The Poised Cannula Technique Reduces the Stereotactic Error of the Fiducial-Less Frameless DBS Procedure.

BACKGROUND: In this study, we describe a technique of optimizing the accuracy of frameless deep brain stimulation (DBS) lead placement through the use of a cannula poised at the entry to predict the location of the fully inserted device. This allows real-time correction of error prior to violation of the deep gray matter. METHODS: We prospectively gathered data on radial error during the operative placements of 40 leads in 28 patients using frameless fiducial-less DBS surgery. Once the Nexframe had been aligned to target, a cannula was inserted through the center channel of the BenGun until it traversed the pial surface and a low-dose O-arm spin was obtained. Using 2 points along the length of the imaged cannula, a trajectory line was projected to target depth. If lead location could be improved, the cannula was inserted through an alternate track in the BenGun down to target depth. After intraoperative microelectrode recording and clinical assessment, another O-arm spin was obtained to compare the location of the inserted lead with the location predicted by the poised cannula. RESULTS: The poised cannula projection and the actual implant had a mean radial discrepancy of 0.75 ± 0.64 mm. The poised cannula projection identified potentially clinically significant errors (avg 2.07 ± 0.73 mm) in 33% of cases, which were reduced to a radial error of 1.33 ± 0.66 mm (p = 0.02) after correction using an alternative BenGun track. The final target to implant error for all 40 leads was 1.20 ± 0.52 mm with only 2.5% of errors being >2.5 mm. CONCLUSION: The poised cannula technique results in a reduction of large errors (>2.5 mm), resulting in a decline in these errors to 2.5% of implants as compared to 17% in our previous publication using the fiducial-less method and 4% using fiducial-based methods of DBS lead placement.

A Comparative Study of Fiducial-Based and Fiducial-Less Registration Utilizing the O-Arm.

BACKGROUND: Frameless stereotactic surgery utilizing fiducial-based (FB) registration is an established tool in the armamentarium of deep brain stimulation (DBS) surgeons. Fiducial-less (FL) registration via intraoperative CT, such as the O-arm, has been routinely used in spine surgery, but its accuracy for DBS surgery has not been studied in a clinical setting. OBJECTIVE: We undertook a study to analyze the accuracy of the FL technique in DBS surgery and compare it to the FB method. METHODS: In this prospective cohort study, 97 patients underwent DBS surgery using the NexFrame and the O-arm registration stereotactic system. Patients underwent FB (n = 50) registration from 2015 to 2016 and FL (n = 47) O-arm registration from 2016 to 2017. RESULTS: The radial errors (RE) and vector/euclidean errors of FB and FL registration were not significantly different. There was no difference in additional passes between methods, but there was an increase in the number of RE ≥2.5 mm in the FL method. CONCLUSION: Although there was no statistically significant difference in RE or the need for additional passes, the increased number of errors ≥2.5 mm with the FL method (17 vs. 4% in FB) indicates the need for further study. We concluded that O-arm images of the implants should be utilized to assess and correct for this error.

A quantitative assessment of the accuracy and reliability of O-arm images for deep brain stimulation surgery.

BACKGROUND: Deep brain stimulation (DBS) surgery has an average accuracy of 2 to 3 mm (range, 0-6 mm). Intraoperative detection of track location may be useful in interpreting physiological results and thus limit the number of brain penetrations as well as decrease the incidence of reoperations. The O-arm has been used to identify the DBS lead position; however, early results have indicated a significant discrepancy with lead position on postoperative imaging. OBJECTIVE: This prospective study was conducted to determine the accuracy and reliability of fiducial and track localization and to assess the accuracy of O-arm image-based registration. The computed tomography (CT) image was considered the gold standard, and so for this study, the locations of all objects on the O-arm image were compared with their CT location. METHODS: Thirty-three DBS surgeries were performed using the O-arm to image each track with detailed analysis of fiducial and track localization accuracy. Twenty-one subsequent surgeries were performed using O-arm registration. Only the final lead position was assessed in these individuals. RESULTS: The measurement error of the system was 0.7 mm, with a maximum error of 1.9 mm. Twenty-two percent of the parallel tracks through the BenGun exceeded this error and demonstrated the ability of the O-arm to detect these skewed tracks. The accuracy of final lead position was 2.04 mm in procedures with registration based on an O-arm image. This was not significantly different from CT-based registration at 2.16 mm. CONCLUSION: The O-arm was able to detect skewed tracks and provide registration accuracy equivalent to a CT scan.

4D Cone-beam CT reconstruction using a motion model based on principal component analysis.

PURPOSE: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. METHODS: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. RESULTS: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. CONCLUSIONS: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

The quantized DCT and its application to DCT-based video coding.

The two-dimensional (2-D) discrete cosine transform (DCT) and the subsequent quantization of the transform coefficients are two computationally demanding steps of any DCT-based video encoder. In this paper, we propose an efficient joint implementation of these two steps, where the precision in computing the DCT can be exchanged for a reduction in the computational complexity. First, the quantization is embedded in the DCT, thus eliminating the need to explicitly quantize the transform coefficients. A multiplierless integer implementation of the quantized DCT (QDCT) is then proposed that performs shift and add operations instead of full multiplications. A sequence of multiplierless QDCT algorithms is obtained with increasing precision and number of computations. Finally, further savings in computations are obtained by terminating the DCT computations whenever intermediate results indicate that the transform and quantization steps will likely result in a block of zero values. The proposed algorithms are applied to, and results are presented for, high-quality MPEG-2 and low bit rate H.263 video encoding.