Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.

Background: To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment. Methods: Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability. Results: SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%. Conclusions: Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.

Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans.

Objectives: To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models. Methods: This single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.1 months (range 5-52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike's and Bayesian information criteria (AIC & BIC). Results: 420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs. Individuals with lower vBMD at any spine level had higher odds for VFs (L1-5, prevalent VF: OR,95%-CI,p: 2.2, 1.4-3.5,p=0.001; incident VF: 3.5, 1.8-6.9,p<0.001). In contrast, VF status (2.15, 0.72-6.43,p=0.170) and count (1.38, 0.89-2.12,p=0.147) performed worse in incident VF prediction. Information criteria revealed best fit for vBMD-based models (AIC vBMD=165.2; VF status=181.0; count=180.7). Conclusions: VF prediction based on automatically extracted vBMD from routine clinical MDCT outperforms prediction models based on VF status and count. These findings underline the importance of opportunistic quantitative osteoporosis screening in clinical routine MDCT data.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features.

PURPOSE: To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS: A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS: Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION: Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.

BACKGROUND: Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare. METHODS: A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level. RESULTS: On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05). CONCLUSIONS: AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients. CRITICAL RELEVANCE STATEMENT: Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions.

Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and stroke, intracranial hemorrhage and trauma, while CT angiography (CTA) has become the standard for first-line cerebrovascular evaluation; however, resulting improvements in patient management and clinical outcomes come at the cost of radiation exposure, increasing the risk for secondary morbidity. Therefore, radiation dose optimization should always be part of technical advancements in CT imaging but how can the dose be optimized? What dose reduction can be achieved without compromising diagnostic value, and what is the potential of the upcoming technologies artificial intelligence and photon counting CT? In this article, we look for answers to these questions by reviewing dose reduction techniques with respect to the major clinical indications of NCCT and CTA of the head, including a brief perspective on what to expect from current and future developments in CT technology with respect to radiation dose optimization.

Computed Tomography of the Spine : Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, new indications continued to emerge, such as assessment of vertebral fractures or degenerative spine disease, preoperative and postoperative evaluation, or CT-guided interventions at the spine; however, improvements in patient management and clinical outcomes come along with higher radiation exposure, which increases the risk for secondary malignancies. Therefore, technical developments in CT acquisition and reconstruction must always include efforts to reduce the radiation dose. But how exactly can the dose be reduced? What amount of dose reduction can be achieved without compromising the clinical value of spinal CT examinations and what can be expected from the rising stars in CT technology: artificial intelligence and photon counting CT? In this article, we try to answer these questions by systematically reviewing dose reduction techniques with respect to the major clinical indications of spinal CT. Furthermore, we take a concise look on the dose reduction potential of future developments in CT hardware and software.

Associations of Texture Features of Proton Density Fat Fraction Maps between Lumbar Vertebral Bone Marrow and Paraspinal Musculature.

Chemical shift encoding-based water−fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) has been used for non-invasive assessment of regional body fat distributions. More recently, texture analysis (TA) has been proposed to reveal even more detailed information about the vertebral or muscular composition beyond PDFF. The aim of this study was to investigate associations between vertebral bone marrow and paraspinal muscle texture features derived from CSE-MRI-based PDFF maps in a cohort of healthy subjects. In this study, 44 healthy subjects (13 males, 55 ± 30 years; 31 females, 39 ± 17 years) underwent 3T MRI including a six-echo three-dimensional (3D) spoiled gradient echo sequence used for CSE-MRI at the lumbar spine and the paraspinal musculature. The erector spinae muscles (ES), the psoas muscles (PS), and the vertebral bodies L1-4 (LS) were manually segmented. Mean PDFF values and texture features were extracted for each compartment. Features were compared between males and females using logistic regression analysis adjusted for age and body mass index (BMI). All texture features of ES except for Sum Average were significantly (p < 0.05) different between men and women. The three global texture features (Variance, Skewness, Kurtosis) for PS as well as LS showed a significant difference between male and female subjects (p < 0.05). Mean PDFF measured in PS and ES was significantly higher in females, but no difference was found for the vertebral bone marrow's PDFF. Partial correlation analysis between the texture features of the spine and the paraspinal muscles revealed a highly significant correlation for Variance(global) (r = 0.61 for ES, r = 0.62 for PS; p < 0.001 respectively). Texture analysis using PDFF maps based on CSE-MRI revealed differences between healthy male and female subjects. Global texture features in the lumbar vertebral bone marrow allowed for differentiation between men and women, when the overall PDFF was not significantly different, indicating that PDFF maps may contain detailed and subtle textural information beyond fat fraction. The observed significant correlation of Variance(global) suggests a metabolic interrelationship between vertebral bone marrow and the paraspinal muscles.

Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data-A Pilot Study.

(1) Background: To study the feasibility of developing finite element (FE) models of the whole lumbar spine using clinical routine multi-detector computed tomography (MDCT) scans to predict failure load (FL) and range of motion (ROM) parameters. (2) Methods: MDCT scans of 12 subjects (6 healthy controls (HC), mean age ± standard deviation (SD): 62.16 ± 10.24 years, and 6 osteoporotic patients (OP), mean age ± SD: 65.83 ± 11.19 years) were included in the current study. Comprehensive FE models of the lumbar spine (5 vertebrae + 4 intervertebral discs (IVDs) + ligaments) were generated (L1−L5) and simulated. The coefficients of correlation (ρ) were calculated to investigate the relationship between FE-based FL and ROM parameters and bone mineral density (BMD) values of L1−L3 derived from MDCT (BMDQCT-L1-3). Finally, Mann−Whitney U tests were performed to analyze differences in FL and ROM parameters between HC and OP cohorts. (3) Results: Mean FE-based FL value of the HC cohort was significantly higher than that of the OP cohort (1471.50 ± 275.69 N (HC) vs. 763.33 ± 166.70 N (OP), p < 0.01). A strong correlation of 0.8 (p < 0.01) was observed between FE-based FL and BMDQCT-L1-L3 values. However, no significant differences were observed between ROM parameters of HC and OP cohorts (p = 0.69 for flexion; p = 0.69 for extension; p = 0.47 for lateral bending; p = 0.13 for twisting). In addition, no statistically significant correlations were observed between ROM parameters and BMDQCT- L1-3. (4) Conclusions: Clinical routine MDCT data can be used for patient-specific FE modeling of the whole lumbar spine. ROM parameters do not seem to be significantly altered between HC and OP. In contrast, FE-derived FL may help identify patients with increased osteoporotic fracture risk in the future.

Finite Element Analysis of Osteoporotic and Osteoblastic Vertebrae and Its Association With the Proton Density Fat Fraction From Chemical Shift Encoding-Based Water-Fat MRI - A Preliminary Study.

Purpose: Osteoporosis is prevalent and entails alterations of vertebral bone and marrow. Yet, the spine is also a common site of metastatic spread. Parameters that can be non-invasively measured and could capture these alterations are the volumetric bone mineral density (vBMD), proton density fat fraction (PDFF) as an estimate of relative fat content, and failure displacement and load from finite element analysis (FEA) for assessment of bone strength. This study's purpose was to investigate if osteoporotic and osteoblastic metastatic changes in lumbar vertebrae can be differentiated based on the abovementioned parameters (vBMD, PDFF, and measures from FEA), and how these parameters correlate with each other. Materials and Methods: Seven patients (3 females, median age: 77.5 years) who received 3-Tesla magnetic resonance imaging (MRI) and multi-detector computed tomography (CT) of the lumbar spine and were diagnosed with either osteoporosis (4 patients) or diffuse osteoblastic metastases (3 patients) were included. Chemical shift encoding-based water-fat MRI (CSE-MRI) was used to extract the PDFF, while vBMD was extracted after automated vertebral body segmentation using CT. Segmentation masks were used for FEA-based failure displacement and failure load calculations. Failure displacement, failure load, and PDFF were compared between patients with osteoporotic vertebrae versus patients with osteoblastic metastases, considering non-fractured vertebrae (L1-L4). Associations between those parameters were assessed using Spearman correlation. Results: Median vBMD was 59.3 mg/cm3 in osteoporotic patients. Median PDFF was lower in the metastatic compared to the osteoporotic patients (11.9% vs. 43.8%, p=0.032). Median failure displacement and failure load were significantly higher in metastatic compared to osteoporotic patients (0.874 mm vs. 0.348 mm, 29,589 N vs. 3,095 N, p=0.034 each). A strong correlation was noted between PDFF and failure displacement (rho -0.679, p=0.094). A very strong correlation was noted between PDFF and failure load (rho -0.893, p=0.007). Conclusion: PDFF as well as failure displacement and load allowed to distinguish osteoporotic from diffuse osteoblastic vertebrae. Our findings further show strong associations between PDFF and failure displacement and load, thus may indicate complimentary pathophysiological associations derived from two non-invasive techniques (CSE-MRI and CT) that inherently measure different properties of vertebral bone and marrow.

