Accurate Image Registration Research Articles

Endoscope image mosaic based on pyramid ORB

The primary method of endoscopic image mosaics seamlessly mosaics several continuous and overlapping endoscopic images to have a broader vision and clearer images. The key to its success is the accuracy of image registration and fusion. This paper uses the Gaussian Pyramid to improve the simple ORB-oriented algorithm. The experimental results show that the pyramid sphere algorithm has invariability, good robustness in scale change and rotation change, high registration accuracy, and the stitching speed is about 10 times that of SIFT. The improved method has more efficient feature extraction and expression methods, reduces the calculation and storage requirements, and improves the image registration efficiency. The pyramid ORB algorithm solves the problem that the ORB does not have scale invariance. The pyramid ORB is more suitable for minimally invasive surgery endoscopic image mosaic through theoretical analysis and experimental verification. The algorithm has good robustness and real-time performance.

Development of Novel Image Processing System Using Super-Resolution to Reduce Cone-Beam CT Imaging Dose in Radiation Therapy

To develop an image processing system which can improve the image quality of low-dose Cone-bean CT (CBCT) images for Image Guided Radiation Therapy (IGRT) by using super-resolution technique which can predict high-resolution CBCT images from low-resolution CBCT images.The developed system was using Single Image Super-Resolution (SISR) based on Convolutional Neural Network (CNN). We used CBCT images of the pelvic region of 123 patients for training of the developed system and its evaluation: 104 patients for training and 19 patients for performance evaluation. Original images (512×512 pixels, IMorg) were downsampled to multiple low matrix sizes (256×256 pixels (IM256), 128×128 pixels (IM128)), and we trained two types of the system to output super-resolution images (512×512 pixels) from each low matrix size image. Then, for performance evaluation, super-resolution images (IMsr2x, IMsr4x) were generated by the trained systems and evaluated them by Peak Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM). Image registration using bone matching and target matching were performed to evaluate the impact of the developed system for image registration. The generated images were also compared with the images obtained by the conventional upsampling method, Bi-Linear (IMlin2x, IMlin4x) and Bi-Cubic (IMcub2x, IMcub4x) methods, using PSNR and SSIM.The mean values and standard deviation of PSNR and SSIM of IMsr2x, IMlin2x and IMcub2x were 51.34 ± 0.87 [dB] and 0.995 ± 0.001, 43.05 ± 0.61 [dB] and 0.985 ± 0.002, 45.25 ± 0.66 [dB] and 0.989 ± 0.001, respectively. Similarly, the PSNR and SSIM of IMsr4x, IMlin4x and IMcub4x were 46.27 ± 0.95 [dB] and 0.988 ± 0.002, 37.63 ± 0.61 [dB] and 0.956 ± 0.005, 38.66 ± 0.66 [dB] and 0.960 ± 0.004, respectively. As a result of frequency component analysis, IMsr was added high frequency components that were not included in low matrix size CBCT images. Therefore, the PSNR and SSIM values were significantly higher in IMsr. The registration errors, which were defined as the errors from the registration results of IMorg, of IMsr2x and IMsr4x were 0.20 ± 0.48 [mm] and 1.25 ± 1.45 [mm] for target matching, 0.10 ± 0.31 [mm] and 0.53 ± 0.73 [mm] for bone matching respectively. With IMsr4x, the values of PSNR and SSIM were reduced, but the accuracy of image registration was not significantly affected.We developed the image processing system using SISR based on CNN to reduce the CBCT imaging dose in radiation therapy. The developed system could add the high-frequency components which were not included in low matrix size CBCT images. CBCT imaging dose could be reduced to 1/16 and 1/4 when CBCT scans were taken with 128×128 and 256×256 pixels, respectively. The developed system enables CBCT imaging using a low radiation dose without degrading the image quality.

Few-shot learning for deformable image registration in 4DCT images.

To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy. We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process. The proposed FR-Net is evaluated for 4D groupwise and 3D pairwise registration on thoracic 4DCT data sets DIR-Lab and POPI. FR-Net displays an averaged target registration error of 1.48 mm and 1.16 mm between the maximum inhalation and exhalation phases in the 4DCT of DIR-Lab and POPI, respectively, with approximately 2 min required to optimize one 4DCT. Overall, FR-Net outperforms state-of-the-art methods in terms of registration accuracy and exhibits a low computational time. We develop a few-shot groupwise DIR algorithm for 4DCT images. The promising registration performance and computational efficiency demonstrate the prospective applications of this approach in registration tasks for online adaptive radiotherapy. This work exploits DL models to solve the optimization problem in registering 4DCT scans while combining groupwise registration and few-shot learning strategy to solve the problem of consuming computational time and inferior registration accuracy.

