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DOI: 10.1055/a-2653-9256
Pitfalls in Bone Marrow Edema Interpretation on Dual-Energy CT: Challenges and Solutions
Fallstricke bei der Interpretation des Knochenmarködems in der Dual-Energy-Computertomografie: Herausforderungen und Lösungsansätze
- Abstract
- Zusammenfassung
- Introduction
- Basic Physiology of Bone Marrow
- Pathophysiology of Bone Marrow Edema
- Physics of DECT in Bone Marrow Edema Detection
- Post-Processing of DECT Raw Data
- Clinical Applications of DECT for BME Detection
- Accuracy of DECT in Traumatic and Non-Traumatic Conditions Compared to MRI
- Pitfalls in Interpreting BME on DECT
- Future Directions (Photon Counting CT)
- Conclusion
- References
Abstract
Background
Bone marrow edema (BME) is a significant imaging finding in musculoskeletal and emergency radiology, often associated with trauma or nontraumatic etiologies such as inflammation, infection, or neoplasms. Magnetic resonance imaging (MRI) remains the gold standard for BME evaluation. However, dual-energy CT (DECT) has emerged as a valuable alternative due to its faster acquisition times, lower costs, and more rapid access in emergency settings (when compared with MRI), facilitating timely decision-making when MRI is impractical or contraindicated. Despite its benefits, accurate interpretation of BME on DECT requires careful understanding of its limitations and potential pitfalls. This article addresses the technical and clinical challenges in DECT-based BME assessment and proposes strategies to enhance diagnostic accuracy.
Method
A review of the literature was performed by searching the PubMed and ScienceDirect databases, using the keywords (“DECT” or “Dual-Energy”) and (“BME” or “bone marrow edema”) and (“musculoskeletal” or “bone” or “skeleton”) for the title and abstract query. The inclusion criteria were scientific papers presented in the English language. Exclusion criteria included articles which had no relevant focus on BME and case reports. Of the 168 articles initially identified, 75 were deemed relevant and were reviewed in detail. Insight from this literature search and the authorsʼ clinical experience forms the basis of this review, highlighting key pitfalls and strategies for accurate BME interpretation.
Results and Conclusion
DECT provides significant advantages for detecting BME, such as material-specific color overlays and high anatomical correlation. However, key pitfalls include the misinterpretation of artifacts, difficulties in cases of severe displacement or sclerosis, and challenges posed by imaging artifacts in large-sized patients or those with metallic implants. Radiologists can improve diagnostic accuracy by understanding the limitations and pitfalls of DECT, and by adopting the solutions outlined in the article to optimize its use.
Key Points
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DECT effectively identifies BME in both traumatic and non-traumatic conditions, with sensitivity and specificity comparable to magnetic resonance imaging (MRI).
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Key interpretation pitfalls include artifacts from photon starvation, metallic implants, severe displacement, and motion, as well as limitations in algorithm processing.
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Misdiagnoses can arise due to mimics of BME, such as sclerosis, red marrow, or pathological fractures, necessitating clinical and imaging correlation.
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Parameter optimization (e.g., spectral FOV, kernel selection, image calibration) enhances diagnostic accuracy and reduces errors.
Citation Format
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Yap JA, Ong YX, Weber M. Pitfalls in Bone Marrow Edema Interpretation on Dual-Energy CT: Challenges and Solutions. Rofo 2025; DOI 10.1055/a-2653-9256
Zusammenfassung
Hintergrund
Das Knochenmarködem (KMÖ) ist ein bedeutender bildgebender Befund in der muskuloskelettalen Radiologie und Notfallradiologie, häufig assoziiert mit traumatischen oder nicht-traumatischen Ursachen wie Entzündungen, Infektionen oder Neoplasien. Die Magnetresonanztomografie (MRT) bleibt der Goldstandard zur Beurteilung von KMÖ; jedoch hat sich die Dual-Energy-CT (DECT) aufgrund ihrer, verglichen mit der MRT kürzeren Messzeiten, geringerer Kosten und größeren Verfügbarkeit in Notfallaufnahmen als wertvolle Alternative etabliert. Trotz ihrer Vorteile erfordert die genaue Interpretation eines KMÖ in der DECT ein sorgfältiges Verständnis der Einschränkungen und potenzieller Fallstricke. Dieser Artikel befasst sich mit den technischen und klinischen Herausforderungen bei der Bewertung eines KMÖ mittels DECT und schlägt Strategien vor, um die diagnostische Genauigkeit zu verbessern.
