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Reconstruction of a cone-beam CT image via forward iterative projection matching.

ABSTRACT: To demonstrate the feasibility of reconstructing a cone-beam CT (CBCT) image by deformably altering a prior fan-beam CT (FBCT) image such that it matches the anatomy portrayed in the CBCT projection data set.A prior FBCT image of the patient is assumed to be available as a source image. A CBCT projection data set is obtained and used as a target image set. A parametrized deformation model is applied to the source FBCT image, digitally reconstructed radiographs (DRRs) that emulate the CBCT projection image geometry are calculated and compared to the target CBCT projection data, and the deformation model parameters are adjusted iteratively until the DRRs optimally match the CBCT projection data set. The resulting deformed FBCT image is hypothesized to be an accurate representation of the patient's anatomy imaged by the CBCT system. The process is demonstrated via numerical simulation. A known deformation is applied to a prior FBCT image and used to create a synthetic set of CBCT target projections. The iterative projection matching process is then applied to reconstruct the deformation represented in the synthetic target projections; the reconstructed deformation is then compared to the known deformation. The sensitivity of the process to the number of projections and the DRR/CBCT projection mismatch is explored by systematically adding noise to and perturbing the contrast of the target projections relative to the iterated source DRRs and by reducing the number of projections.When there is no noise or contrast mismatch in the CBCT projection images, a set of 64 projections allows the known deformed CT image to be reconstructed to within a nRMS error of 1% and the known deformation to within a nRMS error of 7%. A CT image nRMS error of less than 4% is maintained at noise levels up to 3% of the mean projection intensity, at which the deformation error is 13%. At 1% noise level, the number of projections can be reduced to 8 while maintaining CT image and deformation errors of less than 4% and 13%, respectively. The method is sensitive to contrast mismatch between the simulated projections and the target projections when the soft-tissue contrast in the projections is low.By using prior knowledge available in a FBCT image, the authors show that a CBCT image can be iteratively reconstructed from a comparatively small number of projection images, thus saving acquisition time and reducing imaging dose. This will enable more frequent daily imaging during radiation therapy. Because the process preserves the CT numbers of the FBCT image, the resulting 3D image intensities will be more accurate than a CBCT image reconstructed via conventional backprojection methods. Reconstruction errors are insensitive to noise at levels beyond what would typically be found in CBCT projection data, but are sensitive to contrast mismatch errors between the CBCT projection data and the DRRs.

SUBMITTER: Brock RS

PROVIDER: S-EPMC2997812 | biostudies-other | 2010 Dec

REPOSITORIES: biostudies-other

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Project description:To generalize and experimentally validate a novel algorithm for reconstructing the 3D pose (position and orientation) of implanted brachytherapy seeds from a set of a few measured 2D cone-beam CT (CBCT) x-ray projections.The iterative forward projection matching (IFPM) algorithm was generalized to reconstruct the 3D pose, as well as the centroid, of brachytherapy seeds from three to ten measured 2D projections. The gIFPM algorithm finds the set of seed poses that minimizes the sum-of-squared-difference of the pixel-by-pixel intensities between computed and measured autosegmented radiographic projections of the implant. Numerical simulations of clinically realistic brachytherapy seed configurations were performed to demonstrate the proof of principle. An in-house machined brachytherapy phantom, which supports precise specification of seed position and orientation at known values for simulated implant geometries, was used to experimentally validate this algorithm. The phantom was scanned on an ACUITY CBCT digital simulator over a full 660 sinogram projections. Three to ten x-ray images were selected from the full set of CBCT sinogram projections and postprocessed to create binary seed-only images.In the numerical simulations, seed reconstruction position and orientation errors were approximately 0.6 mm and 5 degrees, respectively. The physical phantom measurements demonstrated an absolute positional accuracy of (0.78 +/- 0.57) mm or less. The theta and phi angle errors were found to be (5.7 +/- 4.9) degrees and (6.0 +/- 4.1) degrees, respectively, or less when using three projections; with six projections, results were slightly better. The mean registration error was better than 1 mm/6 degrees compared to the measured seed projections. Each test trial converged in 10-20 iterations with computation time of 12-18 min/iteration on a 1 GHz processor.This work describes a novel, accurate, and completely automatic method for reconstructing seed orientations, as well as centroids, from a small number of radiographic projections, in support of intraoperative planning and adaptive replanning. Unlike standard back-projection methods, gIFPM avoids the need to match corresponding seed images on the projections. This algorithm also successfully reconstructs overlapping clustered and highly migrated seeds in the implant. The accuracy of better than 1 mm and 6 degrees demonstrates that gIFPM has the potential to support 2D Task Group 43 calculations in clinical practice.

Project description:PurposeTo compare cone-beam computed tomography (CT) navigation vs conventional CT image guidance during biopsies.Materials and methodsPatients scheduled for image-guided biopsies were prospectively and randomly assigned to conventional CT guidance vs cone-beam CT navigation. Radiation dose, accuracy of final needle position, rate of histopathologic diagnosis, and number of needle repositions to reach the target (defined as pullback to adjust position) were compared.ResultsA total of 58 patients (mean age, 57 y; 62.1% men) were randomized: 29 patients underwent 33 biopsies with CT guidance and 29 patients with 33 lesions underwent biopsy with cone-beam CT navigation. The average body mass index (BMI) was similar between groups, at 28.8 kg/m(2) ± 6.55 (P = .18). There was no difference between groups in terms of patient and lesion characteristics (eg, size, depth). The average lesion size was 29.1 ± 12.7mm for CT group vs 32.1mm ±16.8mm for cone-beam CT group (P < 0.59). Location of lesions was equally divided between the 2 groups, 20 lung lesions, 18 renal lesions and 20 other abdominal lesions. Mean number of needle repositions in the cone-beam CT group was 0.3 ± 0.5, compared with 1.9 ± 2.3 with conventional CT (P < .001). The average skin entry dose was 29% lower with cone-beam CT than with conventional CT (P < .04 accounting for BMI). The average estimated effective dose for the planning scan from phantom data was 49% lower with cone-beam CT vs conventional CT (P = .018). Accuracy, defined as the difference between planned and final needle positions, was 4.9 mm ± 4.1 for the cone-beam CT group, compared with 12.2 mm ± 8.1 for conventional CT (P < .001). Histopathologic diagnosis rates were similar between groups, at 90.9% for conventional CT and 93.9% for cone-beam CT (P = .67).ConclusionsCone-beam CT navigation for biopsies improved targeting accuracy with fewer needle repositions, lower skin entry dose, and lower effective dose for planning scan, and a comparable histopathologic diagnosis rate.

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Reconstruction of a cone-beam CT image via forward iterative projection matching.

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