Rapid Quality Assessment of Nonrigid Image Registration Based on Supervised Learning.

When preprocedural images are overlaid on intraprocedural images, interventional procedures benefit in that more structures are revealed in intraprocedural imaging. However, image artifacts, respiratory motion, and challenging scenarios could limit the accuracy of multimodality image registration necessary before image overlay. Ensuring the accuracy of registration during interventional procedures is therefore critically important. The goal of this study was to develop a novel framework that has the ability to assess the quality (i.e., accuracy) of nonrigid multimodality image registration accurately in near real time. We constructed a solution using registration quality metrics that can be computed rapidly and combined to form a single binary assessment of image registration quality as either successful or poor. Based on expert-generated quality metrics as ground truth, we used a supervised learning method to train and test this system on existing clinical data. Using the trained quality classifier, the proposed framework identified successful image registration cases with an accuracy of 81.5%. The current implementation produced the classification result in 5.5 s, fast enough for typical interventional radiology procedures. Using supervised learning, we have shown that the described framework could enable a clinician to obtain confirmation or caution of registration results during clinical procedures.

Reconstruction of Three-Dimensional Tibiofemoral Kinematics Using Single-Plane Fluoroscopy and a Personalized Kinematic Model

Model-based 3D/2D image registration using single-plane fluoroscopy is a common setup to determine knee joint kinematics, owing to its markerless aspect. However, the approach was subjected to lower accuracies in the determination of out-of-plane motion components. Introducing additional kinematic constraints with an appropriate anatomical representation may help ameliorate the reduced accuracy of single-plane image registration. Therefore, this study aimed to develop and evaluate a multibody model-based tracking (MbMBT) scheme, embedding a personalized kinematic model of the tibiofemoral joint for the measurement of tibiofemoral kinematics. The kinematic model was consisted of three ligaments and an articular contact mechanism. The knee joint activities in six volunteers during isolated knee flexion, lunging, and sit-to-stand motions were recorded with a biplane X-ray imaging system. The tibiofemoral kinematics determined with the MbMBT and mediolateral view fluoroscopic images were compared against those determined using biplane fluoroscopic images. The MbMBT was demonstrated to yield tibiofemoral kinematics with precision values in the range from 0.1 mm to 1.1 mm for translations and from 0.2° to 1.3° for rotations. The constraints provided by the kinematic model were shown to effectively amend the nonphysiological tibiofemoral motion and not compromise the image registration accuracy with the proposed MbMBT scheme.

An Effective Framework of Automated Visual Surface Defect Detection for Metal Parts

Developing effective automated visual surface defect detection of metal parts is a challenge due to highly reflective surfaces and miscellaneous defect patterns. In this work, an effective framework consisting of an image registration module and a defect detection module is proposed to cope with this challenge. First, an algorithm based on dual weighted principal component analysis is designed for the image registration module. In this algorithm, the visual feature and spatial feature are integrated into a unified principal component to obtain highly accurate image registration results. Then, a defect detection module is constructed using an image difference algorithm with prior constraints. In this module, two categories of constraint bounding boxes are designed to reduce the misdetection due to the susceptible edges and improve the accuracy of surface defect detection of metal parts. Three applications, including a magnetic resonance imaging dataset, a printed circuit board dataset, and an industrial case of the stainless-steel positioning pins are provided to illustrate the superiority of the proposed framework.

Near Real-Time Automatic Sub-Pixel Registration of Panchromatic and Multispectral Images for Pan-Sharpening

