An image correlation procedure for digitally reconstructed radiographs and electronic portal images

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy

PURPOSE

Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation.

METHODS

Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset.

RESULTS

Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection.

CONCLUSION

DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

Clinical implementation of automated treatment planning for whole-brain radiotherapy

The purpose of the study was to develop and clinically deploy an automated, deep learning‐based approach to treatment planning for whole‐brain radiotherapy (WBRT). We collected CT images and radiotherapy treatment plans to automate a beam aperture definition from 520 patients who received WBRT. These patients were split into training (n = 312), cross‐validation (n = 104), and test (n = 104) sets which were used to train and evaluate a deep learning model. The DeepLabV3+ architecture was trained to automatically define the beam apertures on lateral‐opposed fields using digitally reconstructed radiographs (DRRs).

For the beam aperture evaluation, 1st quantitative analysis was completed using a test set before clinical deployment and 2nd quantitative analysis was conducted 90 days after clinical deployment. The mean surface distance and the Hausdorff distances were compared in the anterior‐inferior edge between the clinically used and the predicted fields. Clinically used plans and deep‐learning generated plans were evaluated by various dose–volume histogram metrics of brain, cribriform plate, and lens.

The 1st quantitative analysis showed that the average mean surface distance and Hausdorff distance were 7.1 mm (±3.8 mm) and 11.2 mm (±5.2 mm), respectively, in the anterior–inferior edge of the field. The retrospective dosimetric comparison showed that brain dose coverage (D99%, D95%, D1%) of the automatically generated plans was 29.7, 30.3, and 32.5 Gy, respectively, and the average dose of both lenses was up to 19.0% lower when compared to the clinically used plans. Following the clinical deployment, the 2nd quantitative analysis showed that the average mean surface distance and Hausdorff distance between the predicted and clinically used fields were 2.6 mm (±3.2 mm) and 4.5 mm (±5.6 mm), respectively.

In conclusion, the automated patient‐specific treatment planning solution for WBRT was implemented in our clinic. The predicted fields appeared consistent with clinically used fields and the predicted plans were dosimetrically comparable.

DRRGenerator: A Three-dimensional Slicer Extension for the Rapid and Easy Development of Digitally Reconstructed Radiographs

As the interest in image-guided medical interventions has increased, so too has the necessity for open-source software tools to provide the required capabilities without exorbitant costs. A common issue encountered in these procedures is the need to compare computed tomography (CT) data with X-ray data, for example, to compare pre-operative CT imaging with intraoperative X-rays. A software approach to solve this dilemma is the production of digitally reconstructed radiographs (DRRs) which computationally simulate an X-ray-type image from CT data. The resultant image can be easily compared to an X-ray image and can provide valuable clinical information, such as small anatomical changes that have occurred between the pre-operative and operative imaging (i.e., vertebral positioning). To provide an easy way for clinicians to make their own DRRs, we propose DRR generator, a customizable extension for the open-source medical imaging application three-dimensional (3D) Slicer. DRR generator provides rapid computation of DRRs through a highly customizable user interface. This extension provides end-users a free, open-source, and reliable way of generating DRRs. This program is integrated within 3D Slicer and thus can utilize its powerful imaging tools to provide a comprehensive segmentation and registration application for clinicians and researchers. DRR generator is available for download through 3D Slicer’s in-app extension manager and requires no additional software.

Definition of internal target volumes based on planar X-ray fluoroscopic images for lung and hepatic stereotactic body radiation therapy. Comparison to inhale/exhale CT technique

PURPOSE

To compare tumor motion amplitudes measured with 2D fluoroscopic images (FI) and with an inhale/exhale CT (IECT) technique MATERIALS AND METHODS: Tumor motion of 52 patients (39 lung patients and 13 liver patients) was obtained with both FI and IECT. For FI, tumor detection and tracking was performed by means of a software developed by the authors. Motion amplitude and, thus, internal target volume (ITV), were defined to cover the positions where the tumor spends 95% of the time. The algorithm was validated against two different respiratory motion phantoms. Motion amplitude in IECT was defined as the difference in the position of the centroid of the gross tumor volume in the image sets of both treatments.

RESULTS

Important differences exist when defining ITVs with FI and IECT. Overall, differences larger than 5 mm were obtained for 49%, 31%, and 9.6% of the patients in Superior-Inferior (SI), Anterior-Posterior (AP), and Lateral (LAT) directions, respectively. For tumor location, larger differences were found for tumors in the liver (73.6% SI, 27.3% AP, and 6.7% in LAT had differences larger than 5 mm), while tumors in the upper lobe benefitted less using FI (differences larger than 5 mm were only present in 27.6% (SI), 36.7% (AP), and 0% (LAT) of the patients).

CONCLUSIONS

Use of FI with the linac built-in CBCT system is feasible for ITV definition. Large differences between motion amplitudes detected with FI and IECT methods were found. The method presented in this work based on FI could represent an improvement in ITV definition compared to the method based on IECT due to FI permits tumor motion acquisition in a more realistic situation than IECT.

A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain

During aging the brain undergoes a series of structural changes, in size, shape as well as tissue composition. In particular, cortical atrophy and ventricular enlargement are often present in the brain of elderly individuals. This poses serious challenges in the spatial registration of structural MR images. In this study, we addressed this open issue by proposing an enhanced framework for MR registration and segmentation. Our solution was compared with other approaches based on the tools available in SPM12, a widely used software package. Performance of the different methods was assessed on 229 T1-weighted images collected in healthy individuals, with age ranging between 55 and 90 years old. Our method showed a consistent improvement as compared to other solutions, especially for subjects with enlarged lateral ventricles. It also provided a superior inter-subject alignment in cortical regions, with the most marked improvement in the frontal lobe. We conclude that our method is a valid alternative to standard approaches based on SPM12, and is particularly suitable for the processing of structural MR images of brains with cortical atrophy and ventricular enlargement. The method is integrated in our software toolbox MRTool, which is freely available to the scientific community.

