Computed Tomography Images Of Spine Research Articles

TomoRay: Generating Synthetic Computed Tomography of the Spine From Biplanar Radiographs.

Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively. A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS). At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively. Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.

Application of radiomics model based on lumbar computed tomographyin diagnosis of elderly osteoporosis.

A metabolic bone disease characterized by decreased bone formation and increased bone resorption is osteoporosis. It can cause pain and fracture of patients. The elderly are prone to osteoporosis and are more vulnerable to osteoporosis. In this study, radiomics are extracted from computed tomography (CT)images to screen osteoporosis in the elderly. Collect the plain scan CT images of lumbar spine, cut the region of interest of the image and extract radiomics features, use Lasso regression to screen variables and adjust complexity, use python language to model random forests, support vector machines, K nearest neighbor, and finally use receiver operating characteristic curve to evaluate the performance of the model, including precision, recall, accuracy and area under the curve (AUC). For the model, 14 radiolomics features were selected. The diagnosis performance of random forest model and support vector machine is good, all around 0.9. The AUC of K nearest neighbor model in training set and test set is 0.828 and 0.796, respectively. We selected the plain scan CT images of the elderly lumbar spine to build radiomics features model, which has good diagnostic performance and can be used as a tool to assist the diagnosis of osteoporosis in the elderly.

Evaluation of bone mineral density in adolescent idiopathic scoliosis using a three-dimensional finite element model: a retrospective study

BackgroundAdolescent idiopathic scoliosis (AIS) is often accompanied by osteopenia and osteoporosis, which can cause serious complications. The aim of this study was to determine the specific bone mineral density (BMD) of each vertebral body in patients with AIS using biomechanical finite element modeling based on three-dimensional (3D) reconstruction.MethodsThis retrospective study involved 56 patients with AIS. Computed tomography (CT) and radiography were performed. Spinal vertebrae were segmented from the spinal CT images of patients with AIS to reconstruct 3D vertebral models. The vertebral models were meshed into tetrahedral finite elements to assess the BMD.ResultsThe mean main curve Cobb angle was 88.6 ± 36.7°, and the mean kyphosis angle was 36.8 ± 31.5°. The mean BMD of the global spine was 0.83 ± 0.15 g/cm2. The highest BMD was measured on the concave side of the apex (0.98 ± 0.16 g/cm2). Apical vertebral BMD was negatively correlated with age and height (r = − 0.490, p = 0.009 and r = − 0.478, p = 0.043, respectively). There were no significant differences in BMD values between the concave and convex sides (p > 0.05).ConclusionsThe 3D finite element modeling of BMD in patients with AIS is a reliable and accurate BMD measurement method. Using this method, the overall BMD of patients with AIS was shown to gradually decrease from the top to the bottom of the spine. Our findings provide valuable insights for surgical planning, choice of screw trajectories, and additional biomechanical analyzes using finite element models in the context of scoliosis.

Reduction of Atlanto-Occipital Dislocation After Cervical Collar Removal

A 39-year-old male pedestrian-hit-by-car was transferred to our institution with atlantooccipital dislocation (AOD) on outside computed tomography (CT) imaging. On arrival he had a rigid cervical collar in place. Given the reported AOD, we placed the patient flat, removed the cervical collar, and supported the head in neutral alignment with sandbags. Due to a technical issue uploading his prior imaging to our system, the patient underwent repeat CTs at our center. Subsequently, after the outside images were uploaded, we were able to compare his cervical spine CT images before and after removing his cervical collar. On comparison, we noted a substantial reduction in AOD after collar removal. We hope this serves as a reminder of this key step in managing a rare but deadly clinical entity and a small demonstration of the efficacy of this intervention.

Effect of Deep Learning Reconstruction on Evaluating Cervical Spinal Canal Stenosis With Computed Tomography.

Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal stenosis; however, some patients are ineligible for MRI. We aimed to assess the effect of deep learning reconstruction (DLR) in evaluating cervical spinal canal stenosis using computed tomography (CT) compared with hybrid iterative reconstruction (hybrid IR). This retrospective study included 33 patients (16 male patients; mean age, 57.7 ± 18.4 years) who underwent cervical spine CT. Images were reconstructed using DLR and hybrid IR. In the quantitative analyses, noise was recorded by placing the regions of interest on the trapezius muscle. In the qualitative analyses, 2 radiologists evaluated the depiction of structures, image noise, overall image quality, and degree of cervical canal stenosis. We additionally evaluated the agreement between MRI and CT in 15 patients for whom preoperative cervical MRI was available. Image noise was less with DLR than hybrid IR in the quantitative ( P ≤ 0.0395) and subjective analyses ( P ≤ 0.0023), and the depiction of most structures was improved ( P ≤ 0.0052), which resulted in better overall quality ( P ≤ 0.0118). Interobserver agreement in the assessment of spinal canal stenosis with DLR (0.7390; 95% confidence interval [CI], 0.7189-0.7592) was superior to that with hybrid IR (0.7038; 96% CI, 0.6846-0.7229). As for the agreement between MRI and CT, significant improvement was observed for 1 reader with DLR (0.7910; 96% CI, 0.7762-0.8057) than hybrid IR (0.7536; 96% CI, 0.7383-0.7688). Deep learning reconstruction provided better quality cervical spine CT images in the evaluation of cervical spinal stenosis than hybrid IR.

Lumbar spine segmentation method based on deep learning

Aiming at the difficulties of lumbar vertebrae segmentation in computed tomography (CT) images, we propose an automatic lumbar vertebrae segmentation method based on deep learning. The method mainly includes two parts: lumbar vertebra positioning and lumbar vertebrae segmentation. First of all, we propose a lumbar spine localization network of Unet network, which can directly locate the lumbar spine part in the image. Then, we propose a three-dimensional XUnet lumbar vertebrae segmentation method to achieve automatic lumbar vertebrae segmentation. The method proposed in this paper was validated on the lumbar spine CT images on the public dataset VerSe 2020 and our hospital dataset. Through qualitative comparison and quantitative analysis, the experimental results show that the method proposed in this paper can obtain good lumbar vertebrae segmentation performance, which can be further applied to detection of spinal anomalies and surgical treatment.

A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine

Osteoporosis is a common systemic bone disease with insidious onset and low treatment efficiency. Once it occurs, it will increase bone fragility and lead to fractures. Computed tomography (CT) is a non-invasive medical examination method that can identify the bone condition of patients. In this paper, we propose a novel channel attention module, which is subsequently integrated into the supervised deep convolutional neural network (DCNN) termed DSNet, which can perform feature fusion from two different scales, and use the method of quadratic weight calculation to enhance the interconnection among feature map channels and improve the detection and classification performance for the bone condition in lumbar spine CT images. To train and test the proposed framework, we retrospectively collect 4805 CT images of 133 patients, using DXA as the gold standard. According to the T-value diagnostic criteria defined by WHO, the vertebral bodies of L1 - L4 in CT images are labeled and classified into osteoporosis, osteopenia and normal bone mineral density. Meanwhile, the training set and test set are constructed in the ratio of 4:1. As a result, the DSNet achieves a prediction accuracy of 83.4% and a recall rate of 90.0% on the test set, indicating that the proposed model has the potential to assist clinicians in diagnosing individuals with abnormal BMD and may alert patients at high risk of osteoporosis for timely treatment.

Usefulness of digital tomosynthesis in diagnosing cervical ossification of the posterior longitudinal ligament: a comparative study with other imaging modalities.

