Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women

More accurate trabecular bone imaging using UTE MRI at the resonance frequency of fat

High-resolution magnetic resonance imaging (HR-MRI) has been increasingly used to assess the trabecular bone structure. High susceptibility at the marrow/bone interface may significantly reduce the marrow's apparent transverse relaxation time (T2*), overestimating trabecular bone thickness. Ultrashort echo time MRI (UTE-MRI) can minimize the signal loss caused by susceptibility-induced T2* shortening. However, UTE-MRI is sensitive to chemical shift artifacts, which manifest as spatial blurring and ringing artifacts partially due to non-Cartesian sampling. In this study, we proposed UTE-MRI at the resonance frequency of fat to minimize marrow-related chemical shift artifacts and the overestimation of trabecular thickness. Cubes of trabecular bone from six donors (75 ± 4 years old) were scanned using a 3 T clinical scanner at the resonance frequencies of fat and water, respectively, using 3D UTE sequences with five TEs (0.032, 1.1, 2.2, 3.3, and 4.4 ms) and a clinical 3D gradient echo (GRE) sequence at 0.2 × 0.2 × 0.4 mm3 voxel size. Trabecular bone thickness was measured in 30 regions of interest (ROIs) per sample. MRI results were compared with thicknesses obtained from micro-computed tomography (μCT) at 50 μm3 voxel size. Linear regression models were used to calculate the coefficient of determination between MRI- and μCT-based trabecular thickness. All MRI-based trabecular thicknesses showed significant correlations with μCT measurements. The correlations were higher (examined with paired Student's t-test, P < 0.01) for 3D UTE images performed at the fat frequency (R2 = 0.59-0.74, P < 0.01) than those at the water frequency (R2 = 0.18-0.52, P < 0.01) and clinical GRE images (R2 = 0.39-0.47, P < 0.01). Significantly reduced correlations were observed with longer TEs. This study highlighted the feasibility of UTE-MRI at the fat frequency for a more accurate assessment of trabecular bone thickness.

Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications

Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.

Bone Biomarkers Based on Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is increasingly used to evaluate the microstructural and compositional properties of bone. MRI-based biomarkers can characterize all major compartments of bone: organic, water, fat, and mineral components. However, with a short apparent spin-spin relaxation time (T2*), bone is invisible to conventional MRI sequences that use long echo times. To address this shortcoming, ultrashort echo time MRI sequences have been developed to provide direct imaging of bone and establish a set of MRI-based biomarkers sensitive to the structural and compositional changes of bone. This review article describes the MRI-based bone biomarkers representing total water, pore water, bound water, fat fraction, macromolecular fraction in the organic matrix, and surrogates for mineral density. MRI-based morphological bone imaging techniques are also briefly described.

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.

Measuring Drug Therapy Effect on Osteoporotic Fracture Risk by Trabecular Bone Lacunarity: The LOTO Study

An MRI method providing one parameter (TBLβ: trabecular-bone-lacunarity-parameter-β) that is sensitive to trabecular bone architecture (TBA) changes with aging and osteoporosis is under study as a new tool in the early diagnosis of bone fragility fracture. A cross-sectional and prospective observational study (LOTO: Lacunarity Of Trabecular bone in Osteoporosis) on over-50s women, at risk for bone fragility fracture, was designed to validate the method. From the baseline data, we observed that in women with prevalent vertebral fractures (VF+), TBA was differently characterized by TBLβ when osteoporosis treatment is considered. Here we verify the potential of TBLβ as an index of osteoporosis treatment efficacy. Untreated (N = 156) and treated (N = 123) women were considered to assess differences in TBLβ related to osteoporosis treatment. Prevalent VFs were found in 31% of subjects, 63% of which were under osteoporosis medications. The results show that TBLβ discriminates between VF+ and VF− patients (p = 0.004). This result is mostly stressed in untreated subjects. Treatment, drug therapy in particular (89% Bisphosphonates), significantly counteracts the difference between VF+ and VF− within and between groups: TBLβ values in treated patients are comparable to untreated VF− and statistically higher than untreated VF+ (p = 0.014) ones. These results highlight the potential role of TBLβ as an index of treatment efficacy.