BMD-based assessment of local porosity in human femoral cortical bone

Cortical pores are determinants of the elastic properties and of the ultimate strength of bone tissue. An increase of the overall cortical porosity (Ct.Po) as well as the local coalescence of large pores cause an impairment of the mechanical competence of bone. Therefore, Ct.Po represents a relevant target for identifying patients with high fracture risk. However, given their small size, the in vivo imaging of cortical pores remains challenging. The advent of modern high-resolution peripheral quantitative computed tomography (HR-pQCT) triggered new methods for the clinical assessment of Ct.Po at the peripheral skeleton, either by pore segmentation or by exploiting local bone mineral density (BMD). In this work, we compared BMD-based Ct.Po estimates with high-resolution reference values measured by scanning acoustic microscopy. A calibration rule to estimate local Ct.Po from BMD as assessed by HR-pQCT was derived experimentally. Within areas of interest smaller than 0.5 mm², our model was able to estimate the local Ct.Po with an error of 3.4%. The incorporation of the BMD inhomogeneity and of one parameter from the BMD distribution of the entire scan volume led to a relative reduction of the estimate error of 30%, if compared to an estimate based on the average BMD. When applied to the assessment of Ct.Po within entire cortical bone cross-sections, the proposed BMD-based method had better accuracy than measurements performed with a conventional threshold-based approach.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

A novel method to quantify and compare anatomical shape: application in cervix cancer radiotherapy

Adaptive radiation therapy (ART) had been proposed to restore dosimetric deficiencies during treatment delivery. In this paper, we developed a technique of Geometric reLocation for analyzing anatomical OBjects' Evolution (GLOBE) for a numerical model of tumor evolution under radiation therapy and characterized geometric changes of the target using GLOBE. A total of 174 clinical target volumes (CTVs) obtained from 32 cervical cancer patients were analyzed. GLOBE consists of three main steps; step (1) deforming a 3D surface object to a sphere by parametric active contour (PAC), step (2) sampling a deformed PAC on 642 nodes of icosahedron geodesic dome for reference frame, and step (3) unfolding 3D data to 2D plane for convenient visualization and analysis. The performance was evaluated with respect to (1) convergence of deformation (iteration number and computation time) and (2) accuracy of deformation (residual deformation). Based on deformation vectors from planning CTV to weekly CTVs, target specific (TS) margins were calculated on each sampled node of GLOBE and the systematic (Σ) and random (σ) variations of the vectors were calculated. Population based anisotropic (PBA) margins were generated using van Herk's margin recipe. GLOBE successfully modeled 152 CTVs from 28 patients. Fast convergence was observed for most cases (137/152) with the iteration number of 65 ± 74 (average ± STD) and the computation time of 13.7 ± 18.6 min. Residual deformation of PAC was 0.9 ± 0.7 mm and more than 97% was less than 3 mm. Margin analysis showed random nature of TS-margin. As a consequence, PBA-margins perform similarly to ISO-margins. For example, PBA-margins for 90% patients' coverage with 95% dose level is close to 13 mm ISO-margins in the aspect of target coverage and OAR sparing. GLOBE demonstrates a systematic analysis of tumor motion and deformation of patients with cervix cancer during radiation therapy and numerical modeling of PBA-margin on 642 locations of CTV surface.

INTEGRATING MRI AND MRSI INFORMATION INTO TRUS-GUIDED ROBOTIC PROSTATE BIOPSY

The development of prostate biopsy robotics can make biopsies both automatic and accurate. However, intervention from urologists is still needed to define the location of biopsy cores. With the aid of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging (MRSI) diagnosis information obtained pre-operationally, it is possible to guide the biopsy needle towards those sites where cancer is suspected, thereby achieving higher detection rates. In this paper, a deformable image registration method is presented for the purpose of merging MRI/MRSI and transrectal ultrasound (TRUS) images. Given the poor quality of ultrasound (US) images and the deformation occurring across modalites, a thin-plate spline transformation is used to match the prostate surfaces and thereafter their volumes. A deformable prostate phantom that simulates the condition in humans was also set up for validation purposes. Fifteen fiducial markers were implanted inside the phantom prostate to act as the reference of "ground truth." The phantom study shows that our method can achieve an accuracy around 1.28 ± 0.50 mm, with voxel dimensions of 0.5 × 0.5 × 0.5 mm3. This result is promising since none of the knowledge about the interior prostate is utilized in the algorithm. Experimental results on patient data are also presented.

A feasibility study of a molecular-based patient setup verification method using a parallel-plane PET system

A feasibility study of a novel PET-based molecular image guided radiation therapy (m-IGRT) system was conducted by comparing PET-based digitally reconstructed planar image (PDRI) registration with radiographic registration. We selected a pair of opposing parallel-plane PET systems for the practical implementation of this system. Planar images along the in-plane and cross-plane directions were reconstructed from the parallel-plane PET data. The in-plane and cross-plane FWHM of the profile of 2 mm diameter sources was approximately 1.8 and 8.1 mm, respectively. Therefore, only the reconstructed in-plane image from the parallel-plane PET data was used in the PDRI registration. In the image registration, five different sizes of (18)F cylindrical sources (diameter: 8, 12, 16, 24, 32 mm) were used to determine setup errors. The data acquisition times were 1, 3 and 5 min. Image registration was performed by five observers to determine the setup errors from PDRI registration and radiographic registration. The majority of the mean registration errors obtained from the PDRI registration were not significantly different from those obtained from the radiographic registration. Acquisition time did not appear to result in significant differences in the mean registration error. The mean registration error for the PDRI registration was found to be 0.93 ± 0.33 mm. This is not statistically different from the radiographic registration which had a mean registration error of 0.92 ± 0.27 mm. Our results suggest that m-IGRT image registration using PET-based reconstructed planar images along the in-plane direction is feasible for clinical use if PDRI registration is performed at two orthogonal gantry angles.

Detection of patient setup errors with a portal image - DRR registration software application

The purpose of this study was to evaluate a custom portal image — digitally reconstructed radiograph (DRR) registration software application. The software works by transforming the portal image into the coordinate space of the DRR image using three control points placed on each image by the user, and displaying the fused image. In order to test statistically that the software actually improves setup error estimation, an intra‐ and interobserver phantom study was performed. Portal images of anthropomorphic thoracic and pelvis phantoms with virtually placed irradiation fields at known setup errors were prepared. A group of five doctors was first asked to estimate the setup errors by examining the portal and DRR image side‐by‐side, not using the software. A second group of four technicians then estimated the same set of images using the registration software. These two groups of human subjects were then compared with an auto‐registration feature of the software, which is based on the mutual information between the portal and DRR images. For the thoracic case, the average distance between the actual setup error and the estimated error was 4.3±3.0 mm for doctors using the side‐by‐side method, 2.1±2.4 mm for technicians using the registration method, and 0.8±0.4 mm for the automatic algorithm. For the pelvis case, the average distance between the actual setup error and estimated error was 2.0±0.5 mm for the doctors using the side‐by‐side method, 2.5±0.4 mm for technicians using the registration method, and 2.0±1.0 mm for the automatic algorithm. The ability of humans to estimate offset values improved statistically using our software for the chest phantom that we tested. Setup error estimation was further improved using our automatic error estimation algorithm. Estimations were not statistically different for the pelvis case. Consistency improved using the software for both the chest and pelvis phantoms. We also tested the automatic algorithm with a database of over 5,000 clinical cases from our hospital. The algorithm performed well for head and breast but performed poorly for pelvis cases, probably due to lack of contrast in the megavoltage portal image. The software incorporates an original algorithm to fuse portal and DRR images, which we describe in detail. The offset optimization algorithm used in the automatic mode of operation is also unique, and may be useful if the contrast of the portal images can be improved.