Methode
Eine Literaturrecherche wurde in den Datenbanken PubMed und ScienceDirect durchgeführt, wobei die englischen Schlüsselwörter („DECT“ oder „Dual-Energy“) und („BME“, oder „bone marrow edema“) sowie („musculoskeletal“ oder „bone“ oder „skeleton“) für die Titel- und Abstract-Suche verwendet wurden. Eingeschlossen wurden wissenschaftliche Arbeiten in englischer Sprache. Ausschlusskriterien waren Artikel, die keinen relevanten Fokus auf das KMÖ legten, ebenso wurden Fallberichte ausgeschlossen. Von den zunächst identifizierten 168 Artikeln wurden 75 als relevant erachtet und detailliert analysiert. Die Erkenntnisse aus dieser Literaturrecherche sowie die klinische Erfahrung der Autoren bilden die Grundlage dieser Übersichtsarbeit, die zentrale Fallstricke und Strategien für eine präzise KMÖ-Interpretation hervorhebt.
Ergebnisse und Fazit
DECT bietet wesentliche Vorteile bei der Detektion eines KMÖ, darunter materialspezifische Farbüberlagerungen und eine hohe anatomische Korrelation. Dennoch existieren relevante Fallstricke, wie die Fehlinterpretation von Artefakten, Schwierigkeiten bei ausgeprägter Dislokation oder Sklerose sowie Herausforderungen durch Bildartefakte bei adipösen Patienten oder metallischen Implantaten. Radiologen können die diagnostische Genauigkeit verbessern, indem sie die Einschränkungen und Fallstricke der DECT verstehen und die in diesem Artikel dargestellten Lösungen und Protokolle zur Optimierung der Anwendung übernehmen.
Kernaussagen
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DECT ermöglicht die zuverlässige Identifikation eines KMÖ sowohl bei traumatischen als auch nicht-traumatischen Erkrankungen mit einer Sensitivität und Spezifität, die mit der Magnetresonanztomografie (MRT) vergleichbar ist.
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Wichtige Interpretationsfallen umfassen Artefakte durch Photonenmangel, metallische Implantate, ausgeprägte Dislokationen, Bewegungsartefakte sowie algorithmische Limitationen.
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Fehlinterpretationen können durch KMÖ-Mimiker wie Sklerose, rotes Knochenmark oder pathologische Frakturen entstehen, wodurch eine enge klinische und bildgebende Korrelation erforderlich ist.
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Die Optimierung technischer Parameter (z.B. spektrales FOV, Wahl des Kernel, Bildkalibrierung) verbessert die diagnostische Genauigkeit und reduziert Fehler.
Introduction
Bone marrow edema (BME) reflects a spectrum of conditions causing abnormal marrow signal changes. While magnetic resonance imaging (MRI) remains the gold standard for BME evaluation, dual-energy CT (DECT) has emerged as a valuable alternative due to its rapid acquisition, lower cost, and greater availability in emergency settings, facilitating timely decision-making when MRI is impractical or contraindicated. Virtual non-calcium (VNCa) images and color maps generated through material decomposition techniques enhance the visibility of BME [1]. Despite its advantages, accurate interpretation of BME on DECT requires a thorough understanding of its technical nuances and potential pitfalls.
While previous studies have assessed DECT’s sensitivity and specificity for BME detection, gaps remain with regard to defining its limitations, optimizing clinical applications, and refining strategies to reduce misinterpretation. This article explores the principles of DECT, its clinical utility, challenges, and strategies to enhance diagnostic accuracy.
Basic Physiology of Bone Marrow
Understanding the physiology and pathophysiology of bone marrow is essential for interpreting BME.
Bones consist of cortical and trabecular (cancellous) components, with trabeculae undergoing continuous remodeling in response to stress. These trabeculae house bone marrow, composed of red (hematopoietic) and yellow (fatty) marrow [2].
