Target Vertebrae Research Articles

Making wrong site surgery a "never event" in spinal deformity surgery by use of a "landmark vertebra" to eliminate variability in identifying a target vertebral level.

Despite the introduction of "standardized counting" methods, errors in counting spinal levels and subsequent wrong-level surgery (WLS)remain critically important patient safety concerns. Previous work by our group has documented inconsistency in the identification of T12 despite the use of these systems including the Spinal Deformity Study Group (SDSG)conventions. To assist with consistent and repeatable identification of proposed preoperative surgical levels, the current study investigates a new strategy: utilization of a "landmark vertebra". It was hypothesized that individuals using a "landmark vertebra" strategy will achieve high concordance with target level identification between distinct time points as compared to conventional methods defining T12. Survey participants analyzed 99 pre-op radiographs, identifying and naming a "landmark vertebra" with concise descriptions like "last bilaterally ribbed vertebra." They then noted the proposed lowest instrumented vertebra's (LIV) distance relative to landmark (i.e., one below landmark). After a waiting period, participants used their written descriptions of the landmark and distance to LIV to reidentify these vertebrae. Cohen's Kappa (k) was used to measure intra-rater agreeability. The landmark strategy was compared to our previous work evaluating consistency in defining T12 based on the SDSG system. All raters showed perfect to near-perfect agreement when re-identifying the landmark and target vertebrae (k = 0.819-1.00; Table1A). Raters at all training levels had higher agreeability in naming the landmark vertebra and target when compared to raters at similar training levels defining T12 (k = 0.34-0.91; Table1B). This high agreement across training demonstrates the strategy's versatility and generalizability. Utilization of a landmark strategy proved to be highly effective in reducing intra-rater variability, with perfect to near-perfect agreement among all raters and consistently higher agreeability when compared to defining T12. Level II-prospective survey.

Nine-grid Area Division Method: A New Ideal Bone Puncture Region for Percutaneous Vertebroplasty in Lumbar Spine.

Percutaneous vertebroplasty (PVP) is widely recognized as an efficacious intervention for alleviating low back pain resulting from osteoporotic vertebral compression fractures. The ideal bone puncture point is conventionally situated at the projection "left 10 points, right 2 points" of the pedicle in the lumbar spine. Determining the optimal bone puncture point represents a critical and complex challenge. The accuracy of percutaneous vertebroplasty (PVP) is primarily influenced by the proficiency of the operating surgeons and the utilization of multiple fluoroscopes during the conventional procedure. Incidences of puncture-related complications have been documented globally. In an effort to enhance the precision of the surgical technique and reduce the occurrence of puncture-related complications, our team applied the "Nine-grid Area Division Method" for PVP in the lumbar spine to modify the traditional procedure. There is potential to decrease the number of puncture times, the radiation exposure dosage, and the duration of surgical procedures. This protocol introduces the definition of the "Nine-grid Area Division Method" and describes the process of modeling target vertebrae DICOM imaging data within medical imaging processing software, simulating operations within a 3-D model, refining the 3-D model using reverse engineering production software, reconstructing the vertebral engineering model within 3-D modeling design software, and utilizing surgical data to determine safe entry regions for pedicle projection. By employing this methodology, surgeons can effectively identify appropriate puncture points with precision and ease, thereby reducing the intricacies associated with puncturing and enhancing the overall accuracy of surgical procedures.

A retrospective cohort study on the efficacy and safety of percutaneous vertebroplasty combined with bone-filling mesh container in vertebral metastases with posterior wall defect.

