Multiparametric Evaluation of Head and Neck Squamous Cell Carcinoma Using a Single-Source Dual-Energy CT with Fast kVp Switching: State of the Art

Quantitative parameter analysis of pretreatment dual-energy computed tomography in nasopharyngeal carcinoma cervical lymph node characteristics and prediction of radiotherapy sensitivity

BACKGROUND

Treatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor-node-metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck-lymph node radiotherapy in patients with NPC.

METHODS

Overall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression.

RESULTS

DECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73-0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively.

CONCLUSION

Quantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.

Dual-Energy CT Iodine Uptake of Head and Neck: Definition of Reference Values in a Big Data Cohort

BACKGROUND

Despite a considerable amount of literature on dual-energy CT (DECT) iodine uptake of the head and neck, the physiologic iodine uptake of this region has not been defined yet. This study aims to establish reference values for the iodine uptake of healthy organs to facilitate clinical application.

METHODS

Consecutive venous DECT scans of the head and neck were reviewed, and unremarkable exams were included (n = 617). A total of 35 region of interest measurements were performed in 16 anatomical regions. Iodine uptake was compared among different organs/tissues and subgroup analysis was performed (male (n = 403) vs. female (n = 214); young (n = 207) vs. middle-aged (n = 206) vs. old (n = 204); and normal weight (n = 314) vs. overweight (n = 196) vs. obese (n = 107)).

RESULTS

Overall mean iodine uptake values ranged between 0.5 and 9.4 mg/mL. Women showed higher iodine concentrations in the cervical vessels and higher uptake for the parotid gland, masseter muscle, submandibular glands, sublingual glands, palatine tonsils, tongue body, thyroid gland, and the sternocleidomastoid muscle than men (p ≤ 0.04). With increasing age, intravascular iodine concentrations increased as well as iodine uptake for cerebellum and thyroid gland, while values for the tongue and palatine tonsils were lower compared to younger subjects (p ≤ 0.03). Iodine concentrations for parotid glands and sternocleidomastoid muscles decreased with a higher BMI (p ≤ 0.004), while normal-weighted patients showed higher iodine values inside the jugular veins, other cervical glands, and tonsils versus patients with a higher BMI (p ≤ 0.04).

CONCLUSION

physiologic iodine uptake values of cervical organs and tissues show gender-, age-, and BMI-related differences, which should be considered in the clinical routine of head and neck DECT.

The value of dual-layer spectral detector CT in preoperative T staging of laryngeal and hypopharyngeal squamous cell carcinoma

PURPOSE

To explore the optimal kiloelectron voltage (keV) of virtual monochromatic images (VMIs) of dual-layer spectral detector computed tomography (DLSCT) to display laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) and its diagnostic performance for preoperative T staging of LHSCC.

METHODS

A total of 67 LHSCC patients were included, and the contrast between the tumor and sternocleidomastoid muscle (SM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image noise of 40-100 keV VMIs and conventional polyenergetic images (CIs) were evaluated. The image quality of the CI and 40-100 keV VMI was evaluated by a five-point method. The VMI with the best image quality was screened out, and the accuracy of the optimal keV VMI and CI for T staging was assessed using clinical T staging as the reference standard.

RESULTS

The contrast between the tumor and SM, SNR, CNR and subjective image quality scores of LHSCC on 40-50 keV VMIs were higher than those on CIs (P < 0.05); the image noises of 40-100 keV VMIs were lower than those of CIs (P < 0.05). The 40 keV VMI had the highest SNR, CNR and subjective score of image quality. The accuracy rates of the 40 keV VMI and CI for T staging of LHSCC were 0.86 and 0.63 (P < 0.001), respectively.

CONCLUSION

The image quality of 40-50 keV VMI is higher than that of CI, and the diagnostic accuracy of 40 keV VMI is better than that of CI, which is most suitable for preoperative T staging of LHSCC.

Accuracy of dual-energy computed tomography for bone marrow edema in the sacroiliac joint: A systematic review and meta-analysis

BACKGROUND

This systematic literature review and meta-analysis aimed to assess the accuracy, sensitivity, and specificity of dual-energy computed tomography (DECT) of the sacroiliac joint. Bone marrow edema (BME) of the sacroiliac joint is an early manifestation of some diseases, such as ankylosing spondylitis, and is usually examined by nuclear magnetic resonance imaging (MRI); however, MRI can be intolerable for some patients; hence, numerous studies have analyzed DECT examinations.