Level-Specific Volumetric BMD Threshold Values for the Prediction of Incident Vertebral Fractures Using Opportunistic QCT: A Case-Control Study.

Purpose: To establish and evaluate the diagnostic accuracy of volumetric bone mineral density (vBMD) threshold values at different spinal levels, derived from opportunistic quantitative computed tomography (QCT), for the prediction of incident vertebral fractures (VF). Materials and Methods: In this case-control study, 35 incident VF cases (23 women, 12 men; mean age: 67 years) and 70 sex- and age-matched controls were included, based on routine multi detector CT (MDCT) scans of the thoracolumbar spine. Trabecular vBMD was measured from routine baseline CT scans of the thoracolumbar spine using an automated pipeline including vertebral segmentation, asynchronous calibration for HU-to-vBMD conversion, and correction of intravenous contrast medium (https://anduin.bonescreen.de). Threshold values at T1-L5 were calculated for the optimal operating point according to the Youden index and for fixed sensitivities (60 - 85%) in receiver operating characteristic (ROC) curves. Results: vBMD at each single level of the thoracolumbar spine was significantly associated with incident VFs (odds ratio per SD decrease [OR], 95% confidence interval [CI] at T1-T4: 3.28, 1.66-6.49; at T5-T8: 3.28, 1.72-6.26; at T9-T12: 3.37, 1.78-6.36; and at L1-L4: 3.98, 1.97-8.06), independent of adjustment for age, sex, and prevalent VF. AUC showed no significant difference between vertebral levels and was highest at the thoracolumbar junction (AUC = 0.75, 95%-CI = 0.63 - 0.85 for T11-L2). Optimal threshold values increased from lumbar (L1-L4: 52.0 mg/cm³) to upper thoracic spine (T1-T4: 69.3 mg/cm³). At T11-L2, T12-L3 and L1-L4, a threshold of 80.0 mg/cm³ showed sensitivities of 85 - 88%, and specificities of 41 - 49%. To achieve comparable sensitivity (85%) at more superior spinal levels, resulting thresholds were higher: 114.1 mg/cm³ (T1-T4), 92.0 mg/cm³ (T5-T8), 88.2 mg/cm³ (T9-T12). Conclusions: At all levels of the thoracolumbar spine, lower vBMD was associated with incident VFs in an elderly, predominantly oncologic patient population. Automated opportunistic osteoporosis screening of vBMD along the entire thoracolumbar spine allows for risk assessment of imminent VFs. We propose level-specific vBMD threshold at the thoracolumbar spine to identify individuals at high fracture risk.

Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk.