This paper presents a near real-time automatic sub-pixel registration method of high-resolution panchromatic (PAN) and multispectral (MS) images using a graphics processing unit (GPU). In the first step, the method uses differential geo-registration to enable accurate geographic registration of PAN and MS images. Differential geo-registration normalizes PAN and MS images to the same direction and scale. There are also some residual misalignments due to the geometrical configuration of the acquisition instruments. These residual misalignments mean the PAN and MS images still have deviations after differential geo-registration. The second step is to use differential rectification with tiny facet primitive to eliminate possible residual misalignments. Differential rectification corrects the relative internal geometric distortion between PAN and MS images. The computational burden of these two steps is large, and traditional central processing unit (CPU) processing takes a long time. Due to the natural parallelism of the differential methods, these two steps are very suitable for mapping to a GPU for processing, to achieve near real-time processing while ensuring processing accuracy. This paper used GaoFen-6, GaoFen-7, ZiYuan3-02 and SuperView-1 satellite data to conduct an experiment. The experiment showed that our method’s processing accuracy is within 0.5 pixels. The automatic processing time of this method is about 2.5 s for 1 GB output data in the NVIDIA GeForce RTX 2080Ti, which can meet the near real-time processing requirements for most satellites. The method in this paper can quickly achieve high-precision registration of PAN and MS images. It is suitable for different scenes and different sensors. It is extremely robust to registration errors between PAN and MS.

Evaluation of performance of pelvic CT-MR deformable image registration using two software programs.

We assessed the accuracy of deformable image registration (DIR) accuracy between CT and MR images using an open-source software (Elastix, from Utrecht Medical Center) and a commercial software (Velocity AI Ver. 3.2.0 from Varian Medical Systems, Palo Alto, CA, USA) software. Five male patients' pelvic regions were studied using publicly available CT, T1-weighted (T1w) MR, and T2-weighted (T2w) MR images. In the cost function of the Elastix, we used six DIR parameter settings with different regularization weights (Elastix0, Elastix0.01, Elastix0.1, Elastix1, Elastix10, and Elastix100). We used MR Corrected Deformable algorithm for Velocity AI. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) for the prostate, bladder, rectum and left and right femoral heads were used to evaluate DIR accuracy. Except for the bladder, most algorithms produced good DSC and MDA results for all organs. In our study, the mean DSCs for the bladder ranged from 0.75 to 0.88 (CT-T1w) and from 0.72 to 0.76 (CT-T2w). Similarly, the mean MDA ranges were 2.4 to 4.9mm (CT-T1w), 4.6 to 5.3mm (CT-T2w). For the Elastix, CT-T1w was outperformed CT-T2w for both DSCs and MDAs at Elastix0, Elastix0.01, and Elastix0.1. In the case of Velocity AI, no significant differences in DSC and MDA of all organs were observed. This implied that the DIR accuracy of CT and MR images might differ depending on the sequence used.

Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy.

Objective assessment of deformable image registration (DIR) accuracy often relies on the identification of anatomical landmarks in image pairs, a manual process known to be extremely time-expensive. The goal of this study is to propose a method to automatically detect vessel bifurcations in images and assess their use for the computation of target registration errors (TREs). Three image datasets were retrospectively analyzed. The first dataset included 10 pairs of inhale/exhale phases from lung 4DCTs and full inhale and exhale breath-hold CT scans from 10 patients presenting with chronic obstructive pulmonary disease, with 300 corresponding landmarks available for each case (DIR-Lab). The second dataset included six pairs of inhale/exhale phases from lung 4DCTs (POPI dataset), with 100 pairs of landmarks for each case. The third dataset included 28 pairs of pre/post-radiotherapy liver contrast-enhanced CT scans, each with five manually picked vessel bifurcation correspondences. For all images, the vasculature was autosegmented by computing and thresholding a vesselness image. Images of the vasculature centerline were computed, and bifurcations were detected based on centerline voxel neighbors' count. The vasculature segmentations were independently registered using a Demons algorithm between representations of their surface with distance maps. Detected bifurcations were considered as corresponding when distant by less than 5mm after vasculature DIR. The selected pairs of bifurcations were used to calculate TRE after registration of the images considering three algorithms: rigid registration, Anaconda, and a Demons algorithm. For comparison with the ground truth, TRE values calculated using the automatically detected correspondences were interpolated in the whole organs to generate TRE maps. The performance of the method in automatically calculating TRE after image registration was quantified by measuring the correlation with the TRE obtained when using the ground truth landmarks. The median Pearson correlation coefficients between ground truth TRE and corresponding values in the generated TRE maps were r=0.81 and r=0.67 for the lung and liver cases, respectively. The correlation coefficients between mean TRE for each case were r=0.99 and r=0.64 for the lung and liver cases, respectively. For lungs or liver CT scans DIR, a strong correlation was obtained between TRE calculated using manually picked or landmarks automatically detected with the proposed method. This tool should be particularly useful in studies requiring assessing the reliability of a high number of DIRs.