METHODS

We searched PUBMED, CNKI, and EMBASE in 2023 for articles containing the following terms (DECT) or (DE-CT) or (dual-energy CT) or "dual-energy CT" or (dual-energy computed tomography) and ((sacroiliac joint) or (ankylosing spondylitis) or (sacroiliac arthritis) or (sacroiliitis)). An initial search identified 444 articles, of which 7 met the criteria. Data were extracted to calculate the sensitivity, specificity, and diagnostic odds for analysis using R software.

RESULTS

Out of 291 patients and 577 sacroiliac joints, 429 (74.35%) exhibited BME. All studies used magnetic resonance as the control group. The overall sensitivity and specificity of DECT were 79%, and 92%, respectively, with positive prediction rate of 92.55% and negative prediction rate of 83.73%.

CONCLUSION

DECT appears to be a promising diagnostic tool for detecting BME in the sacroiliac joint and can be used as an alternative examination method for patients in whom MRI is contraindicated.

Evaluation of optimal monoenergetic images acquired by dual-energy CT in the diagnosis of T staging of thoracic esophageal cancer

OBJECTIVES

The purpose of our study was to objectively and subjectively assess optimal monoenergetic image (MEI (+)) characteristics from dual-energy CT (DECT) and the diagnostic performance for the T staging in patients with thoracic esophageal cancer (EC).

METHODS

In this retrospective study, patients with histopathologically confirmed EC who underwent DECT from September 2019 to December 2020 were enrolled. One standard polyenergetic image (PEI) and five MEI (+) were reconstructed. Two readers independently assessed the lesion conspicuity subjectively and calculated the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of EC. Two readers independently assessed the T stage on the optimal MEI (+) and PEI subjectively. Multiple quantitative parameters were measured to assess the diagnostic performance to identify T1-2 from T3-4 in EC patients.

RESULTS

The study included 68 patients. Subjectively, primary tumor delineation received the highest ratings in MEI (+) _40 keV of the venous phase. Objectively, MEI (+) images showed significantly higher SNR compared with PEI (p < 0.05), peaking at MEI (+) _40 keV in the venous phase. CNR of tumor (MEI (+) _{40 keV -80 keV}) was all significantly higher than PEI in arterial and venous phases (p < 0.05), peaking at MEI (+) _40 keV in venous phases. The agreement between MEI (+) _40 keV and pathologic T categories was 81.63% (40/49). Rho values in venous phases had excellent diagnostic efficiency for identifying T1-2 from T3-4 (AUC = 0.84).

CONCLUSIONS

MEI (+) reconstructions at low keV in the venous phase improved the assessment of lesion conspicuity and also have great potential for preoperative assessment of T staging in patients with EC.

Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC.

METHODS

In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations. Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λ_HU), and normalized effective atomic number (NZ_eff) were calculated. The attenuation values on 40-140 keV noise-optimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves.

RESULTS

The NIC, λ_HU, NZ_eff, and attenuation value on the VMIs (+) at 40 keV (A₄₀) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively).

CONCLUSIONS

Quantitative parameters derived from pretreatment DECT, including NIC, λ_HU, NZ_eff, and A_4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC. Combining all these characteristics improved the predictive performance of the model.

Spectral CT Hybrid Images in the Diagnostic Evaluation of Hypervascular Abdominal Tumors-Potential Advantages in Clinical Routine

Background: This study aimed to investigate the use of spectral computed tomography (SCT) hybrid images combining virtual monoenergetic images (VMIs) and iodine maps (IMs) as a potentially efficient search series for routine clinical imaging in patients with hypervascular abdominal tumors. Methods: A total of 69 patients with hypervascular abdominal tumors including neuroendocrine neoplasms (NENs, n = 48), renal cell carcinoma (RCC, n = 10), and primary hepatocellular carcinoma (HCC, n = 11) were analyzed retrospectively. Two radiological readers (blinded to clinical data) read three CT image sets (1st a reference set with 70 keV; 2nd a 50:50 hybrid 140 keV/40 keV set; 3rd a 50:50 hybrid 140 keV/IM set). They assessed images subjectively by rating several parameters including image contrast, visibility of suspicious lesions, and diagnostic confidence on five-point Likert scales. In addition, reading time was estimated. Results: Median subjective Likert scores were highest for the 1st set, except for image contrast, for which the 2nd set was rated highest. Scores for diagnostic confidence, artifacts, noise, and visibility of suspicious lesions or small structures were significantly higher for the 1st set than for the 2nd or 3rd set (p < 0.001). Regarding image contrast, the 2nd set was rated significantly higher than the 3rd set (p < 0.001), while the median did not differ significantly compared with the 1st set. Agreement between the two readers was high for all sets. Estimated potential reading time was the same for hybrid and reference sets. Conclusions: Hybrid images have the potential to efficiently exploit the additional information provided by SCT in patients with hypervascular abdominal tumors. However, the use of rigid weighting did not significantly improve diagnostic performance in this study.