Aging and Bone Metabolism

Changes in bone architecture and metabolism with aging increase the likelihood of osteoporosis and fracture. Age-onset osteoporosis is multifactorial, with contributory extrinsic and intrinsic factors including certain medical problems, specific prescription drugs, estrogen loss, secondary hyperparathyroidism, microenvironmental and cellular alterations in bone tissue, and mechanical unloading or immobilization. At the histological level, there are changes in trabecular and cortical bone as well as marrow cellularity, lineage switching of mesenchymal stem cells to an adipogenic fate, inadequate transduction of signals during skeletal loading, and predisposition toward senescent cell accumulation with production of a senescence-associated secretory phenotype. Cumulatively, these changes result in bone remodeling abnormalities that over time cause net bone loss typically seen in older adults. Age-related osteoporosis is a geriatric syndrome due to the multiple etiologies that converge upon the skeleton to produce the ultimate phenotypic changes that manifest as bone fragility. Bone tissue is dynamic but with tendencies toward poor osteoblastic bone formation and relative osteoclastic bone resorption with aging. Interactions with other aging physiologic systems, such as muscle, may also confer detrimental effects on the aging skeleton. Conversely, individuals who maintain their BMD experience a lower risk of fractures, disability, and mortality, suggesting that this phenotype may be a marker of successful aging. © 2023 American Physiological Society. Compr Physiol 13:4355-4386, 2023.

Quantifying calcium changes in the fetal spine using quantitative susceptibility mapping as extracted from STAGE imaging

OBJECTIVES

To evaluate calcium deposition in the fetal spine in vivo during the second and third trimesters using quantitative susceptibility mapping (QSM).

METHODS

Fifty-four pregnant women in their second and third trimesters underwent a 2D multi-echo STrategically Acquired Gradient Echo (STAGE) MR imaging protocol at 3T covering the fetal spine. The first echo data was used for QSM processing. A linear regression model was used to assess the correlation between magnetic susceptibility and gestational age (GA). A paired sample t-test was used to compare the consistency of QSM measurements from each sequence.

RESULTS

The magnetic susceptibility of the fetal spine decreased linearly with advancing GA, with a slope of -52.3 parts per billion (ppb)/week and a Pearson correlation coefficient (r) of 0.83 (p < 0.001). In 37 subjects for whom the STAGE local QSM data were available from both flip angles, the average magnetic susceptibility values were -1111 ± 278 ppb and -1081 ± 262 ppb for FA = 8° and FA = 40°, respectively. These means were not statistically different according to a paired sample t-test (p = 0.156).

CONCLUSIONS

QSM is a reliable technique for evaluating calcium deposition and bone mineral density of fetal vertebrae. Our results demonstrate an increase in fetal calcium levels as a function of GA. These measures might be able to provide reference values for calcium content in the fetal spine during the second and third trimesters.

KEY POINTS

• Calcium deposition and mineralization in the fetal spine, evaluated by vertebral magnetic susceptibility, increased with advancing gestational age. • Our results provide reference values for calcium content in the fetal spine during the second and third trimesters.

The role of Fast spin-echo T2-weighted and diffusion-weighted imaging for spine bone marrow changes evaluation in postmenopausal women with osteoporosis

Background

To prospectively investigate the role of Fast spin-echo T2-weighted (FSE T2-w) and diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI) for detecting spine bone marrow changes in postmenopausal women with osteoporosis (OP). A total of 101 postmenopausal women, mean age of 60.97 ± 7.41 (range 52–68) years old, who underwent dual-energy X-ray absorptiometry of the spine, were invited to this study and divided into three bone density (normal, osteopenic, and osteoporotic) groups based on T-score. After that MRI scan with both FSE T2-w and DWI of the vertebral body was done to calculate the signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC). Finally, MRI findings were compared in patients, between three groups and correlated with bone marrow density.