The distribution of marrow changes with age, with red marrow gradually converting to yellow marrow in specific regions, culminating in the adult pattern by 24 to 25 years [3] [4]. While most adult marrow is yellow, red marrow persists in areas like the proximal metaphysis and intertrochanteric regions [4]. Factors such as anemia, smoking, and obesity can trigger red marrow reconversion in regions typically dominated by yellow marrow [5]. Red and yellow marrow differ in composition: red marrow has higher water and protein content and shows intermediate signal intensity on T1-weighted and T2-weighted imaging. Yellow marrow, being fat-rich, appears hyperintense on T1-weighted and T2-weighted imaging [4].
Pathophysiology of Bone Marrow Edema
The underlying mechanism is that of trabecular microfractures resulting in disruption of capillaries and pooling of fluid and blood products due to capillary leakage [3] [6]. There may also be increased intramedullary and intravascular pressure due to venous stasis from vascular injury or traumatic joint effusion, causing increased interstitial fluid in the intertrabecular and sinusoidal spaces of the bone [3]. These mechanisms lead to BME.
Physics of DECT in Bone Marrow Edema Detection
On conventional CT, tissue contrast is based on how X-ray beams are absorbed, which is influenced by the atomic number and K-edge of substances [7]. Soft tissues (lower atomic number elements) exhibit minimal X-ray absorption, while calcium in bone (higher atomic number) absorbs X-rays more effectively.
DECT uses two X-ray energy spectra, one low and one high energy, to distinguish materials based on their unique X-ray absorption characteristics [8]. Low-energy spectrum X-rays (typically around 80–100 kilovolt peak (kVp)) are more readily absorbed by bone and soft tissues. High-energy spectrum X-rays (typically around 140–150 kVp) penetrate deeper with less absorption. Commonly, spectra at 80 and 140 kVp are used.
Various manufacturers have different ways of achieving this on modern CT scanners, including a Dual Source method (Siemens Healthineers, Erlangen, Germany) and Rapid Switching method (GE Healthcare, Chicago, IL, USA). For this article, images acquired using CT scanners from the two aforementioned manufacturers are shown.
Post-Processing of DECT Raw Data
Proprietary software algorithms are used to post-process the raw CT data, using material decomposition techniques to eliminate high-attenuation trabeculae of bone, thereby enabling clearer visualization of underlying marrow attenuation. Depending on the manufacturer, these images can be presented to radiologists in various formats, depending on the manufacturer’s advanced visualization software and institutional preferences.
Siemens Healthineers (Erlangen, Germany) employs syngo.via software, which uses a three-material decomposition approach (red marrow, yellow marrow, and calcium). The resulting data can be presented in a color overlay image (default blue-green color map) generated based on the calculated attenuation of underlying pure marrow and can either be superimposed with grayscale CT images or shown as a 3D image. This helps the radiologist to visualize BME and its anatomical correlation [9].
GE Healthcare (Waukesha, WI, USA) utilizes Gemstone Spectral Imaging (GSI) technology in its AW software, which applies a two-material decomposition algorithm with a water–hydroxyapatite (HAP) material pair. The algorithm assumes the entire volume is made up of either material and generates images showing water density in each voxel with subtracted HAP information [10]. The measured pixel intensity value in the material density images corresponds to the material density and is quantitatively expressed as mg/cm³ [11]. By default, these water-HAP images are displayed in grayscale, though a color map can be applied based on institutional preferences. Additional post-processing, such as segmentation and fusion with grayscale CT images, can generate images similar to those produced by syngo.via, thereby facilitating easier interpretation.
Clinical Applications of DECT for BME Detection
Trauma
DECT is useful for detecting occult fractures, determining fracture chronicity, and mapping the extent of bone marrow injury. It identifies BME in cases where conventional CT may appear normal, aiding early diagnosis and management [12] [13].
Similar to MRI images, fracture-associated BME typically appears focal and linearly centered on or adjacent to a fracture. The fracture line may be easily seen on BME if the fracture is mildly displaced.
