Quantitative analysis of therapeutic response in psoriatic arthritis of digital joints with Dual-energy CT iodine maps

Updates on the Applications of Spectral Computed Tomography for Musculoskeletal Imaging

Spectral CT represents a novel imaging approach that can noninvasively visualize, quantify, and characterize many musculoskeletal pathologies. This modality has revolutionized the field of radiology by capturing CT attenuation data across multiple energy levels and offering superior tissue characterization while potentially minimizing radiation exposure compared to traditional enhanced CT scans. Despite MRI being the preferred imaging method for many musculoskeletal conditions, it is not viable for some patients. Moreover, this technique is time-consuming, costly, and has limited availability in many healthcare settings. Thus, spectral CT has a considerable role in improving the diagnosis, characterization, and treatment of gout, inflammatory arthropathies, degenerative disc disease, osteoporosis, occult fractures, malignancies, ligamentous injuries, and other bone-marrow pathologies. This comprehensive review will delve into the diverse capabilities of dual-energy CT, a subset of spectral CT, in addressing these musculoskeletal conditions and explore potential future avenues for its integration into clinical practice.

Dual-Energy Computed Tomography and Beyond: Musculoskeletal System

Traditional monoenergetic computed tomography (CT) scans in musculoskeletal imaging provide excellent detail of bones but are limited in the evaluation of soft tissues. Dual-energy CT (DECT) overcomes many of the traditional limitations of CT and offers anatomical details previously seen only on MR imaging. In addition, DECT has benefits in the evaluation and characterization of arthropathies, bone marrow edema, and collagen applications in the evaluation of tendons, ligaments, and vertebral discs. There is current ongoing research in the application of DECT in arthrography and bone mineral density calculation.

Virtual Monochromatic Images from Dual-Energy Computed Tomography Do Not Improve the Detection of Synovitis in Hand Arthritis

The objective of this study was to investigate subtraction images from different polychromatic and virtual monochromatic reconstructions of dual-energy computed tomography (CT) for the detection of inflammation (synovitis/tenosynovitis or peritendonitis) in patients with hand arthritis. In this IRB-approved prospective study, 35 patients with acute hand arthritis underwent contrast-enhanced dual-energy CT and musculoskeletal ultrasound (MSUS) of the clinically dominant hand. CT subtractions (CT-S) were calculated from 80 and 135 kVp source data and monochromatic 50 and 70 keV images. CT-S and MSUS were scored for synovitis and tenosynovitis/peritendonitis. Specificity, sensitivity and diagnostic accuracy were assessed by using MSUS as a reference. Parameters of objective image quality were measured. Thirty-three patients were analyzed. MSUS was positive for synovitis and/or tenosynovitis/peritendonitis in 28 patients. The 70 keV images had the highest diagnostic accuracy, with 88% (vs. 50 keV, 82%; 80 kVp, 85%; and 135 kVp, 82%), and superior sensitivity, with 96% (vs. 50 keV: 86%, 80 kVp: 93% and 135 kVp: 79%). The 80 kVp images showed the highest signal- and contrast-to-noise ratio, while the 50 keV images provided the lowest image quality. While all subtraction methods of contrast-enhanced dual-energy CT proved to be able to detect inflammation with sufficient diagnostic accuracy, virtual monochromatic images with low keV showed no significant improvement over conventional subtraction techniques and lead to a loss of image quality.

Contrast-enhanced CT techniques and MRI perform equally well in arthritis imaging of the hand: a prospective diagnostic accuracy study

OBJECTIVES

To investigate the performance of dual-energy CT (DECT)-generated iodine maps (iMap) and CT subtraction (CT-S) in the detection of synovitis, tenosynovitis, and peritendonitis/paratenonitis compared to magnetic resonance imaging (MRI) using musculoskeletal ultrasound (MSUS) as standard of reference.

METHODS

This IRB-approved prospective study consecutively investigated patients with undifferentiated arthritis. All patients underwent MSUS, MRI and contrast-enhanced DECT of the hand; from the latter conventional CT-S, image-based iMap (iMap-I) and raw data-based iMap (iMap-RD) were reconstructed. CT and MRI datasets were scored for synovitis and tenosynovitis/paratenonitis applying the modified Rheumatoid Arthritis MRI Score (RAMRIS). Sensitivity, specificity, and diagnostic accuracy were calculated. Non-inferiority was tested using the one-tailed McNemar test. Correlation of sum scores was assessed using Pearson's test. Interreader reliability was assessed using Cohen's kappa.