To evaluate the clinical safety and efficacy of percutaneous vertebroplasty (PVP) combined with bone-filling mesh containers (BFMCs) for vertebral metastases with posterior wall defect. From January 2019 to December 2021, patients with vertebral metastases and posterior wall defect who received BFMCs combined with PVP were included. The visual analog scale (VAS) scores and Oswestry disability index (ODI) scores were evaluated before and 72 hours after the operation, respectively. Post-operational X-ray and computed tomography (CT) scans were conducted to observe bone cement leakage, and complications were recorded. Follow-up CT and magnetic resonance imaging (MRI) were conducted to evaluate the condition of the operated vertebrae and the recurrence or progression of other bone metastases. A total of 43 patients with 44 operated vertebrae were included. All patients successfully completed the surgery. The average VAS score decreased from 7.35 ± 0.78 to 1.63 ± 0.93 (p < 0.05), and the ODI score decreased from 80.06 ± 8.91 to 32.5 ± 4.87 (p < 0.05). Bone cement leakage was observed in 18 operated vertebrae, which were all asymptomatic. No intraspinal leakage, post-operative spinal nerve compression, pulmonary embolism, or other serious complications were recorded. A total of 21 patients had a follow-up of more than 1 year, with no operated vertebral progression, 13 target vertebrae showed obvious sclerosis and necrosis, and no adjacent pathological fracture occurred. Of these patients, 16 had different degrees of bone metastasis of other sites other than the operated vertebrae. For spinal metastases with posterior wall defect, PVP combined with BFMCs was highly safe and can effectively relieve pain for patients. A 1-year follow-up showed a local antitumor effect.

Radiographic age estimation based on degenerative changes of vertebrae.

Age estimation is an important component of decedent identification. When assessing adult remains, anthropologists frequently use gross examination of skeletal elements, such as clavicles, ribs, and pubic symphyses. For fleshed bodies, this requires the removal of these elements and maceration prior to analysis. A new method was developed using radiographic imaging to estimate age from degenerative changes of the lower thoracic and upper lumbar vertebrae. This technique will complement anthropological age estimation methods in young and middle-aged adults and may serve as a stand-alone method for older individuals. Digital radiographs from 240 medical examiner cases were evaluated. The sample included 120 females and 120 males between the ages of 18 and 101 years. A 3-phased scoring system was used for the target vertebrae. Transition analysis was conducted on binned average scores and a Bayesian approach was used to assign age intervals. At the 90% credible interval, individuals in Bin 1 were under 36 years of age while those in Bin 3 were over 47 years of age. Individuals in Bin 2 showed too much age variation to be informative. No significant differences were found between males and females. These findings will be especially useful in the age estimation of older adults and may eliminate the need for skeletal sampling in medicolegal cases where advanced degenerative changes are radiographically observed in the lower thoracic and/or upper lumbar vertebrae. This method was developed for use on fleshed individuals but may also be applicable to skeletonized remains.

Deep Learning-Based Automated Quality Assurance for Palliative Spinal Treatment Planning in Radiotherapy

Robustquality assurance (QA) for palliative spine radiation therapy (RT) remains critical due to the risk of wrong anatomic level treatment on account of human error in enumerating vertebral bodies accurately based on morphology with incomplete imaging of the spine and prevalence of anatomic variants (10%). We propose a rapid, fully automated deep learning-based QA (DL-QA) tool for segmenting and enumerating vertebral structures from image data, capable of identifying misalignment based on discrepancies in calculated dose coverage. Ina retrospective cohort of 514 patients who received palliative spine radiation treatment at a single institution for spinal metastases, vertebral volumes for each individual spine level were automatically segmented on RT planning computed tomography scans using a publicly available deep learning algorithm, Total Segmentator (TS) deployed in the treatment planning system (Wasserthal et al, 2022). Departmental policy requires that the prescription/plan name include all spinal levels that receive a prescribed dosimetric threshold of V50% > 50%. By comparing the intended spine level target in the prescription and plan name against the TS volumes, the DL-QA flagged all cases for which any target vertebrae did not receive this threshold dose and/or any non-targeted vertebrae that received V50% > 50%. To detect spine anomalies, cases were also flagged if any vertebrae volume was not within ±1σ of the entire population of vertebrae volumes. Flagged cases were either categorized as: (1) wrong spine level RT error; (2) documentation error, in which treatment was correct but the prescription/plan name did not follow Departmental policy; or (3) potential spinal geometric error. All flagged cases were verified manually by checking the original images and treatment planning data. Outof 514 patients, 29 cases were flagged as potential errors. Manual review revealed that one of these was a previously discovered true treatment error (due to anatomic variant with 4 lumbar bodies) while 10 were treated as intended but showed documentation errors due to variants in the number of vertebral bodies, kyphosis of the spine causing non-targeted vertebrae to appear in the treatment field, or improper observation of the Departmental plan naming policy. The remaining 18 cases were associated with flagged vertebrae volumes. Reviewing those patients, we identified spinal anomalies where TS attempted to account for extra or missing vertebrae (N = 9) and cases where TS made segmentation errors (N = 9). Theproposed automated DL-QA system successfully identified patients with spine anomalies, flagged documentation errors, verified the correct target levels of spine RT treatments, and detected a known misadministration. The next phase will involve prospective testing of the system in a clinical setting upstream of treatment delivery.

Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans.

Bone density measurement is an important examination for the diagnosis and screening of osteoporosis. The aim of this study was to develop a deep learning (DL) system for automatic measurement of bone mineral density (BMD) for osteoporosis screening using low-dose computed tomography (LDCT) images. This retrospective study included 500 individuals who underwent LDCT scanning from April 2018 to July 2021. All images were manually annotated by a radiologist for the cancellous bone of target vertebrae and post-processed using quantitative computed tomography (QCT) software to identify osteoporosis. Patients were divided into the training, validation, and testing sets in a ratio of 6:2:2 using a 4-fold cross validation method. A localization model using faster region-based convolutional neural network (R-CNN) was trained to identify and locate the target vertebrae (T12-L2), then a 3-dimensional (3D) AnatomyNet was trained to finely segment the cancellous bone of target vertebrae in the localized image. A 3D DenseNet was applied for calculating BMD. The Dice coefficient was used to evaluate segmentation performance. Linear regression and Bland-Altman (BA) analyses were performed to compare the calculated BMD values using the proposed system with QCT. The diagnostic performance of the system for osteoporosis and osteopenia was evaluated with receiver operating characteristic (ROC) curve analysis. Our segmentation model achieved a mean Dice coefficient of 0.95, with Dice coefficients greater than 0.9 accounting for 96.6%. The correlation coefficient (R2) and mean errors between the proposed system and QCT in the testing set were 0.967 and 2.21 mg/cm3, respectively. The area under the curve (AUC) of the ROC was 0.984 for detecting osteoporosis and 0.993 for distinguishing abnormal BMD (osteopenia and osteoporosis). The fully automated DL-based system is able to perform automatic BMD calculation for opportunistic osteoporosis screening with high accuracy using LDCT scans.

Clinical outcomes following total en bloc spondylectomy for spinal metastases from lung cancer

BackgroundThe current guidelines for the treatment of non-small cell lung cancer encourage local curative treatment for selected patients with oligometastases. This study evaluated the surgical results of total en bloc spondylectomy (TES) for isolated spinal metastases originating from lung cancer in carefully selected patients. MethodsWe retrospectively reviewed 14 patients (7 men and 7 women) who underwent TES for spinal metastases originating from lung cancer between 2000 and 2017. The primary outcome measure was the postoperative overall survival time. The histological types included adenocarcinoma (n = 12), pleomorphic carcinoma (n = 1), and small cell lung carcinoma (SCLC) (n = 1 patient). We assessed postoperative survival using Kaplan–Meier analysis and the log-rank test. ResultsThe median postoperative survival time was 83.0 months (6–162 months) in 13 patients with non-small cell lung carcinoma (NSCLC) and 6 months in 1 patient with SCLC. The 3-, 5-, and 10-year overall survival rates in patients with NSCLC were 61.5%, 53.8%, and 15.4%, respectively. Poor postoperative performance status (PS) and Frankel grade, and preoperative irradiation to the vertebrae to be resected were significantly associated with short-term survival after TES in patients with NSCLC (p < 0.05). ConclusionsThe surgical results of TES for spinal metastases of lung cancer were relatively favorable among carefully selected patients. TES may be indicated for spinal metastases of lung cancer in patients with controlled primary lung cancer, NSCLC histology, prospect of good postoperative PS, and preferably no irradiation to the target vertebrae.