Osteoporosis is a highly prevalent systemic skeletal disease that is characterized by low bone mass and microarchitectural bone deterioration. It predisposes to fragility fractures that can occur at various sites of the skeleton, but vertebral fractures (VFs) have been shown to be particularly common. Prevention strategies and timely intervention depend on reliable diagnosis and prediction of the individual fracture risk, and dual-energy X-ray absorptiometry (DXA) has been the reference standard for decades. Yet, DXA has its inherent limitations, and other techniques have shown potential as viable add-on or even stand-alone options. Specifically, three-dimensional (3 D) imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), are playing an increasing role. For CT, recent advances in medical image analysis now allow automatic vertebral segmentation and value extraction from single vertebral bodies using a deep-learning-based architecture that can be implemented in clinical practice. Regarding MRI, a variety of methods have been developed over recent years, including magnetic resonance spectroscopy (MRS) and chemical shift encoding-based water-fat MRI (CSE-MRI) that enable the extraction of a vertebral body's proton density fat fraction (PDFF) as a promising surrogate biomarker of bone health. Yet, imaging data from CT or MRI may be more efficiently used when combined with advanced analysis techniques such as texture analysis (TA; to provide spatially resolved assessments of vertebral body composition) or finite element analysis (FEA; to provide estimates of bone strength) to further improve fracture prediction. However, distinct and experimentally validated diagnostic criteria for osteoporosis based on CT- and MRI-derived measures have not yet been achieved, limiting broad transfer to clinical practice for these novel approaches. KEY POINTS:: · DXA is the reference standard for diagnosis and fracture prediction in osteoporosis, but it has important limitations.. · CT- and MRI-based methods are increasingly used as (opportunistic) approaches.. · For CT, particularly deep-learning-based automatic vertebral segmentation and value extraction seem promising.. · For MRI, multiple techniques including spectroscopy and chemical shift imaging are available to extract fat fractions.. · Texture and finite element analyses can provide additional measures for vertebral body composition and bone strength.. CITATION FORMAT: · Sollmann N, Kirschke JS, Kronthaler S et al. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. Fortschr Röntgenstr 2022; 194: 1088 - 1099.

Automated Opportunistic Osteoporosis Screening in Routine Computed Tomography of the Spine: Comparison With Dedicated Quantitative CT.

Opportunistic osteoporosis screening in nondedicated routine computed tomography (CT) is of increasing importance. The purpose of this study was to compare lumbar volumetric bone mineral density (vBMD) assessed by a convolutional neural network (CNN)-based framework in routine CT to vBMD from dedicated quantitative CT (QCT), and to evaluate the ability of vBMD and surrogate measurements of Hounsfield units (HU) to distinguish between patients with and without osteoporotic vertebral fractures (VFs). A total of 144 patients (median age: 70.7 years, 93 females) with clinical routine CT (eight different CT scanners, 120 kVp or 140 kVp, with and without intravenous contrast medium) and dedicated QCT acquired within ≤30 days were included. Vertebral measurements included (i) vBMD from the CNN-based approach including automated vertebral body labeling, segmentation, and correction of the contrast media phase for routine CT data (vBMD_OPP), (ii) vBMD from dedicated QCT (vBMD_QCT), and (iii) noncalibrated HU from vertebral bodies of routine CT data as previously proposed for immanent opportunistic osteoporosis screening based on CT attenuation. The intraclass correlation coefficient (ICC) for vBMD_QCT versus vBMD_OPP indicated better agreement (ICC = 0.913) than the ICC for vBMD_QCT versus noncalibrated HU (ICC = 0.704). Bland-Altman analysis showed data points from 137 patients (95.1%) within the limits of agreement (LOA) of -23.2 to 25.0 mg/cm3 for vBMD_QCT versus vBMD_OPP. Osteoporosis (vBMD <80 mg/cm3 ) was detected in 89 patients (vBMD_QCT) and 88 patients (vBMD_OPP), whereas no patient crossed the diagnostic thresholds from normal vBMD to osteoporosis or vice versa. In a subcohort of 88 patients (thoracolumbar spine covered by imaging for VF reading), 69 patients showed one or more prevalent VFs, and the performance for discrimination between patients with and without VFs was best for vBMD_OPP (area under the curve [AUC] = 0.862; 95% confidence interval [CI], 0.771-0.953). In conclusion, automated opportunistic osteoporosis screening in routine CT of various scanner setups is feasible and may demonstrate high diagnostic accuracy for prevalent VFs. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Preconditioned water-fat total field inversion: Application to spine quantitative susceptibility mapping.