The diagnosis and classification of ossification of the posterior longitudinal ligament (OPLL) can be difficult with radiography alone; therefore, computed tomography (CT) is also usually performed. There are many reports on the usefulness of digital tomosynthesis (DTS) for image analysis in orthopedics. This study aimed to compare the accuracy of DTS with radiography and CT for the diagnosis and classification of cervical OPLL (C-OPLL). We included 31 patients with OPLL and 30 with cervical spondylotic myelopathy. The patients' cervical spine radiography, DTS, and CT images were each evaluated twice by three specialists and three residents. In the intra-observer reliability study, there was one observer with a fair level of kappa values for radiography and DTS among three residents. The kappa values for CT were the best for all observers. In the inter-observer reliability study, the interclass correlation coefficient (ICC) values were high for both diagnosis and classification by specialists at the almost perfect level for all three imaging modalities. On the other hand, the ICC values for both diagnosis and classification for radiography by the residents were lower than those for DTS and CT. This study revealed that DTS may be an alternative to CT for the diagnosis and classification of C-OPLL by specialists. Caution should be exercised in diagnosing and classifying C-OPLL using radiography and DTS by residents, and the use of CT is recommended.

The improved faster-RCNN for spinal fracture lesions detection

Purpose: Reading spinal CT (Computed Tomography) images is very important in the diagnosis of spondylosis, which is time-consuming and prones to make biases. In this paper, we propose a framework based on Faster-RCNN to improve detection performances of three spinal fracture lesions: cfracture (cervical fracture), tfracture (thoracic fracture) and lfracture (lumbar fracture). Methods: First, we use ResNet50 to replace VGG16 in backbone network in Faster-RCNN to increase depth of training network. Second, we utilize soft-NMS (Non-Maximum Suppression) instead of NMS to avoid missed detection of overlapped lesions. Third, we simplify RPN (Region Proposal Network) to accelerate training speed and reduce missed detection. Finally, we modify the classifier layer in Faster-RCNN and choose appropriate length-width ratio by changing anchor sizes in sliding window, then adopt multi-scale strategy in training to improve efficiency and accuracy. Results: The experimental results show that the proposed scheme has a good performance, mAP (mean average precision) is 90.6%, IOU (Intersection of Union) is 88.5 and detection time is 0.053 second per CT image, which means our proposed method can accurately detect spinal fracture lesions. Conclusion: Our proposed method can provide assistance and scientific references for both doctors and patients in clinically.

Three dimensional reconstruction of the spine computed tomography (CT) images from 2D CT slices

In medical field, imaging techniques are used for diagnosis of different disease and carrying out treatments. Number of people affected by spinal disorders is increasing nowadays. The Spine details obtained from a Computed Tomography (CT) imaging plays a significant role in screening of spine conditions, disorders and also useful for planning of surgeries. Spine has a complex structure as it consists of vertebrae, intervertebral disks, spinal cord. Surgeons face challenges while working with the computer aided system for training of surgeries. This can be made ease by creating a Three dimensional image models as it provide detailed view of the internal parts of the spine. In this paper, a method for developing three dimensional image from two dimensional slices for spine CT images is presented. The Global thresholding and a mathematical morphological technique is applied in the presented method. It is concluded that segmented and mathematical morphological technique applied reconstruction produce better results than the direct reconstruction of images from the segmented image. The visualization and the quantitative results state the method is effective and better. The results are evaluated using the metrics like Accuracy, Sensitivity, Specificity and Dice.

Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density

ObjectiveThis study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images.MethodsA total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31–94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1–L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix.ResultsThe classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979–1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779–0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891–0.989) for normal BMD versus osteopenia.ConclusionsThe excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.

Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion.