Non-Traumatic Conditions
It is important to note that BME signal also occurs due to various other non-traumatic etiologies such as stress changes, infection, inflammatory conditions [14], and malignancy. These require correlation with clinical history and other imaging modalities, such as MRI, for precise interpretation.
Accuracy of DECT in Traumatic and Non-Traumatic Conditions Compared to MRI
DECT is a very viable alternative to MRI, and DECT has shown comparable sensitivity and specificity to MRI, both for traumatic and non-traumatic conditions.
In a meta-analysis by Wilson et al. [15], they describe a pooled sensitivity of 86% [95% CI 82–89%], specificity of 93% [95% CI 90–95%], and AUC values of 0.95 with regard to detecting BME in the appendicular skeleton compared with MRI and/or clinical outcome. In these 20 studies in the meta-analysis, 17 studies evaluated only post-traumatic patients and three studies evaluated non-traumatic patient populations (including patients with rheumatoid arthritis).
In the axial skeleton, a separate meta-analysis by Yang et al. [16] demonstrated pooled high specificity (98%, 95% CI: 0.97–0.99) but moderate sensitivity (82%, 95% CI: 0.76–0.86 ) for detecting BME in acute vertebral compression fractures.
Similarly, Chen et al. [17] analyzed the diagnostic performance of DECT in non-traumatic BME, revealing excellent sensitivity (88.4%, 95% CI 82.4%–92.5%), specificity (96.1%, 95% CI 94.4%–97.3%). While DECT demonstrated high specificity, its moderate sensitivity highlights the need for MRI in cases where clinical suspicion persists despite negative DECT findings.
Summary of Meta-Analyses and Clinical Relevance
The meta-analyses collectively demonstrate the statistical significance of DECT in detecting bone marrow edema (BME), with high specificity (≥93%), making it a reliable tool for ruling out pathology. The narrow confidence intervals (CIs) in these studies indicate strong diagnostic performance and minimal variability across different patient populations. However, moderate sensitivity (82–88%) suggests that DECT may not detect all cases of BME, underscoring the continued need for MRI in cases where DECT may miss subtle BME.
From a clinical perspective, DECT provides a valuable alternative to MRI, particularly in acute trauma settings, where rapid imaging is critical, and in scenarios where MRI is inaccessible or contraindicated. While MRI remains the gold standard due to its superior sensitivity, DECT’s rapid acquisition, widespread availability, and excellent specificity make it a practical and effective tool in both emergency and outpatient settings. These findings support the integration of DECT as a complementary modality, helping bridge diagnostic gaps when MRI is not an option.
Pitfalls in Interpreting BME on DECT
Accurate interpretation of BME on DECT requires an understanding of common pitfalls and their solutions. While DECT has high specificity for detecting BME, misinterpretations can occur due to various medical, technical, and artifact-related factors. Radiologists should take specific measures to optimize image quality, adjust for patient variability, and recognize limitations to avoid diagnostic errors. [Table 1] summarizes key pitfalls and corresponding solutions to enhance diagnostic accuracy.
1. Medical Conditions
Increased Bone Attenuation
Certain conditions with increased bone attenuation can mimic or obscure BME. This is to be expected and is due to the software algorithm subtracting the calculated calcium (trabeculae), showing the attenuation of the material within the marrow space. Examples include red marrow ([Fig. 1]) and faintly sclerotic bone lesions such as metastases or avascular necrosis [3] ([Fig. 2]).
In patients who have dense sclerosis exceeding a certain threshold (approaching HU of cortical bone), software algorithms will be unable to accurately process the BME within the region of sclerosis ([Fig. 3]). In some instances (depending on the HU threshold), the signal will be ignored and may not show a signal on the VNCa maps [18].
Solution: Always correlate DECT findings with clinical history and MRI in cases where suspicion remains. Recognize that highly sclerotic regions may obscure BME, requiring alternative imaging.
Pathological Fractures
In patients with underlying bone lesions exhibiting BME (such as the conditions mentioned earlier), the BME signal from a pathological fracture may be masked, as both the lesion and the fracture can demonstrate overlapping BME. This masking effect is less likely if the fracture extends significantly beyond the lesion's margins ([Fig. 4]). Generally, MRI remains the preferred modality for accurately evaluating for pathological fractures.