Results

The osteoporotic group showed significantly lower mean ADC values, compared to osteopenic and normal groups (0.58 ± 0.02 vs. 0.36 ± 0.05 vs. 0.24 ± 0.06 × 10–3 mm2/s, p < 0.001). According to these results, a significant positive correlation was found between T-scores and ADC values (r = 0.652, p < 0.001). The mean SNR in FSE T2-w images for normal, osteopenic, and osteoporotic groups was calculated 5.61 ± 0.32, 5.48 ± 0.55, and 6.63 ± 0.67, respectively. No significant correlation was found between the mean SNR and T-score for all groups (r = − 0.304, p > 0.05).

Conclusions

DWI can be used as a noninvasive, quantitative, and valuable technique for OP evaluation. While, routine MRI needs more investigation to be demonstrated as a reliable diagnostic indicator for OP.

Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network

Osteoporosis degrades the quality of bones and is the primary cause of fractures in the elderly and women after menopause. The high diagnostic and treatment costs urge the researchers to find a cost-effective diagnostic system to diagnose osteoporosis in the early stages. X-ray imaging is the cheapest and most common imaging technique to detect bone pathologies butmanual interpretation of x-rays for osteoporosis is difficult and extraction of required features and selection of high-performance classifiers is a very challenging task. Deep learning systems have gained the popularity in image analysis field over the last few decades. This paper proposes a convolution neural network (CNN) based approach to detect osteoporosis from x-rays. In our study, we have used the transfer learning of deep learning-based CNNs namely AlexNet, VggNet-16, ResNet, and VggNet -19 to classify the x-ray images of knee joints into normal, osteopenia, and osteoporosis disease groups. The main objectives of the current study are: (i) to present a dataset of 381 knee x-rays medically validated by the T-scores obtained from the Quantitative Ultrasound System, and (ii) to propose a deep learning approach using transfer learning to classify different stages of the disease. The performance of these classifiers is compared and the best accuracy of 91.1% is achieved by pretrained Alexnet architecture on the presented dataset with an error rate of 0.09 and validation loss of 0.54 as compared to the accuracy of 79%, an error rate of 0.21, and validation loss of 0.544 when pretrained network was not used.. The results of the study suggest that a deep learning system with transfer learning can help clinicians to detect osteoporosis in its early stages hence reducing the risk of fractures.

Quantitative Susceptibility Mapping: Basic Methods and Clinical Applications

Quantitative susceptibility mapping (QSM), one of the advanced MRI techniques for evaluating magnetic susceptibility, offers precise quantitative measurements of spatial distributions of magnetic susceptibility. Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and is a substance-specific value. Recently, QSM has been widely used to estimate various levels of substances in the brain, including iron, hemosiderin, and deoxyhemoglobin (paramagnetism), as well as calcification (diamagnetism). By visualizing iron distribution in the brain, it is possible to identify anatomic structures that are not evident on conventional images and to evaluate various neurodegenerative diseases. It has been challenging to apply QSM in areas outside the brain because of motion artifacts from respiration and heartbeats, as well as the presence of fat, which has a different frequency to the proton. In this review, the authors provide a brief overview of the theoretical background and analyze methods of converting MRI phase images to QSM. Moreover, we provide an overview of the current clinical applications of QSM. Online supplemental material is available for this article. ^©RSNA, 2022.

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; DOI: 10.1055/a-1770-4626.

Simultaneous Quantitative Susceptibility Mapping of Articular Cartilage and Cortical Bone of Human Knee Joint Using Ultrashort Echo Time Sequences

BACKGROUND

It is of great clinical importance to assess the microstructure of the articular cartilage and cortical bone of the human knee joint. While quantitative susceptibility mapping (QSM) is a promising tool for investigating the knee joint, however, previous QSM studies using conventional gradient recalled echo sequences or ultrashort echo time (UTE) sequences only focused on mapping the magnetic susceptibility of the articular cartilage or cortical bone, respectively. Simultaneously mapping the underlying susceptibilities of the articular cartilage and cortical bone of human in vivo has not been explored and reported.

METHOD

Three-dimensional multi-echo radial UTE sequences with the shortest TE of 0.07 msec and computed tomography (CT) were performed on the bilateral knee joints of five healthy volunteers for this prospective study. UTE-QSM was reconstructed from the local field map after water-fat separation and background field removal. Spearman's correlation analysis was used to explore the relationship between the magnetic susceptibility and CT values in 158 representative regions of interest of cortical bone.