Mass spectrometry imaging-based multi-modal technique: Next-generation of biochemical analysis strategy

Mass spectrometry imaging-based multi-modal technique: Next-generation of biochemical analysis strategy

Functional magnetic resonance imaging progressive deformable registration based on a cascaded convolutional neural network.

Intersubject registration of functional magnetic resonance imaging (fMRI) is necessary for group analysis. Accurate image registration can significantly improve the results of statistical analysis. Traditional methods are achieved by using high-resolution structural images or manually extracting functional information. However, structural alignment does not necessarily lead to functional alignment, and manually extracting functional features is complicated and time-consuming. Recent studies have shown that deep learning-based methods can be used for deformable image registration. We proposed a deep learning framework with a three-cascaded multi-resolution network (MR-Net) to achieve deformable image registration. MR-Net separately extracts the features of moving and fixed images via a two-stream path, predicts a sub-deformation field, and is cascaded three times. The moving and fixed images' deformation field is composed of all sub-deformation fields predicted by the MR-Net. We imposed large smoothness constraints on all sub-deformation fields to ensure their smoothness. Our proposed architecture can complete the progressive registration process to ensure the topology of the deformation field. We implemented our method on the 1000 Functional Connectomes Project (FCP) and Eyes Open Eyes Closed fMRI datasets. Our method increased the peak t values in six brain functional networks to 19.8, 17.8, 15.0, 16.4, 17.0, and 13.2. Compared with traditional methods [i.e., FMRIB Software Library (FSL) and Statistical Parametric Mapping (SPM)] and deep learning networks [i.e., VoxelMorph (VM) and Volume Tweening Network (VTN)], our method improved 47.58%, 11.88%, 18.60%, and 15.16%, respectively. Our three-cascaded MR-Net can achieve statistically significant improvement in functional consistency across subjects.

Low dose cone beam CT for paediatric image-guided radiotherapy: Image quality and practical recommendations

PurposeCone beam CT (CBCT) is used in paediatric image-guided radiotherapy (IGRT) for patient setup and internal anatomy assessment. Adult CBCT protocols lead to excessive doses in children, increasing the risk of radiation-induced malignancies. Reducing imaging dose increases quantum noise, degrading image quality. Patient CBCTs also include ‘anatomical noise’ (e.g. motion artefacts), further degrading quality. We determine noise contributions in paediatric CBCT, recommending practical imaging protocols and thresholds above which increasing dose yields no improvement in image quality. Methods and materialsSixty CBCTs including the thorax or abdomen/pelvis from 7 paediatric patients (aged 6–13 years) were acquired at a range of doses and used to simulate lower dose scans, totalling 192 scans (0.5–12.8 mGy). Noise measured in corresponding regions of each patient and a 10-year-old phantom were compared, modelling total (including anatomical) noise, and quantum noise contributions as a function of dose. Contrast-to-noise ratio (CNR) was measured between fat/muscle. Soft tissue registration was performed on the kidneys, comparing accuracy to the highest dose scans. ResultsQuantum noise contributed <20% to total noise in all cases, suggesting anatomical noise is the largest determinant of image quality in the abdominal/pelvic region. CNR exceeded 3 in over 90% of cases ≥ 1 mGy, and 57% of cases at 0.5 mGy. Soft tissue registration was accurate for doses > 1 mGy. ConclusionAnatomical noise dominates quantum noise in paediatric CBCT. Appropriate soft tissue contrast and registration accuracy can be achieved for doses as low as 1 mGy. Increasing dose above 1 mGy holds no benefit in improving image quality or registration accuracy due to the presence of anatomical noise.

Geometric and dosimetric accuracy of deformable image registration between average-intensity images for 4DCT-based adaptive radiotherapy for non-small cell lung cancer.