Solution: Evaluate fracture margins carefully. If the BME pattern appears atypical or does not correlate with the clinical presentation, an MRI should be performed to rule out pathological fractures.
Severely Displaced Fractures
In cases of severe displacement of fractures ([Fig. 5] and [Fig. 6]), DECT may fail to detect BME due to algorithm limitations [3]. However, the primary utility of BME maps lies in identifying occult or undisplaced fractures.
Solution: Reserve DECT primarily for occult or subtle fractures. In cases of severe displacement, standard single-energy CT would suffice.
2. Imaging Artifacts
Photon Starvation and Large-Sized Patients
For accurate dual-energy image postprocessing, both low- and high-energy X-ray spectra must penetrate the body sufficiently. This can be particularly challenging when imaging the hips or spine in large-sized patients ([Fig. 7]), where low-energy X-rays are easily absorbed by the increased body tissues, resulting in photon starvation artifacts [19].
Solution: Increase the energy of the low-energy spectrum to 100kVp, with a tin filter on the high energy tube (only done on scanners with a dual-energy source) [3].
If unavailable, consider a clinical workflow that applies a weight or body circumference limit to the usage of DECT in BME. A study in 2016 by Patel et al. on DECT abdominal protocols showed that many institutes employed a weight cut-off of approximately 118 to 127 kg and/or a lateral dimension cut-off of 38 to 46 cm [19].
External or Metallic Artifacts
External factors such as casts ([Fig. 6]), splints, or metallic implants ([Fig. 8]) can attenuate the lower energy X-ray spectra, leading to false-negative results.
Metallic artifacts, including beam hardening and photon starvation, significantly reduce DECT's sensitivity for detecting BME, particularly near prosthetic devices [2]. These artifacts must be accounted for to avoid misinterpretation, limiting the evaluation of occult periprosthetic fractures. While review articles suggest potential use in detecting BME in undisplaced periprosthetic fractures [3], no dedicated studies currently address this, and in practice, metallic streaking often compromises accuracy.
Solution: Where possible, remove external materials (e.g., casts, splints) before scanning. For metallic implants, assess axial planes that do not directly intersect the metal implant, such as cranial or caudal slices, or areas where the implant is thinnest.
Motion Artifacts
In patients who cannot keep still or hold their breath during the duration of the scan, there may be motion artifacts on the CT image ([Fig. 9]). This can contribute to an erroneous signal on BME, affecting the interpretability of the study.
Solution: Reiterate the importance of breath-holding to patients before scanning. If motion is unavoidable, consider using single-energy CT instead with more rapid acquisition techniques.
3. Technical and System-Related Factors
Spectral Field of View Limitations (for dual-source scanners)
For dual-source scanners, the effectiveness of DECT is confined to the spectral field of view (FOV), typically represented by a dotted circle within axial CT images. This limitation arises because dual-source scanners employ two X-ray tubes with distinct energy levels, with detectors optimized for specific anatomical regions within the FOV. Areas outside this range may fail to show appreciable BME signals ([Fig. 10]), potentially leading to missed pathology.
Solution: Ensure that the area of interest is positioned near the scanner’s center to fall within the spectral FOV.
Kernel Selection
The choice of reconstruction kernel plays a significant role in BME map quality. Manufacturers generally advise avoiding sharp convolutional kernels, such as those used for bone or lung imaging, for BME post-processing. These kernels are prone to trade-offs between sharpness and increased image noise [20], which can degrade BME maps with additional artifacts and scattered “speckles” of signal ([Fig. 4]).
Solution: Use proper reconstruction kernels optimized for BME detection to ensure optimal results and minimize interpretive errors.
DECT Image Display
Proper calibration of BME maps, including the adjustment of ranges and thresholds, is essential for accurate assessment. Radiologists rely on these parameters to effectively identify pathologies using either grayscale or color maps. Inadequate calibration can result in false negatives or positives, significantly affecting diagnostic accuracy. While default settings are generally sufficient for most cases, fine-tuning may be necessary in complex scenarios – such as red marrow reconversion – to detect subtle or focal abnormalities and ensure precise interpretation.