RESULT

The susceptibility properties of the articular cartilage and cortical bone were successfully quantified by UTE-QSM. The laminar structure of articular cartilage was characterized by the difference of susceptibility value in each layer. Susceptibility was mostly diamagnetic in cortical bone. A significant negative correlation (r=-0.43, p<0.001) between the susceptibility value and CT value in cortical bone was observed.

CONCLUSION

UTE-QSM enables simultaneous susceptibility mapping of the articular cartilage and cortical bone of human in vivo. Good association between susceptibility and CT values in cortical bone suggests the potential of UTE-QSM for bone mapping for further clinical application.

Protective effect of resveratrol on estrogen deficiency-induced osteoporosis though attenuating NADPH oxidase 4/nuclear factor kappa B pathway by increasing miR-92b-3p expression

Introduction:

Resveratrol (RES) exhibits estrogen-like effects and has potential applications to treatment of osteoporosis caused by estrogen deficiency; however, the specific mechanism of action of RES remains unclear. Here, we examined the therapeutic effects of RES on ovariectomized (OVX) rats with osteoporosis and determined the underlying mechanism.

Methods:

We established an OVX rat model to study osteoporosis caused by estrogen deficiency. The treatment groups were given orally with RES (50, 100, and 200 mg/day), the estrogen group received 0.8 mg/kg E2 daily via oral route, and the sham-operated and control groups received an equivalent dose of sodium carboxymethylcellulose orally. After 12 weeks of treatment, we used real-time quantitative polymerase chain reaction (PCR) and Western blot analysis to measure the gene and protein expression of miR-92b-3p, Nox4, NF-κBp65, IκB, BMP2, Smad7, and RUNX-2 in bone tissues. Right femur structural parameters were evaluated by micro-CT. Dual-energy X-ray 4500 W was used to determine systemic bone mineral density (BMD). Enzyme-linked immunosorbent assay (ELISA) kits were used to determine the serum levels of bone alkaline phosphatase (BALP), osteoprotegerin (OPG), anti-tartrate acid phosphatase-5b (PTRA5b), and carboxylated terminal peptide (CTX-I). The rat femoral bone specimens were stained using hematoxylin and eosin for pathological examination

Results:

We observed increased levels of serum estrogen in both ovaries, elevated miR-92b-3p levels in bone tissues, reduced levels of Nox4, NF-κBp65, p-IκB-a, and cathepsin K, and elevated gene and protein expression of BMP2, Smad7, and RUNX-2 in the OVX rat model of osteoporosis after treatment with RES. Elevated levels of BALP, OPG, ALP, and BMD along with reduced levels of TRAP-5b and CTX-I were also observed. The structural model index (SMI) and the trabecular space (Tb. Sp) decreased, while the trabecular thickness (Tb. Th), bone volume fraction (BV/TV), trabecular number (Tb.N), and tissue bone density (Conn.D) increased, thereby improving osteoporosis induced by estrogen deficiency in both ovaries.

Conclusion:

Cathepsin K expression and Nox4/NF-κB signaling pathway were suppressed by the elevated expression of miR-92b-3p. This inhibition was pivotal in the protective effect of RES against osteoporosis induced by estrogen deficiency in both ovaries. Thus, RES efficiently alleviated osteoporosis induced by estrogen deficiency in rats.

Survey of MRI Usefulness for the Clinical Assessment of Bone Microstructure

Bone microarchitecture has been shown to provide useful information regarding the evaluation of skeleton quality with an added value to areal bone mineral density, which can be used for the diagnosis of several bone diseases. Bone mineral density estimated from dual-energy X-ray absorptiometry (DXA) has shown to be a limited tool to identify patients’ risk stratification and therapy delivery. Magnetic resonance imaging (MRI) has been proposed as another technique to assess bone quality and fracture risk by evaluating the bone structure and microarchitecture. To date, MRI is the only completely non-invasive and non-ionizing imaging modality that can assess both cortical and trabecular bone in vivo. In this review article, we reported a survey regarding the clinically relevant information MRI could provide for the assessment of the inner trabecular morphology of different bone segments. The last section will be devoted to the upcoming MRI applications (MR spectroscopy and chemical shift encoding MRI, solid state MRI and quantitative susceptibility mapping), which could provide additional biomarkers for the assessment of bone microarchitecture.

