A multi-vertebrae CT to US registration of the lumbar spine in clinical data

Shape completion in the dark: completing vertebrae morphology from 3D ultrasound

PURPOSE

Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures.

METHODS

We introduce a point cloud-based probabilistic deep learning (DL) method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts.

RESULTS

The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in Chamfer Distance (CD), respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomical landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to ground truth (GT) of 4.96 mm), are preserved in the 3D completion.

CONCLUSION

Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomical landmarks and reconstructs crucial injections sites at their correct locations.

Anatomy-aware computed tomography-to-ultrasound spine registration

BACKGROUND

Ultrasound (US) has demonstrated to be an effective guidance technique for lumbar spine injections, enabling precise needle placement without exposing the surgeon or the patient to ionizing radiation. However, noise and acoustic shadowing artifacts make US data interpretation challenging. To mitigate these problems, many authors suggested using computed tomography (CT)-to-US registration to align the spine in pre-operative CT to intra-operative US data, thus providing localization of spinal landmarks.

PURPOSE

In this paper, we propose a deep learning (DL) pipeline for CT-to-US registration and address the problem of a need for annotated medical data for network training. Firstly, we design a data generation method to generate paired CT-US data where the spine is deformed in a physically consistent manner. Secondly, we train a point cloud (PC) registration network using anatomy-aware losses to enforce anatomically consistent predictions.

METHODS

Our proposed pipeline relies on training the network on realistic generated data. In our data generation method, we model the properties of the joints and disks between vertebrae based on biomechanical measurements in previous studies. We simulate the supine and prone position deformation by applying forces on the spine models. We choose the spine models from 35 patients in VerSe dataset. Each spine is deformed 10 times to create a noise-free data with ground-truth segmentation at hand. In our experiments, we use one-leave-out cross-validation strategy to measure the performance and the stability of the proposed method. For each experiment, we choose generated PCs from three spines as the test set. From the remaining, data from 3 spines act as the validation set and we use the rest of the data for training the algorithm. To train our network, we introduce anatomy-aware losses and constraints on the movement to match the physics of the spine, namely, rigidity loss and bio-mechanical loss. We define rigidity loss based on the fact that each vertebra can only transform rigidly while the disks and the surrounding tissue are deformable. Second, by using bio-mechanical loss we stop the network from inferring extreme movements by penalizing the force needed to get to a certain pose.

RESULTS

To validate the effectiveness of our fully automated data generation pipeline, we qualitatively assess the fidelity of the generated data. This assessment involves verifying the realism of the spinal deformation and subsequently confirming the plausibility of the simulated ultrasound images. Next, we demonstrate that the introduction of the anatomy-aware losses brings us closer to state-of-the-art (SOTA) and yields a reduction of 0.25 mm in terms of target registration error (TRE) compared to using only mean squared error (MSE) loss on the generated dataset. Furthermore, by using the proposed losses, the rigidity loss in inference decreases which shows that the inferred deformation respects the rigidity of the vertebrae and only introduces deformations in the soft tissue area to compensate the difference to the target PC. We also show that our results are close to the SOTA for the simulated US dataset with TRE of 3.89 mm and 3.63 mm for the proposed method and SOTA respectively. In addition, we show that our method is more robust against errors in the initialization in comparison to SOTA and significantly achieves better results (TRE of 4.88 mm compared to 5.66 mm) in this experiment.

CONCLUSIONS

In conclusion, we present a pipeline for spine CT-to-US registration and explore the potential benefits of utilizing anatomy-aware losses to enhance registration results. Additionally, we propose a fully automatic method to synthesize paired CT-US data with physically consistent deformations, which offers the opportunity to generate extensive datasets for network training. The generated dataset and the source code for data generation and registration pipeline can be accessed via https://github.com/mfazampour/medphys_ct_us_registration.

Ultrasound-based navigated pedicle screw insertion without intraoperative radiation: feasibility study on porcine cadavers

BACKGROUND

Navigation systems for spinal fusion surgery rely on intraoperative computed tomography (CT) or fluoroscopy imaging. Both expose patient, surgeons and operating room staff to significant amounts of radiation. Alternative methods involving intraoperative ultrasound (iUS) imaging have recently shown promise for image-to-patient registration. Yet, the feasibility and safety of iUS navigation in spinal fusion have not been demonstrated.

PURPOSE

To evaluate the accuracy of pedicle screw insertion in lumbar and thoracolumbar spinal fusion using a fully automated iUS navigation system.

STUDY DESIGN

Prospective porcine cadaver study.