For instance, in the case of the syngo.via software (Siemens Healthineers, Erlangen, Germany), BME maps offer customization options, including resolution, maximum HU, and threshold HU ([Fig. 11]). Increasing the resolution provides smoother signal representation in the trabeculae, while lowering the threshold HU allows the inclusion of a signal that may have been previously “masked out” or excluded ([Fig. 11]).
Similarly, in GE AW software (GE Healthcare, Chicago, IL, USA), BME signal adjustments can be made using window length (L) and window width (W). The window length represents the median concentration of water within the specified voxels, while the window width defines the range between the upper and lower limits of this concentration ([Fig. 11]). These customizable features across different platforms highlight the importance of tailored calibration to enhance diagnostic precision in varied clinical scenarios.
Solution: Calibrate DECT software settings and adjust ranges to accommodate different patient conditions (different patient sizes, bone marrow content, etc.). Always cross-check BME findings with the original CT dataset to ensure correlation with the site of abnormality.
Future Directions (Photon Counting CT)
Advances in detector technology are poised to further enhance DECT performance. Traditional energy-integrating detectors (EIDs), which generate a signal proportional to the sum of all energies of all detected X-ray photons [21], remain the backbone of current CT systems.
In contrast, emerging photon-counting detectors (PCDs) count individual photons and sort them into discrete energy bins, offering improved spectral resolution, reduced noise, and more accurate material differentiation, without any field-of-view limitation [21]. These advantages of PCDs have the potential to significantly improve detection sensitivity for bone marrow edema, particularly in scenarios where DECT faces challenges, like beam-hardening artifacts and other limitations as previously discussed.
Conclusion
Dual-energy CT (DECT) is a powerful imaging modality for detecting bone marrow edema (BME), leveraging its ability to generate material-specific color overlays in 3D for superior anatomical correlation and diagnostic clarity. These features position DECT not only as a complement to MRI but also as a potential first-line modality in advanced cross-sectional imaging, particularly in scenarios where MRI may be contraindicated, unavailable, or less practical.
Despite its advantages, the diagnostic accuracy of DECT hinges on a thorough understanding of its limitations. These include artifacts, algorithmic constraints, and mimics of BME, which may lead to diagnostic challenges. Radiologists must carefully consider factors such as spectral FOV, kernel selection, and image calibration to optimize BME detection. Additionally, challenges like photon starvation in larger patients, metallic artifacts, and motion-related errors necessitate systematic mitigation strategies to ensure diagnostic precision.
Photon-counting CT (PCCT), an emerging technology, holds promise for improving image resolution, noise reduction, and contrast, potentially enhancing the detection and interpretation of BME.
Declaration Regarding Generative AI and AI-Assisted Technologies in the Writing Process
The first author utilized ChatGPT during manuscript preparation, primarily for grammatical checks. Following its use, all authors carefully reviewed and revised the content to ensure accuracy and take full responsibility for the publication. All opinions and ideas expressed are solely those of the human authors.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgement
We extend our gratitude to the European School of Radiology (ESOR) and the European Society of Musculoskeletal Radiology (ESSR) for providing the fellowship opportunity that fostered the collaboration and academic environment enabling the creation of this review article.