Fractal lacunarity of trabecular bone in vertebral MRI to predict osteoporotic fracture risk in over-fifties women. The LOTO study

BACKGROUND

Osteoporotic fractures are a major cause of morbidity in the elderly. Menopausal women represent the population with the highest risk of early osteoporosis onset, often accompanied by vertebral fractures (VF). Bone mineral density (BMD) is commonly assessed by dual-energy X-ray absorptiometry (DXA) for osteoporosis diagnosis; however, BMD alone does not represent a significant predictor of fracture risk. Bone microarchitecture, instead, arises as a determinant of bone fragility independent of BMD. High-resolution magnetic resonance imaging (MRI) is an effective noninvasive/nonionizing tool for in vivo characterisation of trabecular bone microarchitecture (TBA). We have previously set up an MRI method able to characterise TBA changes in aging and osteoporosis by one parameter, trabecular bone lacunarity parameter β (TBLβ). Fractal lacunarity was used for TBA texture analysis as it describes discontinuity of bone network and size of bone marrow spaces, changes of which increase the risk of bone fracture. This study aims to assess the potential of TBLβ method as a tool for osteoporotic fracture risk.

METHODS

An observational, cross-sectional, and prospective study on over-50s women at risk for VF was designed. TBLβ, our index of osteoporotic fracture risk, is the main outcome measure. It was calculated on lumbar vertebra axial images, acquired by 1.5 T MRI spin-echo technique, from 279 osteopenic/osteoporotic women with/without prior VF. Diagnostic power of TBLβ method, by Receiver Operating Characteristics (ROC) curve and other diagnostic accuracy measurements were compared with lumbar spine DXA-BMD.

RESULTS

Baseline results show that TBLβ is able to discriminate patients with/without prevalent VF (p = 0.003). AUC (area under the curve from ROC) is 0.63 for TBLβ, statistically higher (p = 0.012) than BMD one (0.53). Contribution of TBLβ to prevalent VF is statistically higher (p < 0.001) than BMD (sensitivity: 66% vs. 52% respectively; OR: 3.20, p < 0.0001 for TBLβ vs. 1.31, p = 0.297 for BMD). Preliminary 1-year prospective results suggest that TBA contribution to incident VF is even higher (sensitivity: 73% for TBLβ vs. 55% for BMD; RR: 3.00, p = 0.002 for TBLβ vs. 1.31, p = 0.380 for BMD).

CONCLUSION

Results from this study further highlight the usefulness of TBLβ as a biomarker of TBA degeneration and an index of osteoporotic fracture risk.

Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study

Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years). DXA-derived bone mineral density (BMD) measures were used as the reference standard. We categorized patients into three groups according to DXA BMD T-score: normal (T ≥ -1.0), osteopenia (-2.5 < T < -1.0), and osteoporosis (T ≤ -2.5). T-scores were calculated by using the BMD dataset of young Chinese female aged 20-40 years as a reference. A 3-class DCNN model was trained to classify normal BMD, osteoporosis, and osteopenia. Model performance was tested in a validation dataset (204 images from 102 patients) and two test datasets (396 images from 198 patients and 348 images from 147 patients respectively). Model performance was assessed by the receiver operating characteristic (ROC) curve analysis. The results showed that in the test dataset 1, the model diagnosing osteoporosis achieved an AUC of 0.767 (95% confidence interval [CI]: 0.701-0.824) with sensitivity of 73.7% (95% CI: 62.3-83.1), the model diagnosing osteopenia achieved an AUC of 0.787 (95% CI: 0.723-0.842) with sensitivity of 81.8% (95% CI: 67.3-91.8); In the test dataset 2, the model diagnosing osteoporosis yielded an AUC of 0.726 (95% CI: 0.646-0.796) with sensitivity of 68.4% (95% CI: 54.8-80.1), the model diagnosing osteopenia yielded an AUC of 0.810 (95% CI, 0.737-0.870) with sensitivity of 85.3% (95% CI, 68.9-95.0). Accordingly, a deep learning diagnostic network may have the potential in screening osteoporosis and osteopenia based on lumbar spine radiographs. However, further studies are necessary to verify and improve the diagnostic performance of DCNN models.