PurposeRe‐planning for four‐dimensional computed tomography (4DCT)‐based lung adaptive radiotherapy commonly requires deformable dose mapping between the planning average‐intensity image (AVG) and the newly acquired AVG. However, such AVG‐AVG deformable image registration (DIR) lacks accuracy assessment. The current work quantified and compared geometric accuracies of AVG‐AVG DIR and corresponding phase‐phase DIRs, and subsequently investigated the clinical impact of such AVG‐AVG DIR on deformable dose mapping.Methods and MaterialsHybrid intensity‐based AVG‐AVG and phase‐phase DIRs were performed between the planning and mid‐treatment 4DCTs of 28 non‐small cell lung cancer patients. An automated landmark identification algorithm detected vessel bifurcation pairs in both lungs. Target registration error (TRE) of these landmark pairs was calculated for both DIR types. The correlation between TRE and respiratory‐induced landmark motion in the planning 4DCT was analyzed. Global and local dose metrics were used to assess the clinical implications of AVG‐AVG deformable dose mapping with both DIR types.ResultsTRE of AVG‐AVG and phase‐phase DIRs averaged 3.2 ± 1.0 and 2.6 ± 0.8 mm respectively (p < 0.001). Using AVG‐AVG DIR, TREs for landmarks with <10 mm motion averaged 2.9 ± 2.0 mm, compared to 3.1 ± 1.9 mm for the remaining landmarks (p < 0.01). Comparatively, no significant difference was demonstrated for phase‐phase DIRs. Dosimetrically, no significant difference in global dose metrics was observed between doses mapped with AVG‐AVG DIR and the phase‐phase DIR, but a positive linear relationship existed (p = 0.04) between the TRE of AVG‐AVG DIR and local dose difference.ConclusionsWhen the region of interest experiences <10 mm respiratory‐induced motion, AVG‐AVG DIR may provide sufficient geometric accuracy; conversely, extra attention is warranted, and phase‐phase DIR is recommended. Dosimetrically, the differences in geometric accuracy between AVG‐AVG and phase‐phase DIRs did not impact global lung‐based metrics. However, as more localized dose metrics are needed for toxicity assessment, phase‐phase DIR may be required as its lower mean TRE improved voxel‐based dosimetry.

Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation

Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images. Firstly, we propose a multi-slice LRTD scheme to recover the underlying low-rank structure embedded in 3D medical images. It performs the LRTD on small image segments consisting of multiple consecutive image slices. Then, we present an LRTD-based atlas construction method to generate tumor-free liver atlases that mitigates the performance degradation of liver segmentation due to the presence of tumors. Finally, we introduce an LRTD-based MAS algorithm to derive patient-specific liver atlases for each test image, and to achieve accurate pairwise image registration and label propagation. Extensive experiments on three public databases of pathological liver cases validate the effectiveness of the proposed method. Both qualitative and quantitative results demonstrate that, in the presence of major pathology, the proposed method is more accurate and robust than state-of-the-art methods.

The effect of camera settings on image noise and accuracy of subpixel image registration

The effect of camera settings on image noise and accuracy of subpixel image registration

Effects of Exposure Parameters and Voxel Size for Cone-Beam Computed Tomography on the Image Matching Accuracy with an Optical Dental Scan Image: An In Vitro Study

This study is aimed at assessing the effects of exposure parameters and voxel size for cone-beam computed tomography (CBCT) on the image matching accuracy with an optical dental scan image. CBCT and optical scan images of a dry human mandible were obtained. Different CBCT settings were used: tube voltage, 60, 80, and 100 kVp; tube current, 6 and 8 mA; and voxel size, 100, 200, and 300 μm. Image matching between the CBCT and optical scan images was performed using implant planning software by dental professionals (n = 18). The image matching accuracy in each combination of CBCT settings was evaluated by assessing the linear discrepancy between the three-dimensionally reconstructed radiological image and the registered optical scan image using an image analysis software program. The Kruskal-Wallis test and a post hoc Mann–Whitney U test with Bonferroni correction were used to compare the accuracy of image registration between the groups (α = 0.05). Overall, the image matching accuracy was not significantly different between tube voltage and current settings; however, significantly higher image registration errors were found at the combination of 100 kVp tube voltage/8 mA tube current (F = 8.44, P < 0.001). Changes in voxel sizes did not significantly interfere with the image registration results. No interaction was found among voltage, current, and voxel size in terms of image registration accuracy (F = 2.022, P = 0.091). Different exposure parameter settings in tube voltage and tube current did not significantly influence the image matching accuracy between CBCT and optical dental scan images; however, a high radiation dose could be inappropriate. The image matching accuracy was not significantly affected by changing the voxel sizes of CBCT.

UNSUPERVISED MULTIMODAL IMAGE REGISTRATION WITH ADAPTATIVE GRADIENT GUIDANCE.

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which elegantly leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.