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References
- 1 D’Angelo T, Albrecht MH, Caudo D. et al. Virtual non-calcium dual-energy CT: clinical applications. Eur Radiol Exp 2021; 5 (01) 38
- 2 Shah LM, Hanrahan CJ. MRI of Spinal Bone Marrow: Part 1, Techniques and Normal Age-Related Appearances. Am J Roentgenol 2011; 197 (06) 1298-1308
- 3 Gosangi B, Mandell JC, Weaver MJ. et al. Bone Marrow Edema at Dual-Energy CT: A Game Changer in the Emergency Department. RadioGraphics 2020; 40 (03) 859-874
- 4 Vande Berg BC, Malghem J, Lecouvet FE. et al. Magnetic resonance imaging of the normal bone marrow. Skeletal Radiol 1998; 27 (09) 471-483
- 5 Małkiewicz A, Dziedzic M. Bone marrow reconversion – imaging of physiological changes in bone marrow. Pol J Radiol 2012; 77 (04) 45-50
- 6 Rangger C, Kathrein A, Freund MC. et al. Bone bruise of the knee Histology and cryosections in 5 cases. Acta Orthop Scand 1998; 69 (03) 291-294
- 7 Allisy-Roberts PJ. Farr’s Physics for Medical Imaging. 2nd ed. Saunders Elsevier; 2008
- 8 Wortman JR, Uyeda JW, Fulwadhva UP. et al. Dual-Energy CT for Abdominal and Pelvic Trauma. RadioGraphics 2018; 38 (02) 586-602
- 9 Pache G, Krauss B, Strohm P. et al. Dual-Energy CT Virtual Noncalcium Technique: Detecting Posttraumatic Bone Marrow Lesions – Feasibility Study. Radiology 2010; 256 (02) 617-624
- 10 Mendonca PRS, Lamb P, Sahani DV. A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images. IEEE Trans Med Imaging 2014; 33 (01) 99-116
- 11 Son W, Park C, Jeong HS. et al. Bone marrow edema in non-traumatic hip: high accuracy of dual-energy CT with water-hydroxyapatite decomposition imaging. Eur Radiol 2020; 30 (04) 2191-2198
- 12 Reddy T, McLaughlin PD, Mallinson PI. et al. Detection of occult, undisplaced hip fractures with a dual-energy CT algorithm targeted to detection of bone marrow edema. Emerg Radiol 2015; 22 (01) 25-29
- 13 Kellock TT, Nicolaou S, Kim SSY. et al. Detection of Bone Marrow Edema in Nondisplaced Hip Fractures: Utility of a Virtual Noncalcium Dual-Energy CT Application. Radiology 2017; 284 (03) 798-805
- 14 Hetland ML, Ejbjerg B, Hørslev-Petersen K. et al. MRI bone oedema is the strongest predictor of subsequent radiographic progression in early rheumatoid arthritis. Results from a 2-year randomised controlled trial (CIMESTRA). Ann Rheum Dis 2009; 68 (03) 384-390
- 15 Wilson MP, Lui K, Nobbee D. et al. Diagnostic accuracy of dual-energy CT for the detection of bone marrow edema in the appendicular skeleton: a systematic review and meta-analysis. Eur Radiol 2021; 31 (03) 1558-1568
- 16 Yang P, Wu G, Chang X. Diagnostic accuracy of dual-energy computed tomography in bone marrow edema with vertebral compression fractures: A meta-analysis. Eur J Radiol 2018; 99: 124-129
- 17 Chen Z, Chen Y, Zhang H. et al. Diagnostic accuracy of dual-energy computed tomography (DECT) to detect non-traumatic bone marrow edema: A systematic review and meta-analysis. Eur J Radiol 2022; 153: 110359
- 18 Foti G, Serra G, Iacono V. et al. Identification of Traumatic Bone Marrow Oedema: The Pearls and Pitfalls of Dual-Energy CT (DECT). Tomography 2021; 7 (03) 424-433
- 19 Patel BN, Alexander L, Allen B. et al. Dual-energy CT workflow: multi-institutional consensus on standardization of abdominopelvic MDCT protocols. Abdom Radiol 2017; 42 (03) 676-687
- 20 Weiss KL, Cornelius RS, Greeley AL. et al. Hybrid Convolution Kernel: Optimized CT of the Head, Neck, and Spine. Am J Roentgenol 2011; 196 (02) 403-406
- 21 Esquivel A, Ferrero A, Mileto A. et al. Photon-Counting Detector CT: Key Points Radiologists Should Know. Korean J Radiol 2022; 23 (09) 854
Correspondence
Publication History
Received: 04 March 2025
Accepted after revision: 06 July 2025
Article published online:
01 August 2025
© 2025. Thieme. All rights reserved.