Dual-Energy Computed Tomography in Spine Fractures: A Systematic Review and Meta-Analysis

BACKGROUND

The purpose of this study was to perform a systematic literature review and meta-analysis to evaluate the sensitivity, specificity, and accuracy of dual-energy computed tomography (DE-CT) of bone marrow edema and disc edema in spine injuries.In vertebral injuries, prompt diagnosis is essential to avoid any delays in treatment. Conventional radiography may only reveal indirect signs of fractures, such as when it is displaced. Therefore, to detect the presence of bone marrow or disc edemas, adjunctive tools are required, such as magnetic resonance imaging (MRI) or DE-CT.

METHODS

Search terms included ((DECT) OR (DE-CT) OR (dual-energy CT) OR "Dual energy CT" OR (dual-energy computed tomography) OR (dual energy computed tomography)) AND ((spine) OR (vertebral)), and the PubMed, EMBASE, and MEDLINE databases and the Cochrane Library and Google were used. We found 1233 articles on our preliminary search, but only 13 articles met all criteria. Data were extracted to calculate the pooled sensitivity, specificity, and diagnostic odds ratio for analysis using R software.

RESULTS

Within the 13 studies, 515 patients, 3335 vertebrae, and 926 acute fractures (27.8%) defined by MRI were included. The largest cohort included 76 patients with 774 vertebrae. In 12 publications, MRI was reported for comparison. For DE-CT, the overall sensitivity was 86.2% with a specificity of 91.2% and accuracy of 89.3%. Furthermore, 5 studies reported the accuracy of CT with an overall sensitivity of 81.3%, specificity of 80.7%, and accuracy with 80.9%. Significant differences were found for specificity (P < .001) and accuracy (P = .023). However, significant interobserver differences were reported.

CONCLUSIONS

DE-CT seems to be a promising diagnostic tool to detect bone marrow and disc edemas, which can potentially replace the current gold standard, the MRI.

LEVEL OF EVIDENCE

2.

CLINICAL RELEVANCE

This study shows that DE-CT seems to be a promising diagnostic tool with an accuracy of 89.3%.

Dual-energy computed tomography of the neck-optimizing tube current settings and radiation dose using a 3D-printed patient phantom

Background

Dual-energy computed tomography (DECT) is increasingly used in studies and clinical practice. However, the best protocol is controversially discussed and whether it exhibits more radiation exposure compared to conventional protocols. Thus, the purpose of the study was to determine optimal tube current settings for DECT in a 3D-printed anthropomorphic phantom of the neck.

Methods

A 3D-printed iodinated ink based phantom of a contrast enhanced CT of the neck was imaged. Six dual-energy multi-detector computed tomography scans were performed with six different tube currents (80 kVp: 30-400 mAs; 135 kVp: 5-160 mAs). 120 virtual blended images (VBIs) and 66 virtual monochromatic images (VMIs) were reconstructed and 12 regions of interest (bilaterally: common carotid arteries, subcutaneous soft tissue, mandibular bone, sternocleidomastoid muscle, submandibular gland, and mid-image: vertebral body of C2 and pharyngeal space) in six consecutive slices resulting in 96 measurements per scan were performed. Hounsfield units and signal- and contrast-to-noise ratio were compared to single-energy computed tomography as standard of reference.

Results

VBIs overestimated the Hounsfield units (P<0.0001). Optimal dual-energy scanning parameters resulted in 120% (100 kVe: 51.2 vs. 61.7 and 65.2, for signal and contrast-to-noise ratio, respectively; 120 kVe: 60.8 vs. 72.1 vs. 128.3) of the radiation exposure with about 80% of the signal/contrast-to-noise ratio of the corresponding single-energy images. However, optimal weighting of tube currents for both voltages depended on the desired reconstruction.