Quantitative Magnetic Resonance Imaging of Cortical and Trabecular Bone

Bone is a composite material consisting of mineral, organic matrix, and water. Water in bone can be categorized as bound water (BW), which is bound to bone mineral and organic matrix, or as pore water (PW), which resides in Haversian canals as well as in lacunae and canaliculi. Bone is generally classified into two types: cortical bone and trabecular bone. Cortical bone is much denser than trabecular bone that is surrounded by marrow and fat. Magnetic resonance (MR) imaging has been increasingly used for noninvasive assessment of both cortical bone and trabecular bone. Bone typically appears as a signal void with conventional MR sequences because of its short T2*. Ultrashort echo time (UTE) sequences with echo times 100 to 1,000 times shorter than those of conventional sequences allow direct imaging of BW and PW in bone. This article summarizes several quantitative MR techniques recently developed for bone evaluation. Specifically, we discuss the use of UTE and adiabatic inversion recovery prepared UTE sequences to quantify BW and PW, UTE magnetization transfer sequences to quantify collagen backbone protons, UTE quantitative susceptibility mapping sequences to assess bone mineral, and conventional sequences for high-resolution imaging of PW as well as the evaluation of trabecular bone architecture.

MRI-Based Quantitative Osteoporosis Imaging at the Spine and Femur

Osteoporosis is a systemic skeletal disease with a high prevalence worldwide, characterized by low bone mass and microarchitectural deterioration, predisposing an individual to fragility fractures. Dual-energy X-ray absorptiometry (DXA) has been the clinical reference standard for diagnosing osteoporosis and for assessing fracture risk for decades. However, other imaging modalities are of increasing importance to investigate the etiology, treatment, and fracture risk. The purpose of this work is to review the available literature on quantitative magnetic resonance imaging (MRI) methods and related findings in osteoporosis at the spine and proximal femur as the clinically most important fracture sites. Trabecular bone microstructure analysis at the proximal femur based on high-resolution MRI allows for a better prediction of osteoporotic fracture risk than DXA-based bone mineral density (BMD) alone. In the 1990s, T₂ * mapping was shown to correlate with the density and orientation of the trabecular bone. Recently, quantitative susceptibility mapping (QSM), which overcomes some of the limitations of T₂ * mapping, has been applied for trabecular bone quantifications at the spine, whereas ultrashort echo time (UTE) imaging provides valuable surrogate markers of cortical bone quantity and quality. Magnetic resonance spectroscopy (MRS) and chemical shift encoding-based water-fat MRI (CSE-MRI) enable the quantitative assessment of the nonmineralized bone compartment through extraction of the bone marrow fat fraction (BMFF). Furthermore, CSE-MRI allows for the differentiation of osteoporotic vs. pathologic fractures, which is of high clinical relevance. Lastly, advanced postprocessing and image analysis tools, particularly considering statistical parametric mapping and region-specific BMFF distributions, have high potential to further improve MRI-based fracture risk assessments at the spine and hip. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.

Quantitative susceptibility mapping of the spine using in-phase echoes to initialize inhomogeneous field and R2* for the nonconvex optimization problem of fat-water separation