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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References
- 1 D’Angelo T, Albrecht MH, Caudo D. et al. Virtual non-calcium dual-energy CT: clinical applications. Eur Radiol Exp 2021; 5 (01) 38
- 2 Shah LM, Hanrahan CJ. MRI of Spinal Bone Marrow: Part 1, Techniques and Normal Age-Related Appearances. Am J Roentgenol 2011; 197 (06) 1298-1308
- 3 Gosangi B, Mandell JC, Weaver MJ. et al. Bone Marrow Edema at Dual-Energy CT: A Game Changer in the Emergency Department. RadioGraphics 2020; 40 (03) 859-874
- 4 Vande Berg BC, Malghem J, Lecouvet FE. et al. Magnetic resonance imaging of the normal bone marrow. Skeletal Radiol 1998; 27 (09) 471-483
- 5 Małkiewicz A, Dziedzic M. Bone marrow reconversion – imaging of physiological changes in bone marrow. Pol J Radiol 2012; 77 (04) 45-50
- 6 Rangger C, Kathrein A, Freund MC. et al. Bone bruise of the knee Histology and cryosections in 5 cases. Acta Orthop Scand 1998; 69 (03) 291-294
- 7 Allisy-Roberts PJ. Farr’s Physics for Medical Imaging. 2nd ed. Saunders Elsevier; 2008
- 8 Wortman JR, Uyeda JW, Fulwadhva UP. et al. Dual-Energy CT for Abdominal and Pelvic Trauma. RadioGraphics 2018; 38 (02) 586-602
- 9 Pache G, Krauss B, Strohm P. et al. Dual-Energy CT Virtual Noncalcium Technique: Detecting Posttraumatic Bone Marrow Lesions – Feasibility Study. Radiology 2010; 256 (02) 617-624
- 10 Mendonca PRS, Lamb P, Sahani DV. A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images. IEEE Trans Med Imaging 2014; 33 (01) 99-116
- 11 Son W, Park C, Jeong HS. et al. Bone marrow edema in non-traumatic hip: high accuracy of dual-energy CT with water-hydroxyapatite decomposition imaging. Eur Radiol 2020; 30 (04) 2191-2198
- 12 Reddy T, McLaughlin PD, Mallinson PI. et al. Detection of occult, undisplaced hip fractures with a dual-energy CT algorithm targeted to detection of bone marrow edema. Emerg Radiol 2015; 22 (01) 25-29
- 13 Kellock TT, Nicolaou S, Kim SSY. et al. Detection of Bone Marrow Edema in Nondisplaced Hip Fractures: Utility of a Virtual Noncalcium Dual-Energy CT Application. Radiology 2017; 284 (03) 798-805
- 14 Hetland ML, Ejbjerg B, Hørslev-Petersen K. et al. MRI bone oedema is the strongest predictor of subsequent radiographic progression in early rheumatoid arthritis. Results from a 2-year randomised controlled trial (CIMESTRA). Ann Rheum Dis 2009; 68 (03) 384-390
- 15 Wilson MP, Lui K, Nobbee D. et al. Diagnostic accuracy of dual-energy CT for the detection of bone marrow edema in the appendicular skeleton: a systematic review and meta-analysis. Eur Radiol 2021; 31 (03) 1558-1568
- 16 Yang P, Wu G, Chang X. Diagnostic accuracy of dual-energy computed tomography in bone marrow edema with vertebral compression fractures: A meta-analysis. Eur J Radiol 2018; 99: 124-129
- 17 Chen Z, Chen Y, Zhang H. et al. Diagnostic accuracy of dual-energy computed tomography (DECT) to detect non-traumatic bone marrow edema: A systematic review and meta-analysis. Eur J Radiol 2022; 153: 110359
- 18 Foti G, Serra G, Iacono V. et al. Identification of Traumatic Bone Marrow Oedema: The Pearls and Pitfalls of Dual-Energy CT (DECT). Tomography 2021; 7 (03) 424-433
- 19 Patel BN, Alexander L, Allen B. et al. Dual-energy CT workflow: multi-institutional consensus on standardization of abdominopelvic MDCT protocols. Abdom Radiol 2017; 42 (03) 676-687
- 20 Weiss KL, Cornelius RS, Greeley AL. et al. Hybrid Convolution Kernel: Optimized CT of the Head, Neck, and Spine. Am J Roentgenol 2011; 196 (02) 403-406
- 21 Esquivel A, Ferrero A, Mileto A. et al. Photon-Counting Detector CT: Key Points Radiologists Should Know. Korean J Radiol 2022; 23 (09) 854