RESULTS

Overall, 33 patients were included. MSUS was positive for synovitis and tenosynovitis/paratenonitis in 28 patients with a sum score of 6.91. Excellent correlation with MSUS was shown for CT-S (sum score 6.38; r = 0.91), iMap-RD (sum score 9.74; r = 0.82), MRI (sum score 12.70; r = 0.85), and iMap-I (sum score 6.94; r = 0.50). CT-S had the highest diagnostic accuracy of 83%, followed by iMap-I (78%), MRI (75%), and iMap-RD (74%). All modalities showed non-inferiority. Reader agreement was good for CT-S and MRI (κ = 0.62; 0.64) and fair for iMap-RD and iMap-I (κ = 0.31; 0.37).

CONCLUSION

CT-S and iMap allow highly standardized arthritis imaging and are suitable for clinical practice. MSUS still has the highest availability for arthritis imaging and served as gold standard for this study.

KEY POINTS

• CT subtraction, iodine map with dual-energy CT, and MRI showed non-inferiority to musculoskeletal ultrasound. • MRI was the most sensitive but least specific imaging technique compared with CT subtraction and dual-energy CT. • CT subtraction showed the best correlation with musculoskeletal ultrasound.

Relationship between iodine-enhanced dual energy-computed tomographic findings and ultrasonographic findings for psoriatic arthritis

Ultrasonography and magnetic resonance imaging (MRI) are useful for diagnosing psoriatic arthritis (PsA). However, ultrasonography depends on the skill of the operator and MRI is often disturbed by artifacts when distal interphalangeal joints are examined. Although iodine-enhanced dual-energy computed tomography (DECT) has the potential to diagnose PsA without these disadvantages, its usefulness over ultrasonography has not yet been examined in detail; therefore, the present study was conducted to address this issue. The acral joint of 13 PsA patients, which was the most severely affected, was scanned with imaging devices. Ultrasonography was performed with a high-frequency linear 18-MHz probe. Iodine-enhanced DECT was conducted in the DE mode with iohexol as a contrast material. Psoriatic Arthritis Screening and Evaluation (PASE) scores were recorded. Synovitis and periarticular inflammation delineated with iodine-enhanced DECT correlated with the loss of the fibrillar pattern delineated with ultrasonography (p = 0.033 and 0.002, respectively). Peritendinitis delineated with iodine-enhanced DECT also correlated with tendon thickening delineated with ultrasonography (p = 0.011). Iodine uptake did not correlate with Doppler signal or PASE scores. In conclusion, the present results demonstrated that the qualitative findings of iodine-enhanced DECT correlated with those of ultrasonography in PsA patients, whereas quantitative findings did not. Iodine-enhanced DECT may be an alternative imaging modality for the diagnosis of PsA.

Monochromatic image reconstruction via machine learning

X-ray computed tomography (CT) is a nondestructive imaging technique to reconstruct cross-sectional images of an object using x-ray measurements taken from different view angles for medical diagnosis, therapeutic planning, security screening, and other applications. In clinical practice, the x-ray tube emits polychromatic x-rays, and the x-ray detector array operates in the energy-integrating mode to acquire energy intensity. This physical process of x-ray imaging is accurately described by an energy-dependent non-linear integral equation on the basis of the Beer–Lambert law. However, the non-linear model is not invertible using a computationally efficient solution and is often approximated as a linear integral model in the form of the Radon transform, which basically loses energy-dependent information. This approximate model produces an inaccurate quantification of attenuation images, suffering from beam-hardening effects. In this paper, a machine learning-based approach is proposed to correct the model mismatch to achieve quantitative CT imaging. Specifically, a one-dimensional network model is proposed to learn a non-linear transform from a training dataset to map a polychromatic CT image to its monochromatic sinogram at a pre-specified energy level, realizing virtual monochromatic (VM) imaging effectively and efficiently. Our results show that the proposed method recovers high-quality monochromatic projections with an average relative error of less than 2%. The resultant x-ray VM imaging can be applied for beam-hardening correction, material differentiation and tissue characterization, and proton therapy treatment planning.

Intraindividual Consistency of Iodine Concentration in Dual-Energy Computed Tomography of the Chest and Abdomen

OBJECTIVES

Dual-energy computed tomography (DECT)-derived quantification of iodine concentration (IC) is increasingly used in oncologic imaging to characterize lesions and evaluate treatment response. However, only limited data are available on intraindividual consistency of IC and its variation. This study investigates the longitudinal reproducibility of IC in organs, vessels, and lymph nodes in a large cohort of healthy patients who underwent repetitive DECT imaging.