PACS numbers: 87.55.Qr, 87.57.nj

Evaluation of similarity measures for use in the intensity-based rigid 2D-3D registration for patient positioning in radiotherapy

PURPOSE

Rigid 2D-3D registration is an alternative to 3D-3D registration for cases where largely bony anatomy can be used for patient positioning in external beam radiation therapy. In this article, the authors evaluated seven similarity measures for use in the intensity-based rigid 2D-3D registration using a variation in Skerl's similarity measure evaluation protocol.

METHODS

The seven similarity measures are partitioned intensity uniformity, normalized mutual information (NMI), normalized cross correlation (NCC), entropy of the difference image, pattern intensity (PI), gradient correlation (GC), and gradient difference (GD). In contrast to traditional evaluation methods that rely on visual inspection or registration outcomes, the similarity measure evaluation protocol probes the transform parameter space and computes a number of similarity measure properties, which is objective and optimization method independent. The variation in protocol offers an improved property in the quantification of the capture range. The authors used this protocol to investigate the effects of the downsampling ratio, the region of interest, and the method of the digitally reconstructed radiograph (DRR) calculation [i.e., the incremental ray-tracing method implemented on a central processing unit (CPU) or the 3D texture rendering method implemented on a graphics processing unit (GPU)] on the performance of the similarity measures. The studies were carried out using both the kilovoltage (kV) and the megavoltage (MV) images of an anthropomorphic cranial phantom and the MV images of a head-and-neck cancer patient.

RESULTS

Both the phantom and the patient studies showed the 2D-3D registration using the GPU-based DRR calculation yielded better robustness, while providing similar accuracy compared to the CPU-based calculation. The phantom study using kV imaging suggested that NCC has the best accuracy and robustness, but its slow function value change near the global maximum requires a stricter termination condition for an optimization method. The phantom study using MV imaging indicated that PI, GD, and GC have the best accuracy, while NCC and NMI have the best robustness. The clinical study using MV imaging showed that NCC and NMI have the best robustness.

CONCLUSIONS

The authors evaluated the performance of seven similarity measures for use in 2D-3D image registration using the variation in Skerl's similarity measure evaluation protocol. The generalized methodology can be used to select the best similarity measures, determine the optimal or near optimal choice of parameter, and choose the appropriate registration strategy for the end user in his specific registration applications in medical imaging.

Accuracy of an automatic patient-positioning system based on the correlation of two edge images in radiotherapy

We have clinically evaluated the accuracy of an automatic patient-positioning system based on the image correlation of two edge images in radiotherapy. Ninety-six head & neck images from eight patients undergoing proton therapy were compared with a digitally reconstructed radiograph (DRR) of planning CT. Two edge images, a reference image and a test image, were extracted by applying a Canny edge detector algorithm to a DRR and a 2D X-ray image, respectively, of each patient before positioning. In a simulation using a humanoid phantom, performed to verify the effectiveness of the proposed method, no registration errors were observed for given ranges of rotation, pitch, and translation in the x, y, and z directions. For real patients, however, there were discrepancies between the automatic positioning method and manual positioning by physicians or technicians. Using edged head coronal- and sagittal-view images, the average differences in registration between these two methods for the x, y, and z directions were 0.11 cm, 0.09 cm and 0.11 cm, respectively, whereas the maximum discrepancies were 0.34 cm, 0.38 cm, and 0.50 cm, respectively. For rotation and pitch, the average registration errors were 0.95° and 1.00°, respectively, and the maximum errors were 3.6° and 2.3°, respectively. The proposed automatic patient-positioning system based on edge image comparison was relatively accurate for head and neck patients. However, image deformation during treatment may render the automatic method less accurate, since the test image many differ significantly from the reference image.

Improving image-guided target localization through deformable registration

PURPOSE

To quantify the improvements in online target localization using kV cone beam CT (CBCT) with deformable registration.

METHODS AND MATERIAL

Twelve patients treated under a 6 fraction liver cancer radiation therapy protocol were imaged in breath hold using kV CBCT at each treatment fraction. The images were imported into the treatment planning software and rigidly registered by fitting the liver, identified on the daily kV CBCT image, into the liver contours, previously drawn on the planning CT. The liver was then manually contoured on each CBCT image. Deformable registration was automatically performed, aligning the CT liver to the liver on each CBCT image using MORFEUS, a biomechanical model based deformable registration algorithm. The tumor, defined on planning CT, was mapped onto the CBCT, through MORFEUS. The center of mass (COM) displacement of the tumor was computed.

RESULTS

The mean (SD) displacement magnitude (absolute value) of the COM following deformable registration was 0.08 (0.07), 0.10 (0.11), and 0.10 (0.17) cm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The maximum displacement of the COM was 0.34, 0.65, and 0.97 cm in the LR, AP, and SI directions, respectively. Fifteen percent of the treatment fractions had a COM displacement of greater than 0.3 cm and 33% of patients had at least 1 fraction with a displacement of greater than 0.3 cm. The deformable registration, excluding the manual contouring of the liver, was performed in less than 1 minute, on average.

DISCUSSION

Rigid registration of the liver volume between planning CT and verification kV CBCT localizes the tumor to within 0.3 cm for the majority (66%) of patients; however, larger offsets in tumor position can be observed due to liver deformation.

Conformal Radiotherapy Planning for Lung Cancer: Analysis of Set-Up Uncertainties

The objective of this study was to evaluate set-up uncertainties using a portal imaging system in a population of inoperable non-small cell lung cancer. Twenty-one patients were treated by a conformal radiotherapy technique with a personalized immobilization cast. The beam was verified by comparison with a corresponding digitally reconstructed radiograph by superimposition of anatomical structures. One thousand eight hundred eighty three images were analyzed. The mean intrafraction and interfraction errors (+/-SD) were 2.17 mm and 0.9 +/- 3.7 mm, 2.3 mm and 0.9 +/- 3.1 mm, 3 mm and 0.7 +/- 3 mm on the lateral (x), cranio-caudal (y) and anterior-posterior (z) axes, respectively. The mean systematic error was small, less than 1 mm, in all directions. The random errors were 2.5 mm, 2.4 mm, and 1.8 mm on the x, y, and z axes, respectively. No correlation between errors and the patient's height, weight, age, or sex was found. Set-up errors accuracy depending on practices, each institution should review their own treatments to quantify and reduce set-up errors in clinical practice.