BackgroundSpinal tuberculosis (TB) has the highest incidence in remote plateau areas, particularly in Tibet, China, due to inadequate local healthcare services, which not only facilitates the transmission of TB bacteria but also increases the burden on grassroots hospitals. Computer-aided diagnosis (CAD) is urgently required to improve the efficiency of clinical diagnosis of TB using computed tomography (CT) images. However, classical machine learning with handcrafted features generally has low accuracy, and deep learning with self-extracting features relies heavily on the size of medical datasets. Therefore, CAD, which effectively fuses multimodal features, is an alternative solution for spinal TB detection.MethodsA new deep learning method is proposed that fuses four elaborate image features, specifically three handcrafted features and one convolutional neural network (CNN) feature. Spinal TB CT images were collected from 197 patients with spinal TB, from 2013 to 2020, in the People’s Hospital of Tibet Autonomous Region, China; 3,000 effective lumbar spine CT images were randomly screened to our dataset, from which two sets of 1,500 images each were classified as tuberculosis (positive) and health (negative). In addition, virtual data augmentation is proposed to enlarge the handcrafted features of the TB dataset. Essentially, the proposed multimodal feature fusion CNN consists of four main sections: matching network, backbone (ResNet-18/50, VGG-11/16, DenseNet-121/161), fallen network, and gated information fusion network. Detailed performance analyses were conducted based on the multimodal features, proposed augmentation, model stability, and model-focused heatmap.ResultsExperimental results showed that the proposed model with VGG-11 and virtual data augmentation exhibited optimal performance in terms of accuracy, specificity, sensitivity, and area under curve. In addition, an inverse relationship existed between the model size and test accuracy. The model-focused heatmap also shifted from the irrelevant region to the bone destruction caused by TB.ConclusionThe proposed augmentation effectively simulated the real data distribution in the feature space. More importantly, all the evaluation metrics and analyses demonstrated that the proposed deep learning model exhibits efficient feature fusion for multimodal features. Our study provides a profound insight into the preliminary auxiliary diagnosis of spinal TB from CT images applicable to the Tibetan area.

The Shrank YoloV3-tiny for spinal fracture lesions detection

Purpose: at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods: in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results: the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion: the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection.

Computed Tomography of the Chest as a Screening Tool for Low Bone Mineral Density.

Computed tomography (CT) Hounsfield units (HU) recently emerged as a promising screening tool for low bone mineral density (BMD). We hypothesized that CT HU measurements of the thoracic spine would significantly and positively correlate with dual X-ray absorptiometry (DXA) BMD scans of the femoral neck. The study included patients with DXA scans and thoracic CT scans at the Walter Reed National Military Medical Center. One author, blinded to the DXA scans, measured HU from the cancellous bone in T4 vertebrae. Another author statistically compared femoral neck DXA T-scores to the CT HU measurements. The study included 145 patients with CT scans and femoral neck DXAs. The osteoporotic and osteopenic groups had a significant difference in HU measurements compared to the normal group within the study (P < .0001 and .002, respectively). A low BMD screening value of 231 HU provided a sensitivity of 90.1% and negative predictive value of 85.7%. Thoracic vertebrae HU measurements correlate with a low BMD of the femoral neck as determined by DXA T-scores. A high sensitivity and negative predictive value was achieved with a screening value of 231 HU. Utilization of chest or thoracic spine CT imaging as a screening method provides a quick and available screening tool for assessing low BMD in patients with these scans.Level of Evidence: III (Diagnostic).

Finite element modelling of posterior occiput-axis fixation and biomechanical analysis of C2 intralaminar screw fixation with offset connectors

Purpose The occiput-axis crossing translaminar screw (C2LAM) fixation technique can help avoid vertebral injury, while the inclusion of offset connectors can facilitate implantation. This three-dimensional finite element (FE) study compared the stability of C2LAM using offset connectors (C2LAM + OF) with other methods. Materials and methods Occipital and cervical spine computed tomography images of a healthy 30-year-old man were selected to build the FE model. Four internal fixation instruments including occiput plate-C2 pedicle (C2P) and pars (C2Pars) screws, as well as C2LAM and C2LAM + OF were applied consecutively to the model respectively to establish four new models, which were subjected to all states of motion and physiological loads to simulate normal movement, including the four kinds of basic activities of human such as flexion, extension, lateral bending, and axial rotation. Physiological measures and comparison included the range of motion (ROM) and stress distribution in the model. Results ROM between the fixation techniques was comparable, and the stability of the C2LAM + OF fixation technique was similar to that of C2P. Screw entry points, offset connectors and rods were the main stress distribution regions in the C2LAM + OF system. The mean von Mises stress of the inner wall was significantly smaller than that of the outer wall in flexion, extension, and rotation (p < 0.05); however, lateral bending was comparable, indicating a relatively small risk of damage to the inner wall. Conclusions The results of this study indicate that the C2LAM + OF fusion technique can provide sufficient stability and can be used as an alternative to C2P under special circumstances.