Augmented Reality Assisted Surgical Navigation System for Epidural Needle Intervention.

An augmented reality (AR)-assisted surgical navigation system was developed for epidural needle intervention. The system includes three components: a virtual reality-based surgical planning software, a patient and tool tracking system, and an AR-based surgical navigation system. A three-dimensional (3D) path plan for the epidural needle was established on the preoperative computed tomography (CT) image. The plan is then registered to the intraoperative space by 3D models of the target vertebrae using skin markers and real-time tracking information. In the procedure, the plan and tracking information are transmitted to the head-mounted display (HMD) through a wireless network such that the device directly visualizes the plan onto the back surface of the patient. The physician determines the entry point and inserts the needle into the target based on the direct visual guidance of the system. An experiment was conducted to validate the system using two torso phantoms that mimic human respiration. The experimental results demonstrated that the time and the number of X-rays required for needle insertion were significantly decreased by the proposed method (43.6±20.55sec, 2.9±1.3times) compared to those of the conventional fluoroscopy-guided approach (124.5 ± 46.7s, 9.3±2.4times), whereas the average targeting errors were similar in both cases. The proposed system may potentially decrease ionizing radiation exposure not only to the patient but also to the medical team.

Three-Dimensional Printing Guide Template Assisted Percutaneous Vertebroplasty (PVP).

Percutaneous vertebroplasty (PVP) is considered an effective treatment for the back pain caused by osteoporotic vertebral compression fracture. The accuracy of PVP mainly depends on the surgeons' experience and multiple fluoroscopes during a traditional procedure. Puncture related complications were reported all over the world. To make the surgical procedure more precise and decrease the rate of puncture-related complications, our team applied a three-dimensional printing guide template to PVP to modify the traditional procedure. This protocol introduces how to model target vertebrae DICOM imaging data into three-dimensions in the software, how to simulate operation in this 3-D model, and how to use all of the surgical data to reconstruct a patient specific template for application. Using this template, surgeons can identify suitable puncture points accurately to improve the accuracy of the operation. The whole protocol includes: 1) diagnosis of the osteoporotic vertebral compression fracture; 2) acquisition of CT imaging of the target vertebra; 3) simulation of the operation in the software; 4) design and fabrication of the 3-D printing guide template; and 5) application of the template into an operation procedure.

Automatic pedicle screw planning using atlas-based registration of anatomy and reference trajectories

An algorithm for automatic spinal pedicle screw planning is reported and evaluated in simulation and first clinical studies. A statistical atlas of the lumbar spine (N = 40 members) was constructed for active shape model (ASM) registration of target vertebrae to an unsegmented patient CT. The atlas was augmented to include ‘reference’ trajectories through the pedicles as defined by a spinal neurosurgeon. Following ASM registration, the trajectories are transformed to the patient CT and accumulated to define a patient-specific screw trajectory, diameter, and length. The algorithm was evaluated in leave-one-out analysis (N = 40 members) and for the first time in a clinical study (N = 5 patients undergoing cone-beam CT (CBCT) guided spine surgery), and in simulated low-dose CBCT images. ASM registration achieved (2.0 ± 0.5) mm root-mean-square-error (RMSE) in surface registration in 96% of cases, with outliers owing to limitations in CT image quality (high noise/slice thickness). Trajectory centerlines were conformant to the pedicle in 95% of cases. For all non-breaching trajectories, automatically defined screw diameter and length were similarly conformant to the pedicle and vertebral body (98.7%, Grade A/B). The algorithm performed similarly in CBCT clinical studies (93% centerline and screw conformance) and was consistent at the lowest dose levels tested. Average runtime in planning five-level (lumbar) bilateral screws (ten trajectories) was (312.1 ± 104.0) s. The runtime per level for ASM registration was (41.2 ± 39.9) s, and the runtime per trajectory was (4.1 ± 0.8) s, suggesting a runtime of ~(45.3 ± 39.9) s with a more fully parallelized implementation. The algorithm demonstrated accurate, automatic definition of pedicle screw trajectories, diameter, and length in CT images of the spine without segmentation. The studies support translation to clinical studies in free-hand or robot-assisted spine surgery, quality assurance, and data analytics in which fast trajectory definition is a benefit to workflow.

