Biomedical subjects

David Sarrut

Publications and source records attributed to David Sarrut.

5 recordsLinked to original sources

PubMed2006

Deformable registration for image-guided radiation therapy.

Ths paper examines several applications of deformable registration algorithms in the field of image-guided radiotherapy. The first part focuses on the description of input and output of deformable registration algorithms, with a brief review of conventional and most current methods. The typical applications of deformable registration are then reviewed on the basis of four practical examples. The first two sets of examples deal with the fusion of images obtained from the same patient (inter-fraction registration), with time intervals of several days between each image. The other two examples deal with the fusion of images obtained in immediate sequence (intra-fraction registration); in this case, the focus is the displacement during image acquisition or patient treatment (mainly due to respiratory movement), with time intervals in the order of magnitude of tenths of seconds. Finally, the registration of images of different patients (inter-patient registration) is also discussed. In conclusion, deformable registration has become a fundamental tool for image analysis in radiotherapy. Although extensive validation of the numerous existing methods is required before extending its clinical use, deformable registration is expected to become a standard methodology in the treatment planning systems in the near future.

Humans↗

PubMed2006

Simulation of four-dimensional CT images from deformable registration between inhale and exhale breath-hold CT scans.

PURPOSE: We propose to simulate an artificial four-dimensional (4-D) CT image of the thorax during breathing. It is performed by deformable registration of two CT scans acquired at inhale and exhale breath-hold. MATERIALS AND METHODS: Breath-hold images were acquired with the ABC (Active Breathing Coordinator) system. Dense deformable registrations were performed. The method was a minimization of the sum of squared differences (SSD) using an approximated second-order gradient. Gaussian and linear-elastic vector field regularizations were compared. A new preprocessing step, called a priori lung density modification (APLDM), was proposed to take into account lung density changes due to inspiration. It consisted of modulating the lung densities in one image according to the densities in the other, in order to make them comparable. Simulated 4-D images were then built by vector field interpolation and image resampling of the two initial CT images. A variation in the lung density was taken into account to generate intermediate artificial CT images. The Jacobian of the deformation was used to compute voxel values in Hounsfield units. The accuracy of the deformable registration was assessed by the spatial correspondence of anatomic landmarks located by experts. RESULTS: APLDM produced statistically significantly better results than the reference method (registration without APLDM preprocessing). The mean (and standard deviation) of distances between automatically found landmark positions and landmarks set by experts were 2.7(1.1) mm with APLDM, and 6.3(3.8) mm without. Interexpert variability was 2.3(1.2) mm. The differences between Gaussian and linear elastic regularizations were not statistically significant. In the second experiment using 4-D images, the mean difference between automatic and manual landmark positions for intermediate CT images was 2.6(2.0) mm. CONCLUSION: The generation of 4-D CT images by deformable registration of inhale and exhale CT images is feasible. This can lower the dose needed for 4-D CT acquisitions or can help to correct 4-D acquisition artifacts. The 4-D CT model can be used to propagate contours, to compute a 4-D dose map, or to simulate CT acquisitions with an irregular breathing signal. It could serve as a basis for 4-D radiation therapy planning. Further work is needed to make the simulation more realistic by taking into account hysteresis and more complex voxel trajectories.

Algorithms↗

PubMed2005

Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer.

PURPOSE: To study the interfraction reproducibility of breath-holding using active breath control (ABC), and to develop computerized tools to evaluate three-dimensional (3D) intrathoracic motion in each patient. METHODS AND MATERIALS: Since June 2002, 11 patients with non-small-cell lung cancer enrolled in a Phase II trial have undergone four CT scans: one during free-breathing (reference) and three using ABC. Patients left the room between breath-hold scans. The patient's breath was held at the same predefined phase of the breathing cycle (about 70% of the vital capacity) using the ABC device, then patients received 3D-conformal radiotherapy. Automated computerized tools for breath-hold CT scans were developed to analyze lung and tumor interfraction residual motions with 3D nonrigid registration. RESULTS: All patients but one were safely treated with ABC for 7 weeks. For 6 patients, the lung volume differences were <5%. The mean 3D displacement inside the lungs was between 2.3 mm (SD 1.4) and 4 mm (SD 3.3), and the gross tumor volume residual motion was 0.9 mm (SD 0.4) to 5.9 mm (SD 0.7). The residual motion was slightly greater in the inferior part of the lung than the superior. For 2 patients, we detected volume changes >300 cm(3) and displacements >10 mm, probably owing to atelectasia and emphysema. One patient was excluded, and two others had incomplete data sets. CONCLUSION: Breath-holding with ABC was effective in 6 patients, and discrepancies were clinically accountable in 2. The proposed 3D nonrigid registration method allows for personalized evaluation of breath-holding reproducibility with ABC. It will be used to adapt the patient-specific internal margins.

Carcinoma, Non-Small-Cell Lung↗

PubMed2005

Respiratory signal extraction for 4D CT imaging of the thorax from cone-beam CT projections.

Current methods of four-dimensional (4D) CT imaging of the thorax synchronise the acquisition with a respiratory signal to restrospectively sort acquired data. The quality of the 4D images relies on an accurate description of the position of the thorax in the respiratory cycle by the respiratory signal. Most of the methods used an external device for acquiring the respiratory signal. We propose to extract it directly from thorax cone-beam (CB) CT projections. This study implied two main steps: the simulation of a set of CBCT projections, and the extraction, selection and integration of motion information from the simulation output to obtain the respiratory signal. A real respiratory signal was used for simulating the CB acquisition of a breathing patient. We extracted from CB images a respiratory signal with 96.4% linear correlation with the reference signal, but we showed that other measures of the quality of the extracted respiratory signal were required.

Algorithms↗

PubMed2003

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.

Anthropometry↗