Dataset Information

Deformable registration of 3D ultrasound volumes using automatic landmark generation.

ABSTRACT: US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.

SUBMITTER: Figl M

PROVIDER: S-EPMC6420033 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Deformable registration of 3D ultrasound volumes using automatic landmark generation.

Figl Michael M Hoffmann Rainer R Kaar Marcus M Hummel Johann J

PloS one 20190315 3

US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of inte ...[more]

PMID: 30875379

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Project description:PurposeDeformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets.Acquisition and validation methodsThirty CT image pairs were acquired from several publicly available repositories as well as authors' institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4 mm ± 0.3 mm.Data format and usage notesThe data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung-DIR-QA.Potential applicationsThe dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung.

Project description:Accurate patient registration is crucial for effective image-guidance in open cranial surgery. Typically, it is accomplished by matching skin-affixed fiducials manually identified in the operating room (OR) with their counterparts in the preoperative images, which not only consumes OR time and personnel resources but also relies on the presence (and subsequent fixation) of the fiducials during the preoperative scans (until the procedure begins). In this study, the authors present a completely automatic, volumetric image-based patient registration technique that does not rely on fiducials by registering tracked (true) 3D ultrasound (3DUS) directly with preoperative magnetic resonance (MR) images.Multistart registrations between binary 3DUS and MR volumes were first executed to generate an initial starting point without incorporating prior information on the US transducer contact point location or orientation for subsequent registration between grayscale 3DUS and MR via maximization of either mutual information (MI) or correlation ratio (CR). Patient registration was then computed through concatenation of spatial transformations.In ten (N = 10) patient cases, an average fiducial (marker) distance error (FDE) of 5.0 mm and 4.3 mm was achieved using MI or CR registration (FDE was smaller with CR vs MI in eight of ten cases), which are comparable to values reported for typical fiducial- or surface-based patient registrations. The translational and rotational capture ranges were found to be 24.0 mm and 27.0° for binary registrations (up to 32.8 mm and 36.4°), 12.2 mm and 25.6° for MI registrations (up to 18.3 mm and 34.4°), and 22.6 mm and 40.8° for CR registrations (up to 48.5 mm and 65.6°), respectively. The execution time to complete a patient registration was 12-15 min with parallel processing, which can be significantly reduced by confining the 3DUS transducer location to the center of craniotomy in MR before registration (an execution time of 5 min is achievable).Because common features deep in the brain and throughout the surgical volume of interest are used, intraoperative fiducial-less patient registration is possible on-demand, which is attractive in cases where preoperative patient registration is compromised (e.g., from loss∕movement of skin-affixed fiducials) or not possible (e.g., in cases of emergency when external fiducials were not placed in time). CR registration was more robust than MI (capture range about twice as big) and appears to be more accurate, although both methods are comparable to or better than fiducial-based registration in the patient cases evaluated. The results presented here suggest that 3DUS image-based patient registration holds promise for clinical application in the future.

Project description:Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38?mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78?mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.

Dataset Information

Deformable registration of 3D ultrasound volumes using automatic landmark generation.

Publications

Deformable registration of 3D ultrasound volumes using automatic landmark generation.

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