Conclusions

Dual-energy protocols apply an estimated 120% of the single-energy radiation exposure and result in approximately 80% of the image quality. Tube current settings should be adapted to the desired information.

Usefulness of dual-layer spectral CT in follow-up examinations: diagnosing recurrent squamous cell carcinomas in the head and neck

PURPOSE

To evaluate the usefulness of dual-energy analyses using dual-layer spectral CT (DLSCT) for diagnosing recurrent lesions of head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

The study population comprised 62 patients with a history of HNSCC. Attenuation values on conventional 120-kVp images and 40-keV virtual monochromatic images (VMIs) and iodine concentration (IC) were compared between recurrent lesions and post-treatment changes or non-recurrent nodes using the Mann-Whitney U test. Receiver-operating characteristic (ROC) analysis was used to assess the ability of attenuation values and IC to diagnose recurrent lesions.

RESULTS

Attenuation values for 120-kVp and 40-keV images and IC of local recurrent lesions were significantly higher than those of post-treatment changes (p < 0.001), whereas recurrent nodes showed significantly lower attenuation values for both 120 kVp and 40 keV and IC than non-recurrent nodes (p < 0.001). Area under the ROC curves for 120-kVp images, 40-keV images, and IC to diagnose local recurrences were 0.912, 0.992, and 0.984, respectively, and those to diagnose recurrent nodes were 0.819, 0.922, and 0.934, respectively.

CONCLUSIONS

Dual-energy images using DLSCT, particularly 40-keV VMIs and IC, may help in diagnosing recurrent lesions of HNSCC.

Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy

Purpose

To determine whether machine learning assisted-texture analysis of multi-energy virtual monochromatic image (VMI) datasets from dual-energy CT (DECT) can be used to differentiate metastatic head and neck squamous cell carcinoma (HNSCC) lymph nodes from lymphoma, inflammatory, or normal lymph nodes.

Materials and methods

A retrospective evaluation of 412 cervical nodes from 5 different patient groups (50 patients in total) having undergone DECT of the neck between 2013 and 2015 was performed: (1) HNSCC with pathology proven metastatic adenopathy, (2) HNSCC with pathology proven benign nodes (controls for (1)), (3) lymphoma, (4) inflammatory, and (5) normal nodes (controls for (3) and (4)). Texture analysis was performed with TexRAD® software using two independent sets of contours to assess the impact of inter-rater variation. Two machine learning algorithms (Random Forests (RF) and Gradient Boosting Machine (GBM)) were used with independent training and testing sets and determination of accuracy, sensitivity, specificity, PPV, NPV, and AUC.

Results

In the independent testing (prediction) sets, the accuracy for distinguishing different groups of pathologic nodes or normal nodes ranged between 80 and 95%. The models generated using texture data extracted from the independent contour sets had substantial to almost perfect agreement. The accuracy, sensitivity, specificity, PPV, and NPV for correctly classifying a lymph node as malignant (i.e. metastatic HNSCC or lymphoma) versus benign were 92%, 91%, 93%, 95%, 87%, respectively.

Conclusion

Machine learning assisted-DECT texture analysis can help distinguish different nodal pathology and normal nodes with a high accuracy.

An update on advanced dual-energy CT for head and neck cancer imaging

Introduction: Dual-energy-computed tomography (DECT) is an advanced form of computed tomography (CT) that enables spectral tissue characterization beyond what is possible with conventional CT scans. DECT can improve non-invasive diagnostic evaluation of the neck, especially for the evaluation of head and neck cancer. Areas covered: This article is a review of current applications of DECT for the evaluation of head and neck cancer, focusing largely on squamous cell carcinoma (HNSCC). The article will begin with a brief overview of principles and different approaches for DECT scanning. This will be followed by a review of different DECT applications in diagnostic imaging and radiation oncology, practical and workflow considerations, and various emerging advanced applications for tumor analysis, including the use of DECT datasets for radiomics and machine learning applications. Expert opinion: Using a multi-parametric approach, different DECT reconstructions can be used to improve diagnostic evaluation and surveillance of head and neck cancer, including improving visibility of HNSCC, determination of tumor boundaries and extent, and invasion of critical organs such as the thyroid cartilage. In the future, the large amount of quantitative information on DECT scans may be leveraged for improving radiomic and machine learning models for tumor characterization.

Fast kilovoltage-switching dual-energy CT