METHODS

Five porcine cadavers were used to instrument the lumbar and thoracolumbar spine using posterior open surgery. During the procedure, iUS images were acquired and used to establish automatic registration between the anatomy and preoperative CT images. Navigation was performed with the preoperative CT using tracked instruments. The accuracy of the system was measured as the distance of manually collected points to the preoperative CT vertebral surface and compared against fiducial-based registration. A postoperative CT was acquired, and screw placements were manually verified. We report breach rates, as well as axial and sagittal screw deviations.

RESULTS

A total of 56 screws were inserted (5.50 mm diameter n=50, and 6.50 mm diameter n=6). Fifty-two screws were inserted safely without breach. Four screws (7.14%) presented a medial breach with an average deviation of 1.35±0.37 mm (all <2 mm). Two breaches were caused by 6.50 mm diameter screws, and two by 5.50 mm screws. For vertebrae instrumented with 5.50 mm screws, the average axial diameter of the pedicle was 9.29 mm leaving a 1.89 mm margin in the left and right pedicle. For vertebrae instrumented with 6.50 mm screws, the average axial diameter of the pedicle was 8.99 mm leaving a 1.24 mm error margin in the left and right pedicle. The average distance to the vertebral surface was 0.96 mm using iUS registration and 0.97 mm using fiducial-based registration.

CONCLUSIONS

We successfully implanted all pedicle screws in the thoracolumbar spine using the ultrasound-based navigation system. All breaches recorded were minor (<2 mm) and the breach rate (7.14%) was comparable to existing literature. More investigation is needed to evaluate consistency, reproducibility, and performance in surgical context.

CLINICAL SIGNIFICANCE

Intraoperative US-based navigation is feasible and practical for pedicle screw insertion in a porcine model. It might be used as a low-cost and radiation-free alternative to intraoperative CT and fluoroscopy in the future.

3D ultrasound navigation system for screw insertion in posterior spine surgery: a phantom study

PURPOSE

Posterior spinal fusion surgery is required to correct severe idiopathic scoliosis. The surgery involves insertion of screws which requires high accuracy to prevent neurologic damage to the spinal cord. Although conventional CT navigation can reduce this risk, 3D-ultrasound-based navigation could achieve this without added ionizing radiation and usage of expensive and bulky equipment. This study aimed to evaluate the accuracy of a 3D ultrasound navigation system for posterior spine surgery.

METHODS

A custom 3D ultrasound (3DUS) with model-to-surface registration algorithm was developed and integrated into a 3D navigation environment. A CT scan of an adolescent spine (T3-T11) was segmented and 3D printed for experiments. A probe with reflective markers was placed in vertebral pedicles 684 times in varying levels, positions in the capture space and orientation of vertebra, and the entrypoint and trajectory accuracies were measured.

RESULTS

Among 684 probe placements in vertebral levels T3 to T11 in the phantom spine, 95.5% were within 1 mm and 5° of accuracy, with an average accuracy of 0.4 ± 0.4 mm and 2.1 ± 0.9°, requiring 8.8 s to process. Accuracies were statistically significantly affected by vertebral orientation and position in the capture volume, though this was still within the targeted accuracies of 1 mm and 5°.

CONCLUSION

This preliminary ultrasound-based navigation system is accurate and fast enough for guiding placement of pedicle screws into the spine in posterior fusion surgery. The current results are limited to phantom spines, and future study in animal or human cadavers is needed to investigate soft tissue effects on registration accuracy.

Open-source software for ultrasound-based guidance in spinal fusion surgery

Spinal instrumentation and surgical manipulations may cause loss of navigation accuracy requiring an efficient re-alignment of the patient anatomy with pre-operative images during surgery. While intra-operative ultrasound (iUS) guidance has shown clear potential to reduce surgery time, compared with clinical computed tomography (CT) guidance, rapid registration aiming to correct for patient misalignment has not been addressed. In this article, we present an open-source platform for pedicle screw navigation using iUS imaging. The alignment method is based on rigid registration of CT to iUS vertebral images and has been designed for fast and fully automatic patient re-alignment in the operating room. Two steps are involved: first, we use the iUS probe's trajectory to achieve an initial coarse registration; then, the registration transform is refined by simultaneously optimizing gradient orientation alignment and mean of iUS intensities passing through the CT-defined posterior surface of the vertebra. We evaluated our approach on a lumbosacral section of a porcine cadaver with seven vertebral levels. We achieved a median target registration error of 1.47 mm (100% success rate, defined by a target registration error <2 mm) when applying the probe's trajectory initial alignment. The approach exhibited high robustness to partial visibility of the vertebra with success rates of 89.86% and 88.57% when missing either the left or right part of the vertebra and robustness to initial misalignments with a success rate of 83.14% for random starts within ±20° rotation and ±20 mm translation. Our graphics processing unit implementation achieves an efficient registration time under 8 s, which makes the approach suitable for clinical application.