Interfractional set-up errors evaluation by daily electronic portal imaging of IMRT in head and neck cancer patients

INTRODUCTION

Interfractional set-up errors were assessed from daily portal images (PI) registration for head and neck cancer patients. We aimed to evaluate whether a daily PI is worthwhile and we derived the Planning Target Volume (PTV) margins from the estimation of systematic and random errors.

MATERIAL AND METHODS

Twenty patients were treated in supine position with a fixed 5-point mask immobilisation system and head-and-knee supports. DRRs (Digitally Reconstructed Radiograph) were obtained from the planning CT-scan and considered the reference images to be compared with two orthogonal PI by matching bone anatomy landmarks. A total of 567 PI were done. For the set-up errors analysis, we determined the systematic, random, and overall standard deviations (SD), as well as the overall means in three directions (cranio caudal CC, medio lateral ML and anterior posterior AP). PTV-margins were calculated according to three methods. Differences of SD regarding the overall displacements among portals performed every day and each 2, 3, or 4 days were tested.

RESULTS

The systematic set-up errors were less than 1 mm in the three directions whereas the random set-up errors were around 2 mm. PTV margins varied from 3 to 4 mm in the 3 directions. Corrections were significant in the CC direction only, in which the set-up error increased significantly when the scenario of one PI every 3 fractions was adopted.

CONCLUSIONS

It is of practical importance to apply on-line protocols with contouring of the bony landmarks on the PI in order to decrease the systematic mean error in this patient group. This study suggested that a PI in AP and ML directions once a week and every two days in the CC direction would be adequate to overcome the problem of set-up errors.

Quality assurance for kilo- and megavoltage in-room imaging and localization for off- and online setup error correction

In-room radiography is not a new concept for image-guided radiation therapy. Rapid advances in technology, however, have made this positioning method convenient, and thus radiograph-based positioning has propagated widely. The paradigms for quality assurance of radiograph-based positioning include imager performance, systems integration, infrastructure, procedure documentation and testing, and support for positioning strategy implementation.

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).

Interactive image registration tool for positioning verification in head and neck radiotherapy

This paper presents the development of a computer-aided image management tool, named CAIMT, used for patient setup error measurement in radiation treatment planning. The CAIMT incorporates user interaction to facilitate contour detection from both portal film and simulation film. The detected contours were then used as features for image registration through application of the generalized Hough transform (GHT). Positioning error was measured when the optimal registration was achieved. The CAIMT has been applied to register the image pairs of a rando(TM) phantom and five real subjects, in order to evaluate its reliability and stability. The promising results suggest that the CAIMT can assist radiotherapists to align the simulation film and the portal film precisely and steadily and therefore adjust patient positioning to the optimum in a more efficient way.

Novel image registration quality evaluator (RQE) with an implementation for automated patient positioning in cranial radiation therapy

In external beam radiation therapy, digitally reconstructed radiographs (DRRs) and portal images are used to verify patient setup based either on a visual comparison or, less frequently, with automated registration algorithms. A registration algorithm can be trapped in local optima due to irregularity of patient anatomy, image noise and artifacts, and/or out-of-plane shifts, resulting in an incorrect solution. Thus, human observation, which is subjective, is still required to check the registration result. We propose to use a novel image registration quality evaluator (RQE) to automatically identify misregistrations as part of an algorithm-based decision-making process for verification of patient positioning. A RQE, based on an adaptive pattern classifier, is generated from a pair of reference and target images to determine the acceptability of a registration solution given an optimization process. Here we applied our RQE to patient positioning for cranial radiation therapy. We constructed two RQEs-one for the evaluation of intramodal registrations (i.e., portal-portal); the other for intermodal registrations (i.e., portal-DRR). Mutual information, because of its high discriminatory ability compared with other measures (i.e., correlation coefficient and partitioned intensity uniformity), was chosen as the test function for both RQEs. We adopted 1 mm translation and 1 degree rotation as the maximal acceptable registration errors, reflecting desirable clinical setup tolerances for cranial radiation therapy. Receiver operating characteristic analysis was used to evaluate the performance of the RQE, including computations of sensitivity and specificity. The RQEs showed very good performance for both intramodal and intermodal registrations using simulated and phantom data. The sensitivity and the specificity were 0.973 and 0.936, respectively, for the intramodal RQE using phantom data. Whereas the sensitivity and the specificity were 0.961 and 0.758, respectively, for the intermodal RQE using phantom data. Phantom experiments also indicated our RQEs detected out-of-plane deviations exceeding 2.5 mm and 2.50. A preliminary retrospective clinical study of the RQE on cranial portal imaging also yielded good sensitivity > or = 0.857) and specificity (> or = 0.987). Clinical implementation of a RQE could potentially reduce the involvement of the human observer for routine patient positioning verification, while increasing setup accuracy and reducing setup verification time.

A gradient feature weighted Minimax algorithm for registration of multiple portal images to 3DCT volumes in prostate radiotherapy

PURPOSE

To develop an accurate, fast, and robust algorithm for registering portal and computed tomographic (CT) images for radiotherapy using a combination of sparse and dense field data that complement each other.

METHODS AND MATERIALS

Gradient Feature Weighted Minimax (GFW Minimax) method was developed to register multiple portal images to three-dimensional CT images. Its performance was compared with that of three others: Minimax, Mutual Information, and Gilhuijs' method. Phantom and prostate cancer patient images were used. Effects of registration errors on tumor control probability (TCP) and normal tissue complication probability (NTCP) were investigated as a relative measure.

RESULTS

Registration of four portals to CTs resulted in 30% lower error when compared with registration with two portals. Computation time increased by nearly 50%. GFW Minimax performed the best, followed by Gilhuijs' method, the Minimax method, and Mutual Information.

CONCLUSIONS

Using four portals instead of two lowered the registration error. Reduced fields of view images with full feature sets gave similar results in shorter times as full fields of view images. In clinical situations where soft tissue targets are of importance, GFW Minimax algorithm was significantly more accurate and robust. With registration errors lower than 1 mm, margins may be scaled down to 4 mm without adversely affecting TCP and NTCP.