MATERIALS AND METHODS

A total of 159 patients, who underwent a total of 469 repetitive (range, 2-4), clinically indicated portal-venous phase DECT examinations of the chest and abdomen, were retrospectively included. At time of imaging, macroscopic tumor burden was excluded by follow-up imaging (≥3 months). Iodine concentration was measured region of interest-based (N = 43) in parenchymatous organs, vessels, lymph nodes, and connective tissue. Normalization of IC to the aorta and to the trigger delay as obtained from bolus tracking was performed. For statistical analysis, intraclass correlation coefficient and modified variation coefficient (MVC) were used to assess intraindividual agreement of IC and its variation between different time points, respectively. Furthermore, t tests and analysis of variance with Tukey-Kramer post hoc test were used.

RESULTS

The mean intraclass correlation coefficient over all regions of interest was good to excellent (0.642-0.936), irrespective of application of normalization or the normalization technique. Overall, MVC ranged from 1.8% to 25.4%, with significantly lower MVC in data normalized to the aorta (5.8% [1.8%-15.8%]) in comparison with the MVC of not normalized data and data normalized to the trigger delay (P < 0.01 and P = 0.04, respectively).

CONCLUSIONS

Our study confirms intraindividual, longitudinal variation of DECT-derived IC, which varies among vessels, lymph nodes, organs, and connective tissue, following different perfusion characteristics; normalizing to the aorta seems to improve reproducibility when using a constant contrast media injection protocol.

Total-Body PET Imaging of Musculoskeletal Disorders

Imaging of musculoskeletal disorders, including arthritis, infection, osteoporosis, sarcopenia, and malignancies, is often limited when using conventional modalities such as radiography, computed tomography (CT), and MR imaging. As a result of recent advances in Positron Emission Tomography (PET) instrumentation, total-body PET/CT offers a longer axial field-of-view, higher geometric sensitivity, and higher spatial resolution compared with standard PET systems. This article discusses the potential applications of total-body PET/CT imaging in the assessment of musculoskeletal disorders.

Computed tomography in rheumatology - From DECT to high-resolution peripheral quantitative CT

In this chapter, we discuss current updates and applications of Dual Energy Computed Tomography (DECT), iodine-DECT mapping, and high-resolution peripheral quantitative CT (HR-pQCT) in rheumatology. DECT provides a noninvasive diagnosis of gout and can help to differentiate gout from CPPD. Accuracy of DECT varies in various stages of gout. DECT needs specialized hardware, software, and skilled post-processing and interpretation. Sensitivity reduces significantly with deeper tissues such as hip and shoulder. Iodine map enables to delineate inflammatory lesions such as capsulitis and tenosynovitis by improving iodine contrast. Iodine quantification with an iodine map is a promising objective method to evaluate therapeutic effect of inflammatory arthritis. HR-pQCT allows for highly sensitive and specific measures of bone erosions and osteophytes in inflammatory joint diseases, documenting change over time, e.g. in cohorts undergoing immunosuppressive treatments. However, assessing the images requires trained readers, and (semi)-automated scripts to detect bone damage are still undergoing validation and further development.

Virtual Monoenergetic CT Imaging via Deep Learning

Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.

Differentiating Rheumatoid and Psoriatic Arthritis of the Hand: Multimodality Imaging Characteristics

Accurate diagnosis and therapeutic intervention at an early stage is paramount for the management of rheumatoid arthritis (RA) and psoriatic arthritis (PsA), which are the two major types of inflammatory arthritis that involve the hand joints. As more disease-specific medications are developed, medication selection according to the correct diagnosis becomes more important. A delay in diagnosis and inappropriate medication selection may result in poor functional prognosis. However, clinical differentiation between RA and PsA can be challenging and may become largely dependent on imaging interpretation results. Although there is substantial overlap in the imaging findings of RA and PsA, there are differences in the affected primary target sites, reflected by the various patterns of joint involvement, and different microanatomic localization of abnormalities within a single joint in each disease. Therefore, appropriate use of various imaging modalities and accurate image interpretation add significant value to the diagnosis and treatment process. The synovio-entheseal complex is an important concept for understanding the imaging features of PsA. The authors review the different features of RA and PsA of the hands seen with various imaging modalities, including radiography, US, MRI, and dual-energy CT, with updates on the contemporary role of imaging in diagnosis and treatment. The radiologist should have sufficient knowledge to interpret imaging findings and understand the strengths and weaknesses of each modality to recommend the appropriate imaging method and differentiate both diseases accurately. ^©RSNA, 2020.