Quantitative susceptibility mapping (QSM) of human spinal vertebrae from a multi-echo gradient-echo (GRE) sequence is challenging, because comparable amounts of fat and water in the vertebrae make it difficult to solve the nonconvex optimization problem of fat-water separation (R2*-IDEAL) for estimating the magnetic field induced by tissue susceptibility. We present an in-phase (IP) echo initialization of R2*-IDEAL for QSM in the spinal vertebrae. Ten healthy human subjects were recruited for spine MRI. A 3D multi-echo GRE sequence was implemented to acquire out-phase and IP echoes. For the IP method, the R2* and field maps estimated by separately fitting the magnitude and phase of IP echoes were used to initialize gradient search R2*-IDEAL to obtain final R2*, field, water, and fat maps, and the final field map was used to generate QSM. The IP method was compared with the existing Zero method (initializing the field to zero), VARPRO-GC (variable projection using graphcuts but still initializing the field to zero), and SPURS (simultaneous phase unwrapping and removal of chemical shift using graphcuts for initialization) on both simulation and in vivo data. The single peak fat model was also compared with the multi-peak fat model. There was no substantial difference on QSM between the single peak and multi-peak fat models, but there were marked differences among different initialization methods. The simulations demonstrated that IP provided the lowest error in the field map. Compared to Zero, VARPRO-GC and SPURS, the proposed IP method provided substantially improved spine QSM in all 10 subjects.

Significant correlations between human cortical bone mineral density and quantitative susceptibility mapping (QSM) obtained with 3D Cones ultrashort echo time magnetic resonance imaging (UTE-MRI)

PURPOSE

Quantitative susceptibility mapping (QSM) MRI is a tool that can characterize changes in susceptibility, an intrinsic property which is associated with compositional changes in the tissue. Current QSM estimation of cortical bone is challenging because conventional clinical MRI cannot acquire signal in cortical bone. This study aimed to implement Cones 3D ultrashort echo time MRI (UTE-MRI) for ex vivo QSM measurements in human tibial cortical bone, investigating the correlations of QSM with volumetric intracortical bone mineral density (BMD).

MATERIALS AND METHODS

Nine tibial midshaft cortical bone specimens (25 mm long specimens cut at the mid-point of tibial shaft, 67 ± 20 years old, 5 women and 4 men) were scanned on a clinical 3 T MRI scanner for QSM measurement. The specimens were also scanned on a high-resolution micro-computed tomography (μCT) scanner for volumetric BMD estimation. QSM and μCT results were compared at approximately nine regions of interest (ROIs) per specimen.

RESULTS

Average 3D UTE-MRI QSM showed significantly strong correlation with volumetric BMD (R = -0.82, P < 0.01) and bone porosity (R = 0.72, P < 0.01). Combining all data points together (77 ROIs), QSM showed significant moderate to strong correlation with volumetric BMD after correction for interdependencies in specimens (R = -0.70, P < 0.01). The corrections were required because the data points were not independent in each specimen. Similarly, the correlation between QSM and porosity was significant (R = 0.68, P < 0.01).

CONCLUSIONS

These results suggest that the Cones 3D UTE-MRI QSM technique can potentially serve as a novel and accurate tool to assess intracortical bone mineral density whilst avoiding ionizing radiation.

Reproducibility of quantitative susceptibility mapping in lumbar vertebra

Background

To evaluate the reliability and reproducibility of quantitative susceptibility mapping (QSM) in the lumbar vertebra.

Methods

From May 2017 to September 2017, 61 subjects who underwent QSM MRI and quantitative computed tomography (QCT) were consecutively enrolled in this prospective study. QSM examination was performed two times with an interval of less than 1 week for each subject. For each data set, the QSM and QCT values on L1-L4 vertebral bodies were measured independently by two radiologists. The correlation coefficient between QSM and QCT values was calculated on L1-L4 vertebral bodies. The intraclass correlation coefficient (ICC) and Bland-Altman plots were used to evaluate the inter-observer reliability and the inter-scan reproducibility on QSM.

Results

A total of 61 subjects (mean age, 55.5±13.7 years) with 244 vertebral bodies were analyzed. Overall, QSM and QCT showed good correlation in the L1-L4 vertebral body, especially in the L3 (R=-0.75). QSM value showed excellent inter-observer reliability (ICC, 0.992, 95% CI: 0.985-0.996) with a mean difference of 0.35 and 95% limits of agreements of within -22.74 to 23.45 ppb, and very good inter-scan reproducibility (ICC, 0.932, 95% CI: 0.886-0.959) with a mean difference of -7.60 ppb and 95% limits of agreements of within of -92.85 to 77.62 ppb.

Conclusions

QSM in the lumbar vertebra is a reliable and reproducible technique for evaluating bone mineral density.