Targeted intraspinal injections to assess therapies in rodent models of neurological disorders.

Despite decades of research, pharmacological therapies for spinal cord motor pathologies are limited. Alternatives using macromolecular, viral, or cell-based therapies show early promise. However, introducing these substances into the spinal cord, past the blood-brain barrier, without causing injury is challenging. We describe a technique for intraspinal injection targeting the lumbar ventral horn in rodents. This technique preserves motor performance and has a proven track record of translation into phase 1 and 2 clinical trials in amyotrophic lateral sclerosis (ALS) patients. The procedure, in brief, involves exposure of the thoracolumbar spine and dissection of paraspinous muscles over the target vertebrae. Following laminectomy, the spine is affixed to a stereotactic frame, permitting precise and reproducible injection throughout the lumbar spine. We have used this protocol to inject various stem cell types, primarily human spinal stem cells (HSSCs); however, the injection is adaptable to any candidate therapeutic cell, virus, or macromolecule product. In addition to a detailed procedure, we provide stereotactic coordinates that assist in targeting of the lumbar spine and instructional videos. The protocol takes ~2 h per animal.

Joint Vertebrae Identification and Localization in Spinal CT Images by Combining Short- and Long-Range Contextual Information

Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results. We propose a robust and efficient vertebrae identification and localization system that can inherently learn to incorporate both the short- and long-range contextual information in a supervised manner. To this end, we develop a multi-task 3-D fully convolutional neural network to effectively extract the short-range contextual information around the target vertebrae. For the long-range contextual information, we propose a multi-task bidirectional recurrent neural network to encode the spatial and contextual information among the vertebrae of the visible spine column. We demonstrate the effectiveness of the proposed approach on a challenging data set, and the experimental results show that our approach outperforms the state-of-the-art methods by a significant margin.

Dose Sculpting Intensity Modulated Radiation Therapy for Vertebral Body Sparing in Children With Neuroblastoma

To assess the effect of dose sculpting intensity modulated radiation therapy on vertebral body growth in children with neuroblastoma. From 2000 to 2011, 88 children with neuroblastoma underwent radiation at the authors' institution. Children with paravertebral tumors with at least 3years of evaluable posttreatment imaging were included, and children who underwent spine reirradiation before follow-up were excluded. If vertebral bodies could not be spared, these "target" vertebral bodies were treated to at least 18Gy. Thoracic and lumbar vertebral bodies were assessed separately. Dose data for target, spared, and internal control vertebral bodies were extracted. Multivariate generalized estimating equation modeling was used to assess the effect of dose and other clinical factors on vertebral body growth. A total of 34 patients (20 boys, 14 girls) met study criteria. Median age at start of radiation was 4.3years; all but 1 had prior high-dose chemotherapy with stem cell rescue. Mean growth rates of target, spared, and control vertebral bodies (cm/body/y) were, respectively, 0.027, 0.032, and 0.044 in thoracic spine and 0.033, 0.055, and 0.083 in lumbar spine. On multivariate generalized estimating equation analysis, higher dose, older treatment age, male gender, and thoracic spine location were significantly associated with decreased vertebral body growth (P<.0001, P<.0001, P=.007, and P<.0001, respectively). Dose and spine location were significant in a 3-way interaction model (P<.0001). Vertebral bodies spared by intensity modulated radiation therapy grew faster than target vertebrae. Regardless of intent to spare or target, multivariate analysis confirms that lower dose results in significantly increased growth rate. This technique should be investigated prospectively.