The state-of-the-art in ultrasound-guided spine interventions

During the last two decades, intra-operative ultrasound (iUS) imaging has been employed for various surgical procedures of the spine, including spinal fusion and needle injections. Accurate and efficient registration of pre-operative computed tomography or magnetic resonance images with iUS images are key elements in the success of iUS-based spine navigation. While widely investigated in research, iUS-based spine navigation has not yet been established in the clinic. This is due to several factors including the lack of a standard methodology for the assessment of accuracy, robustness, reliability, and usability of the registration method. To address these issues, we present a systematic review of the state-of-the-art techniques for iUS-guided registration in spinal image-guided surgery (IGS). The review follows a new taxonomy based on the four steps involved in the surgical workflow that include pre-processing, registration initialization, estimation of the required patient to image transformation, and a visualization process. We provide a detailed analysis of the measurements in terms of accuracy, robustness, reliability, and usability that need to be met during the evaluation of a spinal IGS framework. Although this review is focused on spinal navigation, we expect similar evaluation criteria to be relevant for other IGS applications.

Spine Surgery Supported by Augmented Reality

STUDY DESIGN

A prospective, case-based, observational study.

OBJECTIVES

To investigate how microscope-based augmented reality (AR) support can be utilized in various types of spine surgery.

METHODS

In 42 spinal procedures (12 intra- and 8 extradural tumors, 7 other intradural lesions, 11 degenerative cases, 2 infections, and 2 deformities) AR was implemented using operating microscope head-up displays (HUDs). Intraoperative low-dose computed tomography was used for automatic registration. Nonlinear image registration was applied to integrate multimodality preoperative images. Target and risk structures displayed by AR were defined in preoperative images by automatic anatomical mapping and additional manual segmentation.

RESULTS

AR could be successfully applied in all 42 cases. Low-dose protocols ensured a low radiation exposure for registration scanning (effective dose cervical 0.29 ± 0.17 mSv, thoracic 3.40 ± 2.38 mSv, lumbar 3.05 ± 0.89 mSv). A low registration error (0.87 ± 0.28 mm) resulted in a reliable AR representation with a close matching of visualized objects and reality, distinctly supporting anatomical orientation in the surgical field. Flexible AR visualization applying either the microscope HUD or video superimposition, including the ability to selectively activate objects of interest, as well as different display modes allowed a smooth integration in the surgical workflow, without disturbing the actual procedure. On average, 7.1 ± 4.6 objects were displayed visualizing target and risk structures reliably.

CONCLUSIONS

Microscope-based AR can be applied successfully to various kinds of spinal procedures. AR improves anatomical orientation in the surgical field supporting the surgeon, as well as it offers a potential tool for education.

Reliable navigation registration in cranial and spine surgery based on intraoperative computed tomography

OBJECTIVE

Low registration errors are an important prerequisite for reliable navigation, independent of its use in cranial or spinal surgery. Regardless of whether navigation is used for trajectory alignment in biopsy or implant procedures, or for sophisticated augmented reality applications, all depend on a correct registration of patient space and image space. In contrast to fiducial, landmark, or surface matching–based registration, the application of intraoperative imaging allows user-independent automatic patient registration, which is less error prone. The authors’ aim in this paper was to give an overview of their experience using intraoperative CT (iCT) scanning for automatic registration with a focus on registration accuracy and radiation exposure.

METHODS

A total of 645 patients underwent iCT scanning with a 32-slice movable CT scanner in combination with navigation for trajectory alignment in biopsy and implantation procedures (n = 222) and for augmented reality (n = 437) in cranial and spine procedures (347 craniotomies and 42 transsphenoidal, 56 frameless stereotactic, 59 frame-based stereotactic, and 141 spinal procedures). The target registration error was measured using skin fiducials that were not part of the registration procedure. The effective dose was calculated by multiplying the dose length product with conversion factors.