HNSF Log‐Demons: Diffeomorphic demons registration using hierarchical neighbourhood spectral features

Many biomedical applications require accurate non-rigid image registration that can cope with complex deformations. However, popular diffeomorphic Demons registration algorithms suffer from difficulties for complex and serious distortions since they only use image greyscale and gradient information. To address these difficulties, a new diffeomorphic Demons registration algorithm is proposed using hierarchical neighbourhood spectral features namely HNSF Log-Demons in this paper. In view of three important properties of hierarchical neighbourhood spectral features based on line graph such as rotation invariance, invariance of linear changes of brightness, and robustness to noise, the hierarchical neighbourhood spectral features of a reference image and a moving image is first extracted and these novel spectral features are incorporated into the energy function of the diffeomorphic registration framework to improve the capability of capturing complex distortions. Secondly, the Nystr o ̈ m approximation based on random singular value decomposition is employed to effectively enhance the computational efficiency of HNSF Log-Demons. Finally, the hybrid multi-resolution strategy based on wavelet decomposition in the registration process is utilised to further improve the registration accuracy and efficiency. Experimental results show that the proposed HNSF Log-Demons not only effectively ensures the generation of smooth and reversible deformation field, but also achieves better performance than state-of-the-art algorithms.

In phantom evaluation of targeting accuracy in MRI-based brain radiosurgery.

To determine the targeting accuracy of brain radiosurgery when planning procedures employing different MRI and MRI+CT combinations are adopted. A new phantom, the BrainTool, has been designed and realized to test image co-registration and targeting accuracy in a realistic anatomical situation. The phantom was created with a 3D printer and materials that mimic realistic brain MRI and CT contrast using a model extracted from a synthetic MRI study of a human brain. Eight markers distributed within the BrainTool provide for assessment of the accuracy of image registrations while two cavities that host an ionization chamber are used to perform targeting accuracy measurements with an iterative cross-scan method. Two procedures employing 1.5T MRI-only or a combination of MRI (taken with 1.5T or 3T scanners) and CT to carry out Gamma Knife treatments were investigated. As distortions can impact targeting accuracy, MR images were preliminary evaluated to assess image deformation extent using GammaTool phantom. MR images taken with both scanners showed average and maximum distortion of 0.3mm and 1mm respectively. The marker distances in co-registered images resulted below 0.5mm for both MRI scans. The targeting mismatches obtained were 0.8mm, 1.0mm and 1.2mm for MRI-only and MRI+CT (1,5T and 3T), respectively. Procedures using a combination of MR and CT images provide targeting accuracies comparable to those of MRI-only procedures. The BrainTool proved to be a suitable tool for carrying out co-registration and targeting accuracy of Gamma Knife brain radiosurgery treatments.

Elastic Image Fusion Software to Coregister Preoperatively Planned Pedicle Screws With Intraoperative Computed Tomography Data for Image-Guided Spinal Surgery.

For complex spinal cases, especially when robotic guidance is used, preoperative planning of pedicle screws can be helpful. Transfer of these preoperatively planned pedicle screws to intraoperative 3-dimensional imaging is challenging because of changes in anatomic alignment between preoperative supine and intraoperative prone imaging, especially when multiple levels are involved. In the spine, where each individual vertebra is subject to independent movement from adjacent level, rigid image fusion is confined to a single vertebra and can display fusion inaccuracies on adjacent levels. A novel elastic fusion algorithm is introduced to overcome these disadvantages. This study aimed to investigate image registration accuracy of preoperatively planned pedicle screws with an elastic fusion algorithm vs. rigid fusion for intraoperative placement with image-guided surgery. A total of 12 patients, were selected depending on the availability of a preoperative spinal computed tomography (CT) and an intraoperative AIRO CT scan (BrainLAB AG, Munich, Germany) of the same spinal region. To verify accuracy differences between rigid fusion and elastic fusion 76 bilateral screw trajectories were virtually defined in the preoperative CT image, and they were transferred via either rigid fusion or elastic fusion to the intraoperative CT scan. Accuracy of the transferred screws in the rigid and elastic fusion group was determined by measuring pedicle breaches on the intraoperative CT. In the rigid fusion group 1.3% of screws showed a breach of less than 2 mm, 9.2% showed breaches between 2 and 4 mm, and 18.4% of the screws showed an error above 4 mm. The elastic fusion group showed no breaches and provided high accuracy between preoperative and intraoperative screw placement. Elastic fusion provides high registration accuracy and represents a considerable step towards efficiency and safety in CT-based image-guided surgery. 3.