3D interfractional patient position verification using 2D-3D registration of orthogonal images

Reproducible positioning of the patient during fractionated external beam radiation therapy is imperative to ensure that the delivered dose distribution matches the planned one. In this paper, we expand on a 2D-3D image registration method to verify a patient's setup in three dimensions (rotations and translations) using orthogonal portal images and megavoltage digitally reconstructed radiographs (MDRRs) derived from CT data. The accuracy of 2D-3D registration was improved by employing additional image preprocessing steps and a parabolic fit to interpolate the parameter space of the cost function utilized for registration. Using a humanoid phantom, precision for registration of three-dimensional translations was found to be better than 0.5 mm (1 s.d.) for any axis when no rotations were present. Three-dimensional rotations about any axis were registered with a precision of better than 0.2 degrees (1 s.d.) when no translations were present. Combined rotations and translations of up to 4 degrees and 15 mm were registered with 0.4 degrees and 0.7 mm accuracy for each axis. The influence of setup translations on registration of rotations and vice versa was also investigated and mostly agrees with a simple geometric model. Additionally, the dependence of registration accuracy on three cost functions, angular spacing between MDRRs, pixel size, and field-of-view, was examined. Best results were achieved by mutual information using 0.5 degrees angular spacing and a 10 x 10 cm2 field-of-view with 140 x 140 pixels. Approximating patient motion as rigid transformation, the registration method is applied to two treatment plans and the patients' setup errors are determined. Their magnitude was found to be < or = 6.1 mm and < or = 2.7 degrees for any axis in all of the six fractions measured for each treatment plan.

Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy

The efficacy of radiation therapy treatment depends on the patient setup accuracy at each daily fraction. A significant problem is reproducing the patient position during treatment planning for every fraction of the treatment process. We propose and evaluate an intensity based automatic registration method using multiple portal images and the pre-treatment CT volume. We perform both geometric and radiometric calibrations to generate high quality digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery. We use a graphics processing unit (GPU) to generate the DRRs in order to gain computational efficiency. We also perform a comparative study on various similarity measures and optimization procedures. Simple similarity measure such as local normalized correlation (LNC) performs best as long as the radiometric calibration is carefully done. Using the proposed method, we achieved better than 1mm average error in repositioning accuracy for a series of phantom studies using two open field (i.e., 41 cm2) portal images with 90 degrees vergence angle.

Anatomic feature-based registration for patient set-up in head and neck cancer radiotherapy

Modern radiotherapy equipment is capable of delivering high precision conformal dose distributions relative to isocentre. One of the barriers to precise treatments is accurate patient re-positioning before each fraction of treatment. At Massachusetts General Hospital, we perform daily patient alignment using radiographs, which are captured by flat panel imaging devices and sent to an analysis program. A trained therapist manually selects anatomically significant features in the skeleton, and couch movement is computed based on the image coordinates of the features. The current procedure takes about 5 to 10 min and significantly affects the efficiency requirement in a busy clinic. This work presents our effort to develop an improved, semi-automatic procedure that uses the manually selected features from the first treatment fraction to automatically locate the same features on the second and subsequent fractions. An implementation of this semi-automatic procedure is currently in clinical use for head and neck tumour sites. Radiographs collected from 510 patient set-ups were used to test this algorithm. A mean difference of 1.5 mm between manual and automatic localization of individual features and a mean difference of 0.8 mm for overall set-up were seen.

Treatment accuracy of fractionated stereotactic radiotherapy

BACKGROUND AND PURPOSE

To assess the geometric accuracy of the delivery of fractionated stereotactic radiotherapy (FSRT) for brain tumours using the Gill-Thomas-Cosman (GTC) relocatable frame. Accuracy of treatment delivery was measured via portal images acquired with an amorphous silicon based electronic portal imager (EPI). Results were used to assess the existing verification process and to review the current margins used for the expansion of clinical target volume (CTV) to planning target volume (PTV).

PATIENTS AND METHODS

Patients were immobilized in a GTC frame. Target volume definition was performed on localization CT and MRI scans and a CTV to PTV margin of 5mm (based on initial experience) was introduced in 3D. A Brown-Roberts-Wells (BRW) fiducial system was used for stereotactic coordinate definition. The existing verification process consisted of an intercomparison of the coordinates of the isocentres and anatomy between the localization and verification CT scans. Treatment was delivered with 6 MV photons using four fixed non-coplanar conformal fields using a multi-leaf collimator. Portal imaging verification consisted of the acquisition of orthogonal images centred through the treatment isocentre. Digitally reconstructed radiographs (DRRs) created from the CT localization scans were used as reference images. Semi-automated matching software was used to quantify set up deviations (displacements and rotations) between reference and portal images.

RESULTS

One hundred and twenty six anterior and 123 lateral portal images were available for analysis for set up deviations. For displacements, the total errors in the cranial/caudal direction were shown to have the largest SD's of 1.2 mm, while systematic and random errors reached SD's of 1.0 and 0.7 mm, respectively, in the cranial/caudal direction. The corresponding data for rotational errors (the largest deviation was found in the sagittal plane) was 0.7 degrees SD (total error), 0.5 degrees (systematic) and 0.5 degrees (random). The total 3D displacement was 1.8 mm (mean), 0.8 mm (SD) with a range of 0.3-3.9 mm.

CONCLUSIONS

Portal imaging has shown that the existing verification and treatment delivery techniques currently in use result in highly reproducible setups. Random and systematic errors in the treatment planning and delivery chain will always occur, but monitoring and minimising them is an essential component of quality control. Portal imaging provides fast and accurate facility for monitoring patients on treatment and the results of this study have shown that a reduction in CTV to PTV margin from 5 to 4 mm (resulting in a considerable increase in the volume of normal tissue sparing) could be made.

Intensity-based image registration using robust correlation coefficients

The ordinary sample correlation coefficient is a popular similarity measure for aligning images from the same or similar modalities. However, this measure can be sensitive to the presence of "outlier" objects that appear in one image but not the other, such as surgical instruments, the patient table, etc., which can lead to biased registrations. This paper describes an intensity-based image registration technique that uses a robust correlation coefficient as a similarity measure. Relative to the ordinary sample correlation coefficient, the proposed similarity measure reduces the influence of outliers. We also compared the performance of the proposed method with the mutual information-based method. The robust correlation-based method should be useful for image registration in radiotherapy (KeV to MeV X-ray images) and image-guided surgery applications. We have investigated the properties of the proposed method by theoretical analysis, computer simulations, a phantom experiment, and with functional magnetic resonance imaging data.