PURPOSE: To (a) develop a preconditioned water-fat total field inversion (wfTFI) algorithm that directly estimates the susceptibility map from complex multi-echo gradient echo data for water-fat regions and to (b) evaluate the performance of the proposed wfTFI quantitative susceptibility mapping (QSM) method in comparison with a local field inversion (LFI) method and a linear total field inversion (TFI) method in the spine. METHODS: Numerical simulations and in vivo spine multi-echo gradient echo measurements were performed to compare wfTFI to an algorithm based on disjoint background field removal (BFR) and LFI and to a formerly proposed TFI algorithm. The data from 1 healthy volunteer and 10 patients with metastatic bone disease were included in the analysis. Clinical routine computed tomography (CT) images were used as a reference standard to distinguish osteoblastic from osteolytic changes. The ability of the QSM methods to distinguish osteoblastic from osteolytic changes was evaluated. RESULTS: The proposed wfTFI method was able to decrease the normalized root mean square error compared to the LFI and TFI methods in the simulation. The in vivo wfTFI susceptibility maps showed reduced BFR artifacts, noise amplification, and streaking artifacts compared to the LFI and TFI maps. wfTFI provided a significantly higher diagnostic confidence in differentiating osteolytic and osteoblastic lesions in the spine compared to the LFI method (p = .012). CONCLUSION: The proposed wfTFI method can minimize BFR artifacts, noise amplification, and streaking artifacts in water-fat regions and can thus better differentiate between osteoblastic and osteolytic changes in patients with metastatic disease compared to LFI and the original TFI method.

Association of Cervical and Lumbar Paraspinal Muscle Composition Using Texture Analysis of MR-Based Proton Density Fat Fraction Maps.

In this study, the associations of cervical and lumbar paraspinal musculature based on a texture analysis of proton density fat fraction (PDFF) maps were investigated to identify gender- and anatomical location-specific structural patterns. Seventy-nine volunteers (25 men, 54 women) participated in the present study (mean age ± standard deviation: men: 43.7 ± 24.6 years; women: 37.1 ± 14.0 years). Using manual segmentations of the PDFF maps, texture analysis was performed and texture features were extracted. A significant difference in the mean PDFF between men and women was observed in the erector spinae muscle (p < 0.0001), whereas the mean PDFF did not significantly differ in the cervical musculature and the psoas muscle (p > 0.05 each). Among others, Variance(global) and Kurtosis(global) showed significantly higher values in men than in women in all included muscle groups (p < 0.001). Not only the mean PDFF values (p < 0.001) but also Variance(global) (p < 0.001), Energy (p < 0.001), Entropy (p = 0.01), Homogeneity (p < 0.001), and Correlation (p = 0.037) differed significantly between the three muscle compartments. The cervical and lumbar paraspinal musculature composition seems to be gender-specific and has anatomical location-specific structural patterns.

Multi-detector computed tomography (MDCT) imaging: association of bone texture parameters with finite element analysis (FEA)-based failure load of single vertebrae and functional spinal units.

BACKGROUND: Osteoporosis is a systemic skeletal disease that is characterized by low bone mass and microarchitectural deterioration, predisposing affected individuals to fragility fractures. Yet, standard measurement of areal bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) as the current reference standard has limitations for correctly detecting osteoporosis and fracture risk, with opportunistic osteoporosis screening using computed tomography (CT) showing increasing importance. This study's objective is to compare finite element analysis (FEA)-based vertebral failure load with parameters of texture analysis (TA) derived from multi-detector CT (MDCT). METHODS: MDCT data of seven subjects (mean age: 71.9±7.4 years) were included for FEA and TA. Manual segmentation was performed for the vertebral bodies T11, T12, L1, and L2 and the intervertebral discs (IVDs) T11/12, T12/L1, L1/2, and L2/3. Correlation analyses between FEA-derived failure loads and parameters of TA for the single vertebrae and two functional spinal units (FSUs) were calculated, defining FSU-1 as T11-IVD-T12-IVD-L1 and FSU-2 as T12-IVD-L1-IVD-L2. Furthermore, multivariate regressions were performed to identify the texture parameters that predicted the failure load best. RESULTS: For single vertebrae, the strongest correlations were observed for skewnessglobal, kurtosisglobal, and gray level variance (rho =-0.7668 to -0.7362; P<0.001), while for FSUs, SumAverage, long-run emphasis, long-run low gray-level emphasis, homogeneity, and energy showed the strongest correlations (rho =-0.8187 to 0.8407; P<0.05) to failure loads. SumAverage best predicted the failure load for single vertebrae (R2 adj =0.523, P<0.001). For the two FSUs, kurtosisglobal (FSU-1: R2 adj =0.611, P=0.001) and skewnessglobal (FSU-2: R2 adj =0.579, P=0.002) were the best predictors. CONCLUSIONS: TA using MDCT data of the spine was significantly associated with FEA-derived failure loads of both, single vertebrae and FSUs. Texture parameters predicted failure loads of FSUs as a more realistic in-vivo scenario equally well as compared to single vertebrae analyses. TA may reflect a less complex and time-consuming approach to accurately and non-invasively evaluate vertebral bone strength.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment.