Combined lumbar spine MRI and CT appropriateness checklist: a quality improvement project in Saskatchewan, Canada

BackgroundAs rates of advanced imaging for lower back pain (LBP) continue to increase, there is a need to ensure the appropriateness of imaging.ObjectiveThe goal of this project was to reduce the number of inappropriate magnetic resonance imaging (MRI) and computed tomography (CT) requests for LBP patients and facilitate appropriate imaging by developing a combined imaging appropriateness checklist for lumbar spine MRI and CT.MethodsIn prior work, we developed and adopted individual evidence-based lumbar spine MRI and CT checklists into the radiology requisition process. In the current project, a combined checklist was developed and trialed in one of the former Saskatchewan health regions (Five Hills) beginning in May 2018. Using statistical process control, control charts compared the monthly number of imaging requests pre-checklist implementation and post-checklist implementation from May 2017 to February 2020. The monthly number of lumbar spine MRI and CT requisitions in the nearby former Saskatchewan Regina Qu’Appelle Health Region, in which the combined checklist was not trialed, was also plotted and compared as a balancing measure.ResultsIn Five Hills, a shift (decrease) was observed in the monthly number of lumbar spine MRI requisitions 7 months following the implementation of the combined checklist. However, the monthly number of lumbar spine CT requisitions did not change significantly. In the Regina Qu’Appelle Health Region, there was a shift (increase) in the monthly number of lumbar spine MRI requisitions, while the monthly number of lumbar spine CT requests decreased after the implementation of the combined checklist.ConclusionsThe combined checklist with evidence-based indications for lumbar spine MRI and CT imaging in LBP patients appeared to reduce the complexity associated with two previous individual checklists and facilitate imaging appropriateness. Accountable benefits may include the reduction of radiation exposure as a result of unnecessary and repeated imaging and reduction in wait times for CT and/or MRI.

Morphometric and volumetric analysis of the cervical spinal cord and vertebral canal using computed tomography images in normal Van cats.

This study was performed on Van cats to determine the volumetric, morphometric and surface area measurement values of their cervical spinal cords (SC) and vertebral canals (CC) and the ratios between their dimensions using computed tomography (CT) images. The study also aims to reveal any biometric differences in these values between the two sexes. Spinal cord and vertebral canal CT images of 16 healthy adult Van cats (8 males and 8 females) were used in the study. First, three measurement points were selected for each vertebra on their cranial, medial and caudal sections along the sagittal axis. Next, the morphometric values were obtained using the transversal images of these measurement points. The surface areas for the SC and CC were calculated using the stereological planimetry method. The Cavalieri's principle was then used to calculate the relevant anatomic structure volumes. The obtained values were then statistically analysed. SC and CC were found to be larger in males in general, while 'SC cranial/CC cranial' and 'SC medial/CC medial' dimensions and volume ratios were found to be larger in female cats on average. Bodyweight and age values were found to have a negative correlation with SC and CC ratio, but the correlation was statistically insignificant. All SC and CC surface area and volume measurements were found to be higher in male cats (p<.05). We believe the results obtained by this study will provide valuable insight into veterinary clinicians in evaluating pathological lesions in the cervical spinal cord and vertebral canal CT scans.

3D vertebrae labeling in spine CT: an accurate, memory-efficient (Ortho2D) framework

Purpose. Accurate localization and labeling of vertebrae in computed tomography (CT) is an important step toward more quantitative, automated diagnostic analysis and surgical planning. In this paper, we present a framework (called Ortho2D) for vertebral labeling in CT in a manner that is accurate and memory-efficient. Methods. Ortho2D uses two independent faster R-convolutional neural network networks to detect and classify vertebrae in orthogonal (sagittal and coronal) CT slices. The 2D detections are clustered in 3D to localize vertebrae centroids in the volumetric CT and classify the region (cervical, thoracic, lumbar, or sacral) and vertebral level. A post-process sorting method incorporates the confidence in network output to refine classifications and reduce outliers. Ortho2D was evaluated on a publicly available dataset containing 302 normal and pathological spine CT images with and without surgical instrumentation. Labeling accuracy and memory requirements were assessed in comparison to other recently reported methods. The memory efficiency of Ortho2D permitted extension to high-resolution CT to investigate the potential for further boosts to labeling performance. Results. Ortho2D achieved overall vertebrae detection accuracy of 97.1%, region identification accuracy of 94.3%, and individual vertebral level identification accuracy of 91.0%. The framework achieved 95.8% and 83.6% level identification accuracy in images without and with surgical instrumentation, respectively. Ortho2D met or exceeded the performance of previously reported 2D and 3D labeling methods and reduced memory consumption by a factor of ∼50 (at 1 mm voxel size) compared to a 3D U-Net, allowing extension to higher resolution datasets than normally afforded. The accuracy of level identification increased from 80.1% (for standard/low resolution CT) to 95.1% (for high-resolution CT). Conclusions. The Ortho2D method achieved vertebrae labeling performance that is comparable to other recently reported methods with significant reduction in memory consumption, permitting further performance boosts via application to high-resolution CT.

Real-time ultrasound-computed tomography image fusion for transforaminal lumbar approach: a lumbosacral spine phantoms study.

Ultrasound (US) is increasingly used for transforaminal approaches. We evaluated whether fusing computed tomography (CT) images with dynamic US could be based on bony and surface landmarks in two phantom models. Recordings were performed in a gelatin-based sonographic phantom and in a computerized imaging reference system training phantom. Three anesthesiologists performed 10 US and CT image fusions via three different techniques: (1) using locations of the fifth lumbar vertebra (L5) spinous process and the two posterior superior iliac spines in both images, (2) using the two lateral edges of the laminae rather than posterior superior iliac spines, and (3) using skin landmarks. Techniques were compared using values of precision (High quality of fusion: VP inferior to 10). Three punctures targeting the L4-L5 right foramina were made, and needle positions were checked using X-ray. Sixty fusions were performed in the gelatin phantom and 90 in the training phantom. VPs values of the 150 fusions were inferior to 5. Technique 2 was superior to one [operator 1: VP: 1.12 ± 0.54 vs. 2.38 ± 1.49; operator 2: 0.6 ± 0.39 vs. 3.66 ± 1.22; operator 3: 0.89 ± 0.31 vs. 1.23 ± 0.63 (p < 0.001)] with the gelatin phantom. There were no differences with the second phantom. X-ray examinations confirmed L4-L5 needle positioning. Bony and surface landmarks allowed for accurate fusion of CT and US images of the lumbar spine. These techniques, performed on phantoms, allowed for precise localization and puncturing of lumbar neural foramina.

Thoracic and lumbar spine pedicle morphology in Japanese patients.

Pedicle morphology is important for intraoperative surgical anatomy and to define pedicle screw design and parameters. However, differences of pedicle size according to ethnicity and gender are not well studied. The purpose of this study is to investigate morphological characteristics of the pedicle in Japanese patients for determining adequate screw size and optimal surgical planning. We investigated thoracic and lumbar pedicle morphology in Japanese patients using computed tomography (CT) measurements and analyzed the standard size of pedicles on upper thoracic to lumbar spine CT images in 227 Japanese patients. Gender had a larger impact on the shape and size of pedicles than racial differences. In the distribution of pedicle width, we calculated the ratio of values less than 4.5mm, that in females resulted to be over 30% for the Th3-Th9 segment, and particularly high, above 60% at Th4 and Th5. Our measurement analysis showed that pedicle morphological parameters in Japanese patients showed tendency to be smaller to those found in other studies, and particularly in female patients, they were statistically significantly smaller. Adequate transpedicular instrumentation for Japanese patients will require smaller size pedicle-related devices that will match our anatomical findings to achieve safe device placement. In addition, serving ethnically non-homogenous patient population can require further to spinal morphometric for precise device selection.