On-line aSi portal imaging of implanted fiducial markers for the reduction of interfraction error during conformal radiotherapy of prostate carcinoma

PURPOSE

An on-line system to ensure accuracy of daily setup and therapy of the prostate has been implemented with no equipment modification required. We report results and accuracy of patient setup using this system.

METHODS AND MATERIALS

Radiopaque fiducial markers were implanted into the prostate before radiation therapy. Lateral digitally reconstructed radiographs (DRRs) were obtained from planning CT data. Before each treatment fraction, a lateral amorphous silicon (aSi) portal image was acquired and the position of the fiducial markers was compared to the DRRs using chamfer matching. Couch translation only was used to account for marker position displacements, followed by a second lateral portal image to verify isocenter position. Residual displacement data for the aSi and previous portal film systems were compared.

RESULTS

This analysis includes a total of 239 portal images during treatment in 17 patients. Initial prostate center of mass (COM) displacements in the superior, inferior, anterior, and posterior directions were a maximum of 7 mm, 9 mm, 10 mm and 11 mm respectively. After identification and correction, prostate COM displacements were <3 mm in all directions. The therapists found it simple to match markers 88% of the time using this system. Treatment delivery times were in the order of 9 min for patients requiring isocenter adjustment and 6 min for those who did not.

CONCLUSIONS

This system is technically possible to implement and use as part of an on-line correction protocol and does not require a longer than standard daily appointment time at our center with the current action limit of 3 mm. The system is commercially available and is more efficient and user-friendly than portal film analysis. It provides the opportunity to identify and accommodate interfraction organ motion and may also permit the use of smaller margins during conformal prostate radiotherapy. Further integration of the system such as remote table control would improve efficiency.

Registration of electronic portal images for patient set-up verification

Images acquired from an electronic portal imaging device are aligned with digitally reconstructed radiographs (DRRs) or other portal images to verify patient positioning during radiation therapy. Most of the currently available computer aided registration methods are based on the manual placement of corresponding landmarks. The purpose of the paper is twofold: (a) the establishment of a methodology for patient set-up verification during radiotherapy based on the registration of electronic portal images, and (b) the evaluation of the proposed methodology in a clinical environment. The estimation of set-up errors, using the proposed methodology, can be accomplished by matching the portal image of the current fraction of the treatment with the portal image of the baseline treatment (reference portal image) using a nearly automated technique. The proposed registration method is tested on a number of phantom data as well as on data from four patients. The phantom data included portal images that corresponded to various positions of the phantom on the treatment couch. For each patient, a set of 30 portal images was used. For the phantom data (for both transverse and lateral portal images), the maximum absolute deviations of the translational shifts were within 1.5 mm, whereas the in-plane rotation angle error was less than 0.5 degrees. The two-way Anova revealed no statistical significant variability both within observer and between-observer measurements (P > 0.05). For the patient data, the mean values obtained with manual and the proposed registration methods were within 0.5 mm. In conclusion, the proposed registration method has been incorporated within a system, called ESTERR-PRO. Its image registration capability achieves high accuracy and both intra- and inter-user reproducibility. The system is fully operational within the Radiotherapy Department of 'HYGEIA' Hospital in Athens and it could be easily installed in any other clinical environment since it requires standardized hardware specifications and minimal human intervention.

Assessment of a radiotherapy patient immobilization device using single plane port radiographs and a remote computed tomography scanner

Radiation treatment requires a precise procedure for interfraction repositioning of the patient. The purpose of this study was to determine the accuracy of our fixation device in treatment position and to evaluate the setup accuracy with two different methods. The positioning data of 19 canine patients with tumors in the head region (oral, nasal, cerebral) treated with photon or proton irradiation were included in this study. The patients were immobilized by means of an individualized fixation device. Focus was set upon interfraction displacement with systematic and random components. In one method, treatment position was evaluated using single plane port radiographs and megavoltage x-rays. In the other method, two orthogonal CT-topograms were acquired to evaluate the precision of positioning of the patient in the immobilization device. Systematic and random displacements were calculated and presented as mean values with corresponding 95% confidence intervals. In spite of a difference between both methods, the positioning seemed to be accurate within the expected range. It seems that a safety margin of 3.7 mm would be enough for both methods to take into account systematic and random position variability in the fixation device, thereby preventing geometric inaccuracies of treatment delivery. The reported immobilization protocol provides accurate patient immobilization for photon and conformal proton radiation therapy.

Evaluation of cost functions for gray value matching of two-dimensional images in radiotherapy

In external beam radiotherapy, portal imaging is applied for verification of the patient setup. Current automatic methods for portal image registration, which are often based on segmentation of anatomical structures, are especially successful for images of the pelvic region. For portal images of more complicated anatomical structures, e.g., lung, these techniques are less successful. It is desirable to have a method for image registration that is applicable for a wide range of treatment sites. In this study, a registration method for two-dimensional (2D) registration of portal and reference images based on intensity values was tested on portal images of various anatomical sites. Tests were performed with and without preprocessing (unsharp mask filtering followed by histogram equalization) for 96 image pairs and six cost functions. The images were obtained from treatments of the rectum, salivary gland, brain, prostate, and lung. To get insight into the behavior of the various cost functions, cost function values were computed for each portal image for 20,000 transformations of the corresponding reference image, translating the reference image in a range of +/- 1 cm and rotating +/- 10 degrees with respect to the clinical match. The automatic match was defined as the transformation associated with the global minimum (found by an exhaustive search). Without preprocessing, the registration reliability was low (less than 27%). With preprocessing, about 90% of the matches were successful, with a difference with our gold standard (manual registration) of about 1 mm and 1 degree SD. All tested cost functions performed similarly. However, the number of local minima using mutual information was larger than for the other tested cost functions. A cost function based on the mean product of the corresponding pixel values had the least number of local minima. In conclusion, gray value based registration of portal images is applicable for a wide range of treatment sites. However, pre-processing of the images is essential.

Patient setup error measurement using 3D intensity-based image registration techniques

PURPOSE

Conformal radiotherapy requires accurate patient positioning with reference to the initial three-dimensional (3D) CT image. Patient setup is controlled by comparison with portal images acquired immediately before patient treatment. Several automatic methods have been proposed, generally based on segmentation procedures. However, portal images are of very low contrast, leading to segmentation inaccuracies. In this study, we propose an intensity-based (with no segmentation), fully automatic, 3D method, associating two portal images and a 3D CT scan to estimate patient setup.

MATERIALS AND METHODS

Images of an anthropomorphic phantom were used. A CT scan of the pelvic area was first acquired, then the phantom was installed in seven positions. The process is a 3D optimization of a similarity measure in the space of rigid transformations. To avoid time-consuming digitally reconstructed radiograph generation at each iteration, we used two-dimensional transformations and two sets of specific and pregenerated digitally reconstructed radiographs. We also propose a technique for computing intensity-based similarity measures between several couples of images. A correlation coefficient, chi-square, mutual information, and correlation ratio were used.

