indicate the potential of the techniques for early diagnosis of breast cancer. Clinical studies on these algorithm procedures are in progress for application as a diagnostic CAD tool in digital mammography. These methods can in general be applied to other medical images such as CT and MRI images.

Feasibility study of beam orientation class-solutions for prostate IMRT

IMRT is being increasingly used for treatment of prostate cancer. In practice, however, the beam orientations used for the treatments are still selected empirically, without any guideline. The purpose of this work was to investigate interpatient variation of the optimal beam configuration and to facilitate intensity modulated radiation therapy (IMRT) prostate treatment planning by proposing a set of beam orientation class-solutions for a range of numbers of incident beams. We used fifteen prostate cases to generate the beam orientation class-solutions. For each patient and a given number of incident beams, a multiobjective optimization engine was employed to provide optimal beam directions. For the fifteen cases considered, the gantry angle of any of the optimized plans were all distributed within a certain range The angular distributions of the optimal beams were analyzed and the most selected directions are identified as optimal directions. The optimal directions for all patients are averaged to obtain the class-solution. The class-solution gantry angles for prostate IMRT were found to be: three beams (0 degrees, 120 degrees, 240 degrees), five beams (35 degrees, 110 degrees, 180 degrees, 250 degrees, 325 degrees), six beams (0 degrees, 60 degrees, 120 degrees, 180 degrees, 240 degrees, 300 degrees), seven beams (25 degrees, 75 degrees, 130 degrees, 180 degrees, 230 degrees, 285 degrees, 335 degrees), eight beams (20 degrees, 70 degrees, 110 degrees, 150 degrees, 200 degrees, 250 degrees, 290 degrees, 340 degrees), and nine beams (20 degrees, 60 degrees, 100 degrees, 140 degrees, 180 degrees, 220 degrees, 260 degrees, 300 degrees, 340 degrees). The level of validity of the class-solutions was tested using an additional clinical prostate case by comparing with the individually optimized beam configurations. The difference between the plans obtained with class-solutions and patient-specific optimizations was found to be clinically insignificant.

Registration using tomographic projection files

An algorithm has been developed and experimentally verified for tomographic registration--a patient positioning method using internal anatomy and standard external fiducial marks. This algorithm improves patient set-up and verification to an accuracy sufficient for tomotherapy. By implementation of this technique, the time-consuming reconstruction process is avoided. Instead, offsets in the x, y and z directions are determined directly from sinogram data by an algorithm that utilizes cross-correlations and Fourier transforms. To verify the efficiency and stability of the algorithm, data were collected on the University of Wisconsin's dedicated tomotherapy research workbench. The experiment indicates offset statistical errors of less than +/-0.8 mm for offsets up to 30 mm. With standard clinical techniques, initial patient offsets are expected to be less than 5 mm, so the 30 mm limitation is of no consequence. The angular resolution for the direction of patient translation is within the +/-2 degrees needed for tomotherapy.

Registration of synthetic tomographic projection data sets using cross-correlation

Tomographic registration, a method that makes possible accurate patient registration directly from projection data, consists of three processing steps: (i) manual coarse positioning, (ii) tomographic projection set acquisition, and (iii) computer mediated refined positioning. In the coarse positioning stage, the degree of patient alignment is comparable with that achieved with the standard radiotherapy set-up. However, the accuracy requirements are somewhat more relaxed in that meticulous alignment of the patient using external laser indicators is not necessary. Instead, tomographic projection sets are compared with planning CTs in order to achieve improved patient set-up. The projection sets are cross-correlated to obtain the best-fit translation and rotation offsets. The algorithm has been tested on synthetic data with the incorporation of varying amounts of Gaussian pseudo-random noise. These tests demonstrate the algorithm's stability and also confirm that alignment can be achieved with an accuracy of less than one projection pixel.

A simple algorithm for planar image registration in radiation therapy

A simple algorithm is presented for planar image registration and the method is applied to the simulator and portal image registration for patient setup verification in radiation therapy. Basically, the algorithm follows the concept proposed by Balter et al. [Med. Phys. 19, 329-334 (1992)], which converts the problem of open curve registration into matching a series of points along the curves. Balter's algorithm consists of three steps: (1) to determine a common starting point for each curve pair, (2) acquire two corresponding point sets along each curve, and (3) obtain a global transform matrix by matching two point sets. We integrate all three steps into one simple procedure which fits the sampled points along the intended curve pair by taking the relative path length shift as an independent fitting parameter. After being modified, the algorithm is able to take the different magnification factors of images into account, and it avoids curvature calculations. Numerical simulation as well as clinical and phantom images have been utilized to test the accuracy of the algorithm. The typical errors are less than 1 mm in translation and 1 degree in rotation. We also made a comparison study with the chamfer method. The results of the two methods agree to within 0.5 mm in translation and 0.5 degree in rotation.

A survey of medical image registration

The purpose of this paper is to present a survey of recent (published in 1993 or later) publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods. The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved.