Computed Tomography Of Lumbar Spine Research Articles

Establishment of Typical Dose Reference Level (DRL) Values for Adult Patients Undergoing Computed Tomography in Three Hospitals in Brazzaville, Congo Republic

The present study aimed at to establish typical dose reference level (DRL) values for adult patients undergoing computed tomography (CT) examinations in three hospitals in Brazzaville, Congo Republic. The examinations considered were head, chest, abdomen-pelvis, chest-abdomen-pelvis and lumbar spine CT scans. For each examination, the median of the distribution for the volume CT dose index (CTDIvol) and dose-length product (DLP) were determined. The median values were considered as the typical DRL values. Hypothesis testing was also carried out to statistically compare the mean values obtained for each participating CT facility. In the case of head scan, the typical values proposed in the present study in terms of CTDIvol and DLP were 15–48% and 28–60% lower than the DRL values reported in the literature, respectively. In the case of lumbar spine, the typical value determined in this study in terms of DLP was 48–70% greater than the DRL values provided in the literature. The typical values proposed for chest, abdomen-pelvis and chest-abdomen-pelvis were within the DRL values reported in the literature. It is possible that the adoption of the typical values proposed in the present study will help in reducing the dose received by adult patients undergoing CT examination in Congo Republic.

Establishment of local diagnostic reference levels for computed tomography with cloud-based automated dose-tracking software in Türkiye

The purpose of this study is to establish local diagnostic reference levels (LDRLs) for computed tomography (CT) procedures using cloud-based automated dose-tracking software. The study includes the dose data obtained from a total of 104,272 examinations performed on adult patients (>18 years) using 8 CT scanners over 12 months. The protocols included in our study were as follows: head CT without contrast, cervical spine CT without contrast, neck CT with contrast, chest CT without contrast, abdomen-pelvis CT without contrast, lumbar spine CT without contrast, high-resolution computed tomography (HRCT) of the chest, and coronary CT angiography (CTA). Dose data were collected using cloud-based automatic dose-tracking software. The 75th percentiles of the distributions of the median volume CT dose index (CTDIvol) and dose length product (DLP) values were used to determine the LDRLs for each protocol. The LDRLs were compared with national DRLs (NDRLs) and DRLs set in other countries. Inter-CT scanner variability, which is a measure of how well clinical practices are standardized, was determined for each protocol. Median values for each protocol were compared with the LDRLs for dose optimization in each CT scanner. The LDRLs (for DLP and CTDIvol, respectively) were 839 mGy.cm and 41.2 mGy for head CT without contrast, 530.6 mGy.cm and 19.8 mGy for cervical spine CT without contrast, 431.9 mGy.cm and 15.5 mGy for neck CT with contrast, 364.8 mGy.cm and 9.3 mGy for chest CT without contrast, 588.9 mGy. cm and 11.2 mGy for abdomen-pelvis CT without contrast, 713 mGy.cm and 24.3 mGy for lumbar spine CT without contrast, 326 mGy.cm and 9.5 mGy for HRCT, and 642.3 mGy.cm and 33.4 mGy for coronary CTA. The LDRLs were comparable to or lower than NDRLs and DRLs set in other countries for most protocols. The comparisons revealed the need for immediate initiation of an optimization process for CT protocols with higher dose distributions. Furthermore, protocols with high inter-CT scanner variability revealed the need for standardization. There is a need to update the NDRLs for CT protocols in Turkey. Until new NDRLs are established, local institutions in Turkey can initiate the optimization process by comparing their dose distributions to the LDRLs established in our study. Automated dose-tracking software can play an important role in establishing DRLs by facilitating the collection and analysis of large datasets.

Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.

Coronal plane in opportunistic screening of osteoporosis using computed tomography: comparison with axial and sagittal planes.

To compare the coronal plane with axial and sagittal planes in opportunistic screening of osteoporosis using computed tomography (CT). A total of 100 patients aged ≥ 50years who underwent both lumbar spine CT and dual-energy X-ray absorptiometry within 3months were included. Osteoporosis was diagnosed based on dual-energy X-ray absorptiometry results. The CT number was measured at the center of the vertebral body in coronal, axial, and sagittal planes. To compare the coronal plane with axial and sagittal planes in diagnosing osteoporosis, the areas under the receiver operating characteristic curve (AUC) were compared and intraclass correlation coefficient (ICC) was calculated. The optimal cutoff values were calculated using Youden's index. The AUC of the coronal plane (0.80; 95% confidence interval [CI], 0.71-0.89) was not significantly different from that of the axial plane (0.78; 95% CI, 0.68-0.87; P = 0.39) and that of the sagittal plane (0.78; 95% CI, 0.69-0.87; P = 0.68). Excellent concordance rates were observed between coronal and axial planes with ICC of 0.95 (95% CI, 0.92-0.96) and between coronal and sagittal planes with ICC of 0.93 (95% CI, 0.85-0.96). The optimal cutoff values for the coronal, axial, and sagittal planes were 110, 112, and 112 HU, respectively. The coronal plane does not significantly differ from axial and sagittal planes in opportunistic screening of osteoporosis. Thus, the coronal plane as well as axial and sagittal planes can be used interchangeably in measuring bone mineral density using CT.

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis.

The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications. A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA). A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort. The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

Estimation of Bone Mineral Density in the Femoral Neck and Lumbar Spine using Texture Analysis of Chest and Pelvis Computed Tomography Hounsfield Unit.

This study aimed to establish an academic basis for using a computed tomography (CT) model for predicting osteoporosis in the clinical setting by illustrating the effectiveness of morphometric texture analysis. We introduce texture analysis and quantitative approaches using CT Hounsfield units (HU) to screen osteoporosis. From March 6th, 2013, to August 11th, 2020, a total of 4,333 cases (1,766 patients) were included in the study. After applying exclusion criteria concerning the patient status and scan interval between CT and DXA, we selected only 1,647 samples (736 patients) and analyzed both their CT and DXA bone mineral density (BMD) results. BMD was measured in the femoral neck and L1 spine body. A region of interest (ROI) was extracted from each patient's CT as the maximum trabecular area of the L1 spine body and femoral neck. A total of 45 texture features were extracted from every ROI using gray-level co-occurrence matrices. Machine-learning techniques, including linear regression (LR) and artificial neural network (ANN), were applied to predict BMD. We assigned samples to (1) Set 1 (857 lumbar spine samples in chest model, L1 spine DXA BMD), (2) Set 2 (392 lumbar spine samples in lumbar spine CT model, L1 spine DXA BMD), (3) Set 3 (1,249 lumbar spine samples in both chest and lumbar spine CT model, L1 spine DXA BMD), (4) Set 4 (398 femoral neck samples in hip and pelvis CT model, femoral neck DXA BMD), and (5) Set 5 (a total of 1,647 samples). When we applied LR, the correlation coefficients between estimated and reference values for Sets 1, 2, 3, and 4 were 0.783, 0.784, 0.757, and 0.652, respectively. For total samples (Set 5), LR and ANN provided correlation coefficients of 0.707 and 0.782, respectively. The modality using morphometric texture analysis with CT HU can be an additional diagnostic tool for osteoporosis and an alternative for DXA.

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.

Intra-individual comparison of lumbar spine CT, abdomen-pelvis contrast enhanced CT, and low-dose chest CT for bone density measurement.

Dual-energy X-ray absorptiometry (DXA) is the reference standard for the measurement of bone mineral density (BMD) and subsequent diagnosis of osteoporosis. Since various computed tomography (CT) protocols are scanned for various indications, we can incidentally measure BMD using CT. Previous studies have revealed a correlation between BMD and Hounsfield unit (HU) values obtained with different CT protocols. To compare the diagnostic value of CT protocols (lumbar spine CT [LSCT], abdomen-pelvis contrast-enhanced CT [APCT], and low-dose chest CT [LDCT]) for osteoporosis. We retrospectively included 17 patients (6 men, 11 women; mean age=68 years) who had undergone all four imaging studies within six months, during 2011-2021. HU values were manually measured at the center of the L1 vertebra by a radiology resident. Pearson correlation test was performed between HU values and BMD of L1 vertebra. The diagnostic performance of each CT protocol was assessed with receiver operating characteristic (ROC) analysis. Intra-individual concordance of the four tests to diagnose osteoporosis was analyzed by tabulating. The mean HU values were 104.4 ± 47.2 HU with LSCT, 149.0 ± 56.9 HU with APCT, and 114.3 ± 60.0 HU with LDCT. HU values from each protocol were positively correlated (r = 0.676-0.735; P < 0.005) with BMD. LDCT had the highest diagnostic performance (area under the ROC curve [AUC] = 0.701) and APCT the lowest (AUC = 0.569). APCT was discordant with the other protocols for diagnosing osteoporosis. LDCT had the highest diagnostic performance for osteoporosis with predetermined cutoff value. APCT requires the increase of cutoff value for osteoporosis diagnosis.

Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study

PurposeThe purpose of this study was to assess the impact of the new artificial intelligence deep-learning reconstruction (AI-DLR) algorithm on image quality and radiation dose compared with iterative reconstruction algorithm in lumbar spine computed tomography (CT) examination. Materials and methodsAcquisitions on phantoms were performed using a tube current modulation system for four DoseRight Indexes (DRI) (i.e., 26/23/20/15). Raw data were reconstructed using the Level 4 of iDose4 (i4) and three levels of AI-DLR (Smoother/Smooth/Standard) with a bone reconstruction kernel. The Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’) were computed (d’ modeled detection of a lytic and a sclerotic bone lesions). Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists. ResultsThe Noise magnitude was lower with AI-DLR than i4 and decreased from Standard to Smooth (-31 ± 0.1 [SD]%) and Smooth to Smoother (-48 ± 0.1 [SD]%). The average NPS spatial frequency was similar with i4 (0.43 ± 0.01 [SD] mm–1) and Standard (0.42 ± 0.01 [SD] mm–1) but decreased from Standard to Smoother (0.36 ± 0.01 [SD] mm–1). TTF values at 50% decreased as the dose decreased but were similar with i4 and all AI-DLR levels. For both simulated lesions, d’ values increased from Standard to Smoother levels. Higher detectabilities were found with a DRI at 15 and Smooth and Smoother levels than with a DRI at 26 and i4. The images obtained with these dose and AI-DLR levels were rated satisfactory for clinical use by the radiologists. ConclusionUsing Smooth and Smoother levels with CT allows a significant dose reduction (up to 72%) with a high detectability of lytic and sclerotic bone lesions and a clinical overall image quality.

Prevalence and neglected rate of sacroiliitis on lumbar spine CT in patients with low back pain.

The study aims to investigate the prevalence and neglected rate of sacroiliitis on lumbar spine computed tomography (CT) in patients with low back pain. From January 2016 to December 2020, a total of 4827 patients (mean age: 35.4 ± 9.5years) who underwent lumbar spine CT examinations were included in this retrospective study. The CT degree of sacroiliitis in all study population were retrospectively reviewed by two radiologists. The independent sample t-test was used to compare the continuous values and chi-squared or Fisher's exact test was used to compare the categorized values. Sacroiliitis was identified in 514 of 4827 patients (10.6%). Patients with sacroiliitis were significantly younger than those without sacroiliitis (32.1 ± 8.9 vs. 35.8 ± 9.5years, p < 0.001). Moreover, significantly more patients with sacroiliitis had HLA-B27 (p < 0.001) positivity and inflammatory back pain syndrome (p = 0.003) than those without sacroiliitis. Among the 514 patients, sacroiliitis was recognized on primary reading in 386 patients (75.1%) but was neglected in the remaining 128 patients (24.9%). Of the 386 patients, 371 patients were followed up, and finally, 295 patients of them (79.5%) were diagnosed with axSpA. Radiologists should pay careful and more attention to sacroiliac joint on lumbar spine CT for early diagnosis of sacroiliitis in young patients with low back pain, which could result in early diagnosis and treatment of axSpA.

Relationship between the Properties of Paraspinal Muscles and Bone Mineral Density in Osteoporotic Patients.

To investigate the properties of paraspinal muscle structures in osteoporotic patients, lumbar computed tomography (CT) and their relationship with lumbar and femoral neck bone mineral density (BMD). A Descriptive study. Department of Radiology and Physical Medicine, Istanbul Medeniyet University, Goztepe Prof Dr Suleyman Yalcin City Hospital, Istanbul, Turkey, between June 2020 and June 2021. Ninety patients with osteoporosis, who received lumbar spine CT and dual-energy X-ray absorptiometry (DEXA) examinations, were retrospectively included. Cross-sectional areas (CSA), densities, index values, and Hounsfield unit average calculation(HUAC) values were measured for psoas, multifidus, erector spinae, and quadratus lumborum muscles at the L3 level, and their associations with BMD were investigated. A total of 90 patients including 71 females were evaluated. The mean age was 63.31±11.90 years. A moderate correlation of lumbar spine (r=0.32, p<0.01) and femur neck BMD (r=0.28, p<0.05) with erector spinae CSA was defined. Small correlation between psoas CSA (r=0.28, p<0.05) and psoas muscle index (PMI, r=0.24, p<0.05) with lumbar spine BMD was found. The CSA of all the paraspinal muscles (p<0.01) and densities, except psoas muscle (p<0.05), were higher in males. In non-obese osteoporotic patients, the density and HUAC values of the paraspinal muscles, except the psoas muscle, were found to be significantly higher (p<0.05). The properties of the paraspinal muscles measured by the lumbar CT scan are found to be related to BMD. The psoas and erector spinae CSA and index values are the most significantly associated parameters with the lumbar spine BMD. Bone mineral density, Computed tomography, Osteoporosis, Paraspinal muscles, Psoas.

Adult Computed Tomography examinations in Uganda: Towards determining the National Diagnostic Reference Levels

IntroductionDiagnostic Reference Levels (DRLs), typically set at the 75th percentile of the dose distribution from surveys conducted across a broad user base using a specified dose-measurement protocol, are recommended for radiological examinations. There is a need to develop and implement DRLs as a standardisation and optimisation tool for the radiological protection of patients at Computed Tomography (CT) facilities.MethodsThis was a retrospective cross-sectional study conducted in seven (7) different CT scan facilities in which participants were recruited by systematic random sampling. The study variables were dose length product (DLP) and volume-weighted CTDI (CTDIvol) for the radiation doses for head, chest, abdomen and lumbar spine CT examinations. The DRLs for CTDIvol and DLP were obtained by calculating the 3rd quartiles of the radiation doses per study site by anatomical region. The national diagnostic reference levels were determined by computation of DRLs using the 75th centile of the median values.ResultsA total of 574 patients were examined with an average age of 47.1 years. For CTDIvol estimates; there was a strong positive significant relationship between the CTDIvol and examination mAs (rs = 0.9017, p-value < 0.001), and reference mAs (rs = 0.0.7708, p-value < 0.001). For DLP estimates; there was a moderate positive significant relationships between DLP and total mAs (rs = 0.6812, p-value < 0.001), reference mAs (rs = 0.5493, p-value < 0.001). The DRLs were as follows; for head CT scan – the average median CTDIvol was 56.02 mGy and the DLP was 1260.3 mGy.cm; for Chest CT, the CTDI volume was 7.82 mGy and the DLP was 377.0 mGy.cm; for the abdomen CT, the CTDI volume 12.54 mGy and DLP 1418.3 mGy.cm and for the lumbar spine 19.48 mGy and the DLP was 843 mGy.cm, respectively.ConclusionThis study confirmed the need to optimize the CT scan parameters in order to lower the national DRLs. This can be achieved by extensive training of all the CT scan radiographers on optimizing the CT scan acquisition parameters. Continuous dose audits are also advised with new equipment or after every three years to ensure that values out of range are either justified or further investigated.

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.

Retrospective analysis of dose delivered to the uterus during CT examination in pregnant women

PurposeThe purpose of this study was to analyze the dose to the uterus (UD) calculated for pregnant women per computed tomography (CT) acquisition and per CT examination in our Institution. Materials and methodsConsecutive pregnant women who underwent CT examination from June 2014 to February 2022 and for whom UD calculation was performed by a medical physicist were retrospectively included. UDs were computed per CT acquisition using the CT Expo 2.4 software and were summed up to obtain the total UD per CT examination. The CTDIvol and dose-length product (DLP) values were retrieved from the dose report and compared with those calculated by the software. ResultsA total of 256 pregnant women with a mean age of 29.4 ± 5.5 (SD) years (range: 18–48) at 24.5 ± 10.4 (SD) weeks of amenorrhea (range: 1–40) were included. UDs were computed for 339 CT acquisitions. The CTDIvol and DLP computed by the software were significantly greater than those retrieved from the dose reports (P < 0.05). The greatest UDs were reported for the abdomen-pelvis (10.93 ± 5.74 [SD] mGy; range: 1.2–24.1), chest-abdomen-pelvis (9.79 ± 7.09 [SD] mGy; range: 3.9–22.1), pelvis (18.50 ± 17.96 [SD] mGy; range: 5.8–31.2) and lumbar spine (10.24 ± 11.38 [SD] mGy; range: 2.3–29.6) CT examinations. The total UDs per CT examination were > 20 mGy for 10 pregnant women and the maximum total UD was 52.3 mGy. ConclusionGreatest UDs during CT examinations are observed when the pelvis is directly exposed to X-rays. With current dose levels and in optimized practices, UDs per CT acquisition and CT examination are always below 100 mGy. UD calculations cannot be performed for CT examinations that do not directly expose the pelvis (i.e., those < 1 mGy).

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.

VALIDAÇÃO DA MEDIÇÃO DAIP USANDO TC E COMPARAÇÃO COM RADIOGRAFIAS DA COLUNA TOTAL E LOMBOSSACRAL

ABSTRACT Objective: The objective of this study is to describe a new, extremely simple method for measuring pelvic incidence (PI) using computed tomography (CT) and to compare those measurements with measurements derived from whole spine and lumbosacral X-rays in a Brazilian population. Methods: Patients who had whole spine and lumbosacral X-rays and whole abdomen, pelvis, or lumbar spine CT performed within a period of less than three months were selected. Image overlay was used to measure PI from the CT. The PI was calculated by two independent examiners, and the PI for each exam was calculated twice, with an interval of two months between the assessments. The intra- and interexaminer reliability and reproducibility were evaluatedusing the intraclass correlation coefficient (ICC) and the repeatability coefficient, considering a 95% confidence interval. Results: Fifty-five patients of both sexes with a mean age of 58.7 years (±19) were analyzed. The mean PI angles in the analyses of both examiners at both evaluations were 54.85° (±13.73) for the whole spine X-ray, 54.06° (±11.67) for the lumbosacral spine X-ray, and 49.96° (±9.85) for the CT. There was good intra- and interexaminer reliability and reproducibility. There was also high concordancewith the whole spine and lumbosacral X-rays. Conclusion: CT is a reliable and reproducible alternative for measuring PI. Level of Evidence III; Prospective comparative.

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.

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.

Usefulness of model-based iterative reconstruction in low-dose lumbar spine CT after spine surgery with metal implant: a phantom study.

The major problems of computed tomography (CT) imaging include radiation exposure and severe artifacts caused by operative implants. To evaluate the usefulness of the metal artifact reduction algorithm and model-based iterative reconstruction (MBIR) in postoperative low-dose (LD) spine CT. A CT torso phantom was scanned at standard-dose (SD) and LD settings. The CT images were reconstructed by three methods: hybrid iterative reconstruction (HIR); metal artifact reduction; and MBIR. The radiation dose of the phantom imaging was evaluated by volume CT dose index (mGy), dose length product (DLP, mGy × cm), and effective dose (mSv). The image quality of the six images was visually evaluated and analyzed using Scheffe's paired comparison method. The average preference of each method was calculated based on the comparative scores. The task transfer function (TTF) and noise power spectrum for HIR and MBIR were also measured. The respective radiation-dose-related parameters of the SD and LD conditions were: volume CT dose index = 10.2 and 1.2 mGy; DLP = 277.9 and 33.9 mGy × cm; and effective dose = 4.2 and 0.5 mSv. The average preference for diagnostic acceptability of MBIR at LD was not significantly different from the other reconstructions of SD data. MBIR successfully reduced metal artifacts in the LD condition. The 10% TTF was higher for HIR at SD and higher for MBIR at LD. MBIR is useful for LD spine CT after spine surgery with metal implant.