RESULTS

Among all 1281 iCT scans obtained, 1172 were used for automatic patient registration (645 initial registration scans and 527 repeat iCT scans). The overall mean target registration error was 0.86 ± 0.38 mm (± SD) (craniotomy, 0.88 ± 0.39 mm; transsphenoidal, 0.92 ± 0.39 mm; frameless, 0.74 ± 0.39 mm; frame-based, 0.84 ± 0.34 mm; and spinal, 0.80 ± 0.28 mm). Compared with standard diagnostic scans, a distinct reduction of the effective dose could be achieved using low-dose protocols for the initial registration scan with mean effective doses of 0.06 ± 0.04 mSv for cranial, 0.50 ± 0.09 mSv for cervical, 4.12 ± 2.13 mSv for thoracic, and 3.37 ± 0.93 mSv for lumbar scans without impeding registration accuracy.

CONCLUSIONS

Reliable automatic patient registration can be achieved using iCT scanning. Low-dose protocols ensured a low radiation exposure for the patient. Low-dose scanning had no negative effect on navigation accuracy.

Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions

In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. A variance and octree based feature point-set selection method is used for selecting distinct and evenly spread point locations for block-matching. Geometric consistency and smoothness criteria are imposed in an outlier rejection step to refine the block-matching results. The block-matching results after outlier rejection are used to determine the affine transformation between the computed tomography and the ultrasound volumes. Various experiments are carried out to assess the optimal performance and the influence of parameters on accuracy and computational time of the registration. A leave-one-patient-out cross-validation registration error of 3.6 mm is achieved over 29 datasets, acquired from 17 patients.

Comparison of Patient Position and Midline Lumbar Neuraxial Access Via Statistical Model Registration to Ultrasound

Patient positioning and needle puncture site are important for lumbar neuraxial anesthesia. We sought to identify optimal patient positioning and puncture sites with a novel ultrasound registration. We registered a statistical model to volumetric ultrasound data acquired from volunteers (n = 10) in three positions: (i) prone; (ii) seated with thoracic and lumbar flexion; and (iii) seated as in position ii, with a 10° dorsal tilt. We determined injection target size and penetration success by simulating lumbar injections on validated registered models. Injection window and target area sizes in seated positions were significantly larger than those in prone positions by 65% in L2-3 and 130% in L3-4; a 10° tilt had no significant effect on target sizes between seated positions. In agreement with computed tomography studies, simulated L2-3 and L3-4 injections had the highest success at the 50% and 75% midline puncture sites, respectively, measured from superior to inferior spinous process. We conclude that our registration to ultrasound technique is a potential tool for tolerable determination of puncture site success in vivo.

Three-Dimensional Registration of Freehand-Tracked Ultrasound to CT Images of the Talocrural Joint

A rigid surface⁻volume registration scheme is presented in this study to register computed tomography (CT) and free-hand tracked ultrasound (US) images of the talocrural joint. Prior to registration, bone surfaces expected to be visible in US are extracted from the CT volume and bone contours in 2D US data are enhanced based on monogenic signal representation of 2D US images. A 3D monogenic signal data is reconstructed from the 2D data using the position of the US probe recorded with an optical tracking system. When registering the surface extracted from the CT scan to the monogenic signal feature volume, six transformation parameters are estimated so as to optimize the sum of monogenic signal features over the transformed surface. The robustness of the registration algorithm was tested on a dataset collected from 12 cadaveric ankles. The proposed method was used in a clinical case study to investigate the potential of US imaging for pre-operative planning of arthroscopic access to talar (osteo)chondral defects (OCDs). The results suggest that registrations with a registration error of 2 mm and less is achievable, and US has the potential to be used in assessment of an OCD' arthroscopic accessibility, given the fact that 51% of the talar surface could be visualized.

Toward dynamic lumbar puncture guidance using needle-based single-element ultrasound imaging

Lumbar punctures (LPs) are interventional procedures that are used to collect cerebrospinal fluid. Since the target window is small, physicians have limited success conducting the procedure. The procedure is especially difficult for obese patients due to the increased distance between bone and skin surface. We propose a simple and direct needle insertion platform, enabling image formation by sweeping a needle with a single ultrasound element at the tip. The needle-shaped ultrasound transducer can not only sense the distance between the tip and a potential obstacle, such as bone, but also visually locate the structures by combining transducer location tracking and synthetic aperture focusing. The concept of the system was validated through a simulation that revealed robust image reconstruction under expected errors in tip localization. The initial prototype was built into a 14 G needle and was mounted on a holster equipped with a rotation shaft allowing one degree-of-freedom rotational sweeping and a rotation tracking encoder. We experimentally evaluated the system using a metal-wire phantom mimicking high reflection bone structures and human spinal bone phantom. Images of the phantoms were reconstructed, and the synthetic aperture reconstruction improved the image quality. These results demonstrate the potential of the system to be used as a real-time guidance tool for improving LPs.

Random Forest-Based Bone Segmentation in Ultrasound

Ultrasound (US) imaging is a safe alternative to radiography for guidance during minimally invasive orthopedic procedures. However, ultrasound is challenging to interpret because of the relatively low signal-to-noise ratio and its inherent speckle pattern that decreases image quality. Here we describe a method for automatic bone segmentation in 2-D ultrasound images using a patch-based random forest classifier and several ultrasound specific features, such as shadowing. We illustrate that existing shadow features are not robust to changes in US acquisition parameters, and propose a novel robust shadow feature. We evaluate the method on several US data sets and report that it favorably compares with existing techniques. We achieve a recall of 0.86 at a precision of 0.82 on a test set of 143 spinal US images.

Ultrasound Aided Vertebral Level Localization for Lumbar Surgery

Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes of the vertebrae. The spinous processes are pre-operatively outlined and labeled in a lateral lumbar X-ray of the patient. Then, in the OR the spinous processes are imaged with 2-D sagittal US, and are automatically segmented and registered with the X-ray shapes. After a small number of scanned vertebrae, the system robustly matches the shapes, and propagates the X-ray label to the US images. The main contributions of our work are: we propose a deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed. We present a matching strategy that determines the levels of the spinal processes being imaged. And lastly, we evaluate the complete procedure on 19 clinical data sets from two hospitals, and two observers. The final labeling was correct in 92% of the cases, demonstrating the feasibility of US-based surgical entry point detection for spinal surgeries.

Intraoperative application of hand-held structured light scanning: a feasibility study

PURPOSE

Structured light scanning is an emerging technology that shows potential in the field of medical imaging and image-guided surgery. The purpose of this study was to investigate the feasibility of applying a hand-held structured light scanner in the operating theatre as an intraoperative image modality and registration tool.

METHODS

We performed an in vitro study with three fresh frozen knee specimens and a clinical pilot study with three patients (one total knee arthroplasty and two hip replacements). Before the procedure, a CT scan of the affected joint was obtained and isosurface models of the anatomies were created. A conventional surgical exposure was performed, and a hand-held structured light scanner (Artec Group, Palo Alto, USA) was used to scan the exposed anatomy. Using the texture information of the scanned model, bony anatomy was selected and registered to the CT models. Registration RMS errors were documented, and distance maps between the scanned model and the CT model were created.

RESULTS

For the in vitro trial, the average RMS error was 1.00 mm for the femur and 1.17 mm for the tibia registration. We found comparable results during clinical trials, with an average RMS error of 1.3 mm.

CONCLUSIONS

The results of this preliminary study indicate that structured light scanning could be applied accurately and safely in a surgical environment. This could result in a variety of applications for these scanners in image-guided interventions as intraoperative imaging and registration tools.

Intraoperative CT as a registration benchmark for intervertebral motion compensation in image-guided open spinal surgery

PURPOSE

An accurate and reliable benchmark of registration accuracy and intervertebral motion compensation is important for spinal image guidance. In this study, we evaluated the utility of intraoperative CT (iCT) in place of bone-implanted screws as the ground-truth registration and illustrated its use to benchmark the performance of intraoperative stereovision (iSV).

METHODS

A template-based, multi-body registration scheme was developed to individually segment and pair corresponding vertebrae between preoperative CT and iCT of the spine. Intervertebral motion was determined from the resulting vertebral pair-wise registrations. The accuracy of the image-driven registration was evaluated using surface-to-surface distance error (SDE) based on segmented bony features and was independently verified using point-to-point target registration error (TRE) computed from bone-implanted mini-screws. Both SDE and TRE were used to assess the compensation accuracy using iSV.

RESULTS

The iCT-based technique was evaluated on four explanted porcine spines (20 vertebral pairs) with artificially induced motion. We report a registration accuracy of 0.57 [Formula: see text] 0.32 mm (range 0.34-1.14 mm) and 0.29 [Formula: see text] 0.15 mm (range 0.14-0.78 mm) in SDE and TRE, respectively, for all vertebrae pooled, with an average intervertebral rotation of [Formula: see text] (range 1.5[Formula: see text]-7.9[Formula: see text]). The iSV-based compensation accuracy for one sample (four vertebrae) was 1.32 [Formula: see text] 0.19 mm and 1.72 [Formula: see text] 0.55 mm in SDE and TRE, respectively, exceeding the recommended accuracy of 2 mm.

CONCLUSION

This study demonstrates the effectiveness of iCT in place of invasive fiducials as a registration ground truth. These findings are important for future development of on-demand spinal image guidance using radiation-free images such as stereovision and ultrasound on human subjects.