RESULTS

The best results were obtained with the correlation ratio. The median root mean square error was 2.0 mm for the seven positions tested and was, respectively, 3.6, 4.4, and 5.1 for correlation coefficient, chi-square, and mutual information.

CONCLUSIONS

Full 3D analysis of setup errors is feasible without any segmentation step. It is fast and accurate and could therefore be used before each treatment session. The method presents three main advantages for clinical implementation-it is fully automatic, applicable to all tumor sites, and requires no additional device.

A feasibility study of mutual information based setup error estimation for radiotherapy

We have investigated a fully automatic setup error estimation method that aligns DRRs (digitally reconstructed radiographs) from a three-dimensional planning computed tomography image onto two-dimensional radiographs that are acquired in a treatment room. We have chosen a MI (mutual information)-based image registration method, hoping for robustness to intensity differences between the DRRs and the radiographs. The MI-based estimator is fully automatic since it is based on the image intensity values without segmentation. Using 10 repeated scans of an anthropomorphic chest phantom in one position and two single scans in two different positions, we evaluated the performance of the proposed method and a correlation-based method against the setup error determined by fiducial marker-based method. The mean differences between the proposed method and the fiducial marker-based method were smaller than 1 mm for translational parameters and 0.8 degree for rotational parameters. The standard deviations of estimates from the proposed method due to detector noise were smaller than 0.3 mm and 0.07 degree for the translational parameters and rotational parameters, respectively.

Remote verification in radiotherapy using digitally reconstructed radiography (DRR) and portal images: a pilot study

PURPOSE

To use digitally reconstructed radiography (DRR) and digitally compressed portal images in distant consultation using a telecommunications network, the verification performance of DRR and digitally compressed portal images on the image console was investigated.

METHODS AND MATERIALS

A human thoracic phantom was scanned with computed tomography (CT). Radiotherapy was planned at 5 different anatomic locations. A digitally reconstructed radiograph was made; verification films of the phantom were then taken with 6-MV X-rays. The treatment center was intentionally dislocated. Fifty sets of DRR and portal images were seen by 7 doctors on a conventional view-box (view-box method) to judge whether the treatment center was dislocated. These image sets were digitalized by a film scanner, compressed to 1/10 Joint Photographic Experts Group (JPEG) format, and compared on an image console by the same physicians (image-console method). The verification performance of the image console method was compared with that of the view-box method by means of receiver operating characteristic (ROC) analysis. Clinically, 159 portal-image-sets were verified with the image-console method and the appropriateness of the decision was later assessed by the view-box method.

RESULTS

The accuracy of the treatment verification was estimated to be 88.8% by the conventional view-box method and 88.3% by the image-console method. There was no statistically significant difference in the verification performances of the conventional method (Az = 0.86+/-0.02) and the image console method (Az = 0.84+/-0.07). Frequent digital image-processing modification was positively related to the accuracy of verification. Clinically, there were 3 (1.8%) major corrections, 31 (19.5%) minor corrections, and 123 cases with no correction. No further correction was called for by the re-evaluation using the view-box method.

CONCLUSION

The verification performance of DRR and digitally compressed portal images on the image console was as accurate as the conventional method. Distant consultation using DRR and portal images through telecommunication is usable in clinical practice.

Calculation of x-ray transmission through a multileaf collimator

A ray tracing based method has been developed to calculate the x-ray transmission through a multileaf collimator (MLC) for beam delivery verification and dose calculation in intensity modulated radiotherapy (IMRT). The path length of a ray line in the MLC is accurately calculated using the exact geometry of the MLC leaves. The fluence distribution of an IMRT field is calculated first using a point source. The fluence distribution for a realistic beam model is obtained, as an approximation, by convolving the point source fluence distribution with the distribution of source strength. Full ray tracing calculations are performed using analytic and Monte Carlo simulated beam models to verify the accuracy of the convolution method. The calculation is in better agreement with measurements using either film or a beam imaging system (BIS) than previous calculations for MLC transmission using a simplified model. This ray tracing calculation can be applied to the problem of verifying dynamic MLC leaf sequences as part of a patient-specific quality assurance process for IMRT.

Guide to clinical use of electronic portal imaging

The Electronic Portal Imaging Device (EPID) provides localization quality images and computer-aided analysis, which should in principal, replace portal film imaging. Modern EPIDs deliver superior image quality and an array of analysis tools that improve clinical decision making. It has been demonstrated that the EPID can be a powerful tool in the reduction of treatment setup errors and the quality assurance and verification of complex treatments. However, in many radiation therapy clinics EPID technology is not in routine clinical use. This low utilization suggests that the capability and potential of the technology alone do not guarantee its full adoption. This paper addresses basic considerations required to facilitate clinical implementation of the EPID technology and gives specific examples of successful implementations.

Automatic on-line electronic portal image analysis with a wavelet-based edge detector

A fully automatic method for on-line electronic portal image analysis is proposed. The method uses multiscale edge detection with wavelets for both the field outline and the anatomical structures. An algorithm to extract and combine the information from different scales has been developed. The edges from the portal image are aligned with the edges from the reference image using chamfer matching. The reference is the first portal image of each treatment. The matching is applied first to the field and subsequently to the anatomy. The setup deviations are quantified as the displacement of the anatomical structures relative to the radiation beam boundaries. The performance of the algorithm was investigated for portal images with different contrast and noise level. The automatic analysis was used first to detect simulated displacements. Then the automatic procedure was tested on anterior-posterior and lateral portal images of a pelvic phantom. In both sets of tests the differences between the measured and the actual shifts were used to quantify the performance. Finally we applied the automatic procedure to clinical images of pelvic and lung regions. The output of the procedure was compared with the results of a manual match performed by a trained operator. The errors for the phantom tests were small: average standard deviation of 0.39 mm and 0.26 degrees and absolute mean error of 0.31 mm and 0.2 degrees were obtained. In the clinical cases average standard deviations of 1.32 mm and 0.6 degrees were found. The average absolute mean errors were 1.09 mm and 0.39 degrees. Failures were registered in 2% of the phantom tests and in 3% of the clinical cases. The algorithm execution is approximately 5 s on a 168 MHz Sun Ultra 2 workstation. The automatic analysis tool is considered to be a very useful tool for on-line setup corrections.

