Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions

Demehri, Shadpour; Baffour, Francis I.; Klein, Joshua G.; Ghotbi, Elena; Ibad, Hamza Ahmed; Moradi, Kamyar; Taguchi, Katsuyuki; Fritz, Jan; Carrino, John A.; Guermazi, Ali; Fishman, Elliot K.; Zbijewski, Wojciech B.

doi:10.1148/radiol.230344

Cited by 28 publications

(4 citation statements)

References 87 publications

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“…This ultimately contributes to the higher SNR and CNR that we observed. This fact may support a clinical anticipation of the use of PCCT in bone imaging, which is expected to more clearly depict bone abnormalities, such as fractures, lytic lesions, and mineralized tumor matrices [20,21].…”

Section: Discussionmentioning

confidence: 75%

Comparison of Bone Evaluation and Metal Artifact between Photon-Counting CT and Five Energy-Integrating-Detector CT under Standardized Conditions Using Cadaveric Forearms

Fukuda,

Yonenaga,

Akao

et al. 2024

Diagnostics

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Background: To compare the potential of various bone evaluations by considering photon-counting CT (PCCT) and multiple energy-integrating-detector CT (EIDCT), including three dual-energy CT (DECT) scanners with standardized various parameters in both standard resolution (STD) and ultra-high-resolution (UHR) modes. Methods: Four cadaveric forearms were scanned using PCCT and five EIDCTs, by applying STD and UHR modes. Visibility of bone architecture, image quality, and a non-displaced fracture were subjectively scored against a reference EIDCT image by using a five-point scale. Image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were also compared. To assess metal artifacts, a forearm with radial plate fixation was scanned by with and without Tin filter (Sn+ and Sn−), and virtual monoenergetic image (VMI) at 120 keV was created. Regarding Sn+ and VMI, images were only obtained from the technically available scanners. Subjective scores and the areas of streak artifacts were compared. Results: PCCT demonstrated significantly lower noise (p < 0.001) and higher bone SNR and CNR (p < 0.001) than all EIDCTs in both resolution modes. However, there was no significant difference between PCCT and EIDCTs in almost all subjective scores, regardless of scan modes, except for image quality where a significant difference was observed, compared to several EIDCTs. Metal artifact analysis revealed PCCT had larger artifact in Sn− and Sn+ (p < 0.001), but fewer in VMIs than three DECTs (p < 0.001 or 0.001). Conclusions: Under standardized conditions, while PCCT had almost no subjective superiority in visualizing bone structures and fracture line when compared to EIDCTs, it outperformed in quantitative analysis related to image quality, especially in lower noise and higher tissue contrast. When using PCCT to assess cases with metal implants, it may be recommended to use VMIs to minimize the possible tendency for artifact to be pronounced.

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Section: Discussionmentioning

confidence: 75%

Comparison of Bone Evaluation and Metal Artifact between Photon-Counting CT and Five Energy-Integrating-Detector CT under Standardized Conditions Using Cadaveric Forearms

Fukuda,

Yonenaga,

Akao

et al. 2024

Diagnostics

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“…CT Scans, utilizing X-rays, exhibit a sensitivity range of 60%–90% and a specificity of ~90%. Still, the method is constrained by radiation risks and less detailed soft tissue imaging ( 20 ). PET Scans employing ionizing radiation show varying sensitivity (70%–90%) and reasonable specificity (80%–90%), yet are subject to sensitivity limitations due to tumor characteristics and tracer use ( 21 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent tumor tissue classification for Hybrid Health Care Units

Butt,

Li,

et al. 2024

Front. Med.

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IntroductionIn the evolving healthcare landscape, we aim to integrate hyperspectral imaging into Hybrid Health Care Units to advance the diagnosis of medical diseases through the effective fusion of cutting-edge technology. The scarcity of medical hyperspectral data limits the use of hyperspectral imaging in disease classification.MethodsOur study innovatively integrates hyperspectral imaging to characterize tumor tissues across diverse body locations, employing the Sharpened Cosine Similarity framework for tumor classification and subsequent healthcare recommendation. The efficiency of the proposed model is evaluated using Cohen's kappa, overall accuracy, and f1-score metrics.ResultsThe proposed model demonstrates remarkable efficiency, with kappa of 91.76%, an overall accuracy of 95.60%, and an f1-score of 96%. These metrics indicate superior performance of our proposed model over existing state-of-the-art methods, even in limited training data.ConclusionThis study marks a milestone in hybrid healthcare informatics, improving personalized care and advancing disease classification and recommendations.

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“…In the field of musculoskeletal imaging, particularly in knee imaging, magnetic resonance (MR) imaging and computed tomography (CT) serve as two indispensable modalities, each offering unique insights [1][2][3][4][5][6]; while MR stands out for its unrivaled soft tissue contrast, CT excels in providing precise density-based information due to its utilization of Hounsfield units (HU). MR's unique ability to non-invasively capture high-resolution, multi-planar images with exceptional soft tissue contrast provides a detailed representation of knee joint structures, making it indispensable for diagnosing a wide array of pathologies and conditions, providing crucial insights into articular cartilage, ligaments, and menisci.…”

Section: Introductionmentioning

confidence: 99%

CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies

Vallejo-Cendrero,

Molina-Maza,

Rodriguez-Gonzalez

et al. 2024

Applied Sciences

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In the field of knee imaging, the incorporation of MR-based pseudo-CT synthesis holds the potential to mitigate the need for separate CT scans, simplifying workflows, enhancing patient comfort, and reducing radiation exposure. In this work, we present a novel DL framework, grounded in the development of the Cycle-Consistent Generative Adversarial Network (CycleGAN) method, tailored specifically for the synthesis of pseudo-CT images in knee imaging to surmount the limitations of current methods. Upon visually examining the outcomes, it is evident that the synthesized pseudo-CTs show an excellent quality and high robustness. Despite the limited dataset employed, the method is able to capture the particularities of the bone contours in the resulting image. The experimental Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Zero-Normalized Cross Correlation (ZNCC), Mutual Information (MI), Relative Change (RC), and absolute Relative Change (|RC|) report values of 30.4638 ± 7.4770, 28.1168 ± 1.5245, 0.9230 ± 0.0217, 0.9807 ± 0.0071, 0.8548 ± 0.1019, 0.0055 ± 0.0265, and 0.0302 ± 0.0218 (median ± median absolute deviation), respectively. The voxel-by-voxel correlation plot shows an excellent correlation between pseudo-CT and ground-truth CT Hounsfield units (m = 0.9785; adjusted R2 = 0.9988; ρ = 0.9849; p < 0.001). The Bland–Altman plot shows that the average of the differences is low ((HUCT−HUpseudo−CT = 0.7199 ± 35.2490; 95% confidence interval [−68.3681, 69.8079]). This study represents the first reported effort in the field of MR-based knee pseudo-CT synthesis, shedding light to significantly advance the field of knee imaging.

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Musculoskeletal CT Imaging: State-of-the-Art Advancements and Future Directions

Cited by 28 publications

References 87 publications

Comparison of Bone Evaluation and Metal Artifact between Photon-Counting CT and Five Energy-Integrating-Detector CT under Standardized Conditions Using Cadaveric Forearms

Comparison of Bone Evaluation and Metal Artifact between Photon-Counting CT and Five Energy-Integrating-Detector CT under Standardized Conditions Using Cadaveric Forearms

Intelligent tumor tissue classification for Hybrid Health Care Units

CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies

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