237 - Clinical Translation of the LevelCheck Algorithm for Automatic Localization of Target Vertebrae in Spine Surgery

237 - Clinical Translation of the LevelCheck Algorithm for Automatic Localization of Target Vertebrae in Spine Surgery

Spinal pedicle screw planning using deformable atlas registration

Spinal screw placement is a challenging task due to small bone corridors and high risk of neurological or vascular complications, benefiting from precision guidance/navigation and quality assurance (QA). Implicit to both guidance and QA is the definition of a surgical plan—i.e. the desired trajectories and device selection for target vertebrae—conventionally requiring time-consuming manual annotations by a skilled surgeon. We propose automation of such planning by deriving the pedicle trajectory and device selection from a patient’s preoperative CT or MRI.An atlas of vertebrae surfaces was created to provide the underlying basis for automatic planning—in this work, comprising 40 exemplary vertebrae at three levels of the spine (T7, T8, and L3). The atlas was enriched with ideal trajectory annotations for 60 pedicles in total. To define trajectories for a given patient, sparse deformation fields from the atlas surfaces to the input (CT or MR image) are applied on the annotated trajectories. Mean value coordinates are used to interpolate dense deformation fields. The pose of a straight trajectory is optimized by image-based registration to an accumulated volume of the deformed annotations. For evaluation, input deformation fields were created using coherent point drift (CPD) to perform a leave-one-out analysis over the atlas surfaces.CPD registration demonstrated surface error of 0.89 ± 0.10 mm (median ± interquartile range) for T7/T8 and 1.29 ± 0.15 mm for L3. At the pedicle center, registered trajectories deviated from the expert reference by 0.56 ± 0.63 mm (T7/T8) and 1.12 ± 0.67 mm (L3). The predicted maximum screw diameter differed by 0.45 ± 0.62 mm (T7/T8), and 1.26 ± 1.19 mm (L3). The automated planning method avoided screw collisions in all cases and demonstrated close agreement overall with expert reference plans, offering a potentially valuable tool in support of surgical guidance and QA.

Dual-energy CT virtual non-calcium technique for detection of bone marrow edema in patients with vertebral fractures: A prospective feasibility study on a single- source volume CT scanner

ObjectivesDual-energy computed tomography (DECT) is a recent development for detecting bone marrow edema (BME) in patients with vertebral compression fractures. The aim of this pilot study was to determine the reliability of single-source DECT in detecting vertebral BME using magnetic resonance imaging (MRI) as standard of reference. Materials and methodsNine patients with radiographic thoracic or lumbar vertebral compression fractures underwent both, DECT on a 320-row single-source scanner and 1.5T MRI. Virtual non-calcium (VNC) images were reconstructed from the DECT volume datasets. Three blinded readers independently scored images for the presence of BME. Only vertebrae with loss of height in radiography (target vertebrae) were included in the analysis. A vertebra was counted as positive if two readers agreed on the presence of BME. Cohen’s kappa was calculated for interrater comparison. Intervertebral ratios of target and the reference vertebra were compared for CT attenuation and MR signal intensity in a reference vertebra using Spearman correlation. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. ResultsFourteen target vertebrae with a radiographic height loss were identified; eight of them showed BME on MRI, while DECT identified BME in 7 instances. There were no false positive virtual non-calcium images, resulting in a sensitivity of 0.88 (0.75–1.0 among all readers) and specificity of 1.0 (0.81–1.0). Interrater agreement was inferior for DECT (κ=0.63–0.89) compared to MRI (κ=0.9–1.0). Intervertebral ratio in VNC images strongly correlated with short-tau inversion recovery (r=0.87) and inversely with T1 (-0.89). SNR (0.2+/− 0.2 in VNC and 16.7+/− 7.3 in STIR) and CNR (0.2+/− 0.3 and 7.1+/− 6.3) values were inferior in VNC. ConclusionsDetecting BME with single-source DECT is feasible and allows detection of vertebral compression fractures with reasonably high sensitivity and specificity. However, image quality of VNC reconstructions has to be improved to achieve better interrater agreement. Nonetheless, DECT might accelerate the diagnostic work-flow in patients with vertebral compression fractures in the future and reduce the number of additional MRI examinations.