Three-dimensional anatomy setup verification by correlation of orthogonal portal images and digitally reconstructed radiographs

A method for three-dimensional verification of anatomy setup that uses the correlation of portal images and reference megavoltage digitally reconstructed radiographs (MDRRs) is presented. Prior to a treatment, an orthogonal pair of portal images is acquired from which subimages containing anatomical features are selected. These subimages are consequently matched to MDRRs that represent different rotations of the anatomy around axes going through the treatment isocenter. The Pearson correlation coefficient is employed for the matching since it is invariant with respect to global scaling and shifting of the image intensities. Furthermore, it does not require feature extraction or point-pair identification. The greatest value of the correlation coefficient corresponds to the proper rotational alignment of the anatomy and the location of the correlation maximum in each view indicates the translational shifts of the anatomy. The mean accuracy of translation and rotation registrations tests were a fraction of a millimeter and a fraction of a degree, respectively, for MDRR-to-MDRR matching. For portal-to-MDRR matching, the mean translation registration error is on the order of 1 mm and the mean error in radial displacement is of the order of 2.7 mm.

Clinical application of digitally-reconstructed radiographs generated from magnetic resonance imaging for intracranial lesions

PURPOSE

The purpose of this work is to demonstrate the clinical utility of magnetic resonance (MR) imaging-based digitally reconstructed radiographs (DRRs) for the setup and verification of patients with intracranial lesions.

METHODS AND MATERIALS

MR images of 16 patients with various intracranial lesions were obtained for treatment planning and virtual simulation. Five-millimeter-thick contiguous T1-weighted postcontrast transverse slices were obtained using a standard head coil in a General Electric Signa 1.5T MR scanner. MR-DRRs were generated using the "pseudo density" technique on an existing treatment planning computer without any special modifications. Anterior and lateral verification films were taken for each patient for visual comparison with MR-based DRRs.

RESULTS

Visual alignment with bony landmarks, including the orbits, frontal sinus, sphenoid sinus, auditory meatus, nasal bone, vomer bone, mastoid process, and the cranium were used by physicians, physicists, and therapists to verify patient positioning. Misalignments from 3 to 10 mm were visually identified and corrected using this technique.

CONCLUSION

A method for visually utilizing MR-based DRRs during simulation has been developed and clinically implemented. The quality of MR-DRRs generated using this technique is such that physicians, physicists, and therapists can easily and routinely compare MR-DRRs side-by-side with simulation films.

[Conformal radiotherapy: principles and classification]

'Conformal radiotherapy' is the name fixed by usage and given to a new form of radiotherapy resulting from the technological improvements observed during, the last ten years. While this terminology is now widely used, no precise definition can be found in the literature. Conformal radiotherapy refers to an approach in which the dose distribution is more closely 'conformed' or adapted to the actual shape of the target volume. However, the achievement of a consensus on a more specific definition is hampered by various difficulties, namely in characterizing the degree of 'conformality'. We have therefore suggested a classification scheme be established on the basis of the tools and the procedures actually used for all steps of the process, i.e., from prescription to treatment completion. Our classification consists of four levels: schematically, at level 0, there is no conformation (rectangular fields); at level 1, a simple conformation takes place, on the basis of conventional 2D imaging; at level 2, a 3D reconstruction of the structures is used for a more accurate conformation; and level 3 includes research and advanced dynamic techniques. We have used our personal experience, contacts with colleagues and data from the literature to analyze all the steps of the planning process, and to define the tools and procedures relevant to a given level. The corresponding tables have been discussed and approved at the European level within the Dynarad concerted action. It is proposed that the term 'conformal radiotherapy' be restricted to procedures where all steps are at least at level 2.

Comparison of computer workstation with light box for detecting setup errors from portal images

PURPOSE

Observer studies were conducted to test the hypothesis that radiation oncologists using a computer workstation for portal image analysis can detect setup errors at least as accurately as when following standard clinical practice of inspecting portal films on a light box.

METHODS AND MATERIALS

In a controlled observer study, nine radiation oncologists used a computer workstation, called PortFolio, to detect setup errors in 40 realistic digitally reconstructed portal radiograph (DRPR) images. PortFolio is a prototype workstation for radiation oncologists to display and inspect digital portal images for setup errors. PortFolio includes tools for image enhancement; alignment of crosshairs, field edges, and anatomic structures on reference and acquired images; measurement of distances and angles; and viewing registered images superimposed on one another. The test DRPRs contained known in-plane translation or rotation errors in the placement of the fields over target regions in the pelvis and head. Test images used in the study were also printed on film for observers to view on a light box and interpret using standard clinical practice. The mean accuracy for error detection for each approach was measured and the results were compared using repeated measures analysis of variance (ANOVA) with the Geisser-Greenhouse test statistic.

RESULTS

The results indicate that radiation oncologists participating in this study could detect and quantify in-plane rotation and translation errors more accurately with PortFolio compared to standard clinical practice.

CONCLUSIONS

Based on the results of this limited study, it is reasonable to conclude that workstations similar to PortFolio can be used efficaciously in clinical practice.

A simple and accurate coordinate transformation for a stereotactic radiotherapy system

A global registration algorithm using only two CT slices was developed to transform target points known in the Brown-Roberts-Wells frame back to a CT-simulator coordinate system. The algorithm uses exact solutions to determine all of the points of interest based on BRW pins in the two CT-slices. In comparison with the algorithms based on individual slices, there is no requirement of digitization of BRW pins in every CT slice. There is no approximation (or linear interpolation) for determination of the target points that fell in between two CT slices. Results in 60 clinical cases demonstrate that the accuracy and precision of the isocentric positions are within the digitization uncertainty. Application of this global image registration can simplify the coordinate transformation in stereotactic radiation therapy.

The importance of computed tomography slice thickness in radiographic patient positioning for radiosurgery

A new radiographic patient positioning technique developed for radiosurgery has been analyzed to show the effect of computed tomography (CT) slice thickness on the precision of target localization during treatment. The positioning technique establishes the pose of the patient's anatomy during treatment by comparing treatment room radiographs with digitally reconstructed radiographs derived from a CT study. The measured pose is then used to align the x-ray therapy beam with the treatment site, without resorting to mechanical fixation. The technique has been found to be sensitive to submillimeter changes in skull position, which is the level of precision desired for radiosurgery. In this report it is shown that the precision of head localization improves by a factor of 2 when the CT slice thickness is reduced from 3.0 to 1.5 mm. This indicates that, in radiosurgical applications, image-guided beam alignment can be significantly influenced by the spatial resolution of the reference CT study. This result is relevant to all high-precision radiographic positioning techniques that utilize CT images.