BACKGROUND: Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment. METHODS: MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted. RESULTS: Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle. CONCLUSIONS: Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD.

PURPOSE: In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine. METHODS: 16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline. Volumetric bone mineral density (vBMD), finite element analysis (FEA)-based failure load (FL) and failure displacement (FD), as well as 24 texture features were extracted from L1 - L3 and averaged. Odds ratios (OR) with 95% confidence intervals (CI), expressed per standard deviation decrease, receiver operating characteristic (ROC) area under the curve (AUC), as well as logistic regression models, including all analyzed parameters as independent variables, were used to assess the prediction of incident vertebral fractures. RESULTS: The texture feature Correlation (AUC = 0.754, p = 0.014; OR = 2.76, CI = 1.16-6.58) and vBMD (AUC = 0.750, p = 0.016; OR = 2.67, CI = 1.12-6.37) classified incident vertebral fractures best, while the best FEA-based parameter FL showed an AUC = 0.719 (p = 0.035). Correlation was the only significant predictor of incident fractures in the logistic regression analysis of all parameters (p = 0.022). CONCLUSION: MDCT-derived FEA parameters and texture features, averaged from L1 - L3, showed only a moderate, but no statistically significant improvement of incident vertebral fracture prediction beyond BMD, supporting the hypothesis that vertebral-specific parameters may be superior for fracture risk assessment.

Regional variation of thigh muscle fat infiltration in patients with neuromuscular diseases compared to healthy controls.

BACKGROUND: Chemical shift encoding-based water-fat magnetic resonance imaging (CSE-MRI) measures a quantitative biomarker: the proton density fat fraction (PDFF). The aim was to assess regional and proximo-distal PDFF variations at the thigh in patients with myotonic dystrophy type 2 (DM2), limb-girdle muscular dystrophy type 2A (LGMD2A), and late-onset Pompe disease (LOPD) as compared to healthy controls. METHODS: Seven patients (n=2 DM2, n=2 LGMD2A, n=3 LOPD) and 20 controls were recruited. A 3D-spoiled gradient echo sequence was used to scan the thigh musculature. Muscles were manually segmented to generate mean muscle PDFF. RESULTS: In all three disease entities, there was an increase in muscle fat replacement compared to healthy controls. However, within each disease group, there were patients with a shorter time since symptom onset that only showed mild PDFF elevation (range, 10% to 20%) compared to controls (P≤0.05), whereas patients with a longer period since symptom onset showed a more severe grade of fat replacement with a range of 50% to 70% (P<0.01). Increased PDFF of around 5% was observed for vastus medialis, semimembranosus and gracilis muscles in advanced compared to early DM2. LGMD2A_1 showed an early disease stage with predominantly mild PDFF elevations over all muscles and levels (10.9%±7.1%) compared to controls. The quadriceps, gracilis and biceps femoris muscles showed the highest difference between LGMD2A_1 with 5 years since symptom onset (average PDFF 11.1%±6.9%) compared to LGMD2A_2 with 32 years since symptom onset (average PDFF 66.3%±6.3%). For LOPD patients, overall PDFF elevations were observed in all major hip flexors and extensors (range, 25.8% to 30.8%) compared to controls (range, 1.7% to 2.3%, P<0.05). Proximal-to-distal PDFF highly varied within and between diseases and within controls. The intra-reader reliability was high (reproducibility coefficient ≤2.19%). CONCLUSIONS: By quantitatively measuring muscle fat infiltration at the thigh, we identified candidate muscles for disease monitoring due to their gradual PDFF elevation with longer disease duration. Regional variation between proximal, central, and distal muscle PDFF was high and is important to consider when performing longitudinal MRI follow-ups in the clinical setting or in longitudinal studies.