WE‐AB‐BRA‐09: Registration of Preoperative MRI to Intraoperative Radiographs for Automatic Vertebral Target Localization

Purpose:Accurate localization of target vertebrae is essential to safe, effective spine surgery, but wrong‐level surgery occurs with surprisingly high frequency. Recent research yielded the “LevelCheck” method for 3D‐2D registration of preoperative CT to intraoperative radiographs, providing decision support for level localization. We report a new method (MR‐LevelCheck) to perform 3D‐2D registration based on preoperative MRI, presenting a solution for the increasingly common scenario in which MRI (not CT) is used for preoperative planning.Methods:Direct extension of LevelCheck is confounded by large mismatch in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation. Using seed points at centroids, vertebrae are segmented using continuous max‐flow method and dilated by 1.8 mm to include surrounding cortical bone (inconspicuous in T2w‐MRI). MRI projections are computed (analogous to DRR) using segmentation and registered to intraoperative radiographs. The method was tested in a retrospective IRB‐approved study involving 11 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. Registration accuracy was evaluated in terms of projection‐distance‐error (PDE) between the true and estimated location of vertebrae in each radiograph.Results:The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch, and large capture range. Segmentation achieved Dice coefficient = 89.2 ± 2.3 and mean‐absolute‐distance (MAD) = 1.5 ± 0.3 mm. Registration demonstrated robust performance under realistic patient variations, with PDE = 4.0 ± 1.9 mm (median ± iqr) and converged with run‐time = 23.3 ± 1.7 s.Conclusion:The MR‐LevelCheck algorithm provides an important extension to a previously validated decision support tool in spine surgery by extending its utility to preoperative MRI. With initial studies demonstrating PDE &lt;5 mm and 0% failure rate, the method is now in translation to larger scale prospective clinical studies.S. Vogt and G. Kleinszig are employees of Siemens Healthcare

Automatic localization of target vertebrae in spine surgery: clinical evaluation of the LevelCheck registration algorithm.

A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients. To measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery. In spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan. Twenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts. At 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s). The LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies. N/A.

A Clinical Evaluation of LevelCheck for Automatic Localization of Target Vertebrae in Spine Surgery

A Clinical Evaluation of LevelCheck for Automatic Localization of Target Vertebrae in Spine Surgery

The Preliminary Research of Drill Guide Template Design for Pedicle Screw Placement with a Low-Cost 3D Pinter

The purpose of this study is illustrated the potential of applying the additive manufacturing (AM) technology with a low-cost three-dimensional (3D) printer on clinical applications of spine surgeries. First, the target vertebrae will be extracted from the computed tomography (CT) images of a patient and converted to a 3D polyhedral model. After choosing the target regions of pedicle screws in this 3D polyhedral model, the optimal screw angles and depths will be obtained without injuring the spinal cord. Then, a drill guide template of pedicle screws will be developed by using an AM software, and fabricated by a low-cost 3D printer. The doctor can utilize it to buckle the specific designed position of the vertebrae of the patient, and drill directly through the guide hole during the scoliosis surgery. These steps can reduce the surgical time substantially. Finally, several cases were executed to verify the placement accuracy of drill guide templates fabricated by the low-cost 3D printer.