MDCT-Based Finite Element Analyses: Are Measurements at the Lumbar Spine Associated with the Biomechanical Strength of Functional Spinal Units of Incidental Osteoporotic Fractures along the Thoracolumbar Spine?

Assessment of osteoporosis-associated fracture risk during clinical routine is based on the evaluation of clinical risk factors and T-scores, as derived from measurements of areal bone mineral density (aBMD). However, these parameters are limited in their ability to identify patients at high fracture risk. Finite element models (FEMs) have shown to improve bone strength prediction beyond aBMD. This study aims to investigate whether FEM measurements at the lumbar spine can predict the biomechanical strength of functional spinal units (FSUs) with incidental osteoporotic vertebral fractures (VFs) along the thoracolumbar spine. Multi-detector computed tomography (MDCT) data of 11 patients (5 females and 6 males, median age: 67 years) who underwent MDCT twice (median interval between baseline and follow-up MDCT: 18 months) and sustained an incidental osteoporotic VF between baseline and follow-up scanning were used. Based on baseline MDCT data, two FSUs consisting of vertebral bodies and intervertebral discs (IVDs) were modeled: one standardly capturing L1-IVD-L2-IVD-L3 (FSU_L1-L3) and one modeling the incidentally fractured vertebral body at the center of the FSU (FSU_F). Furthermore, volumetric BMD (vBMD) derived from MDCT, FEM-based displacement, and FEM-based load of the single vertebrae L1 to L3 were determined. Statistically significant correlations (adjusted for a BMD ratio of fracture/L1-L3 segments) were revealed between the FSU_F and mean load of L1 to L3 (r = 0.814, p = 0.004) and the mean vBMD of L1 to L3 (r = 0.745, p = 0.013), whereas there was no statistically significant association between the FSU_F and FSU_L1-L3 or between FSU_F and the mean displacement of L1 to L3 (p > 0.05). In conclusion, FEM measurements of single vertebrae at the lumbar spine may be able to predict the biomechanical strength of incidentally fractured vertebral segments along the thoracolumbar spine, while FSUs seem to predict only segment-specific fracture risk.

Association of Thigh Muscle Strength with Texture Features Based on Proton Density Fat Fraction Maps Derived from Chemical Shift Encoding-Based Water-Fat MRI.

PURPOSE: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water-fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF. METHODS: 30 healthy subjects (age = 30 ± 6 years; 15 females) underwent CSE-MRI of the lumbar spine at 3T, using a six-echo 3D spoiled gradient echo sequence. Quadriceps (EXT) and ischiocrural (FLEX) muscles were segmented to extract mean PDFF and texture features. Muscle flexion and extension strength were measured with an isokinetic dynamometer. RESULTS: Of the eleven extracted texture features, Variance(global) showed the highest significant correlation with extension strength (p < 0.001, R2adj = 0.712), and Correlation showed the highest significant correlation with flexion strength (p = 0.016, R2adj = 0.658). Multivariate linear regression models identified Variance(global) and sex, but not PDFF, as significant predictors of extension strength (R2adj = 0.709; p < 0.001), while mean PDFF, sex, and BMI, but none of the texture features, were identified as significant predictors of flexion strength (R2adj = 0.674; p < 0.001). CONCLUSIONS: Prediction of quadriceps muscle strength can be improved beyond mean PDFF by means of TA, indicating the capability to quantify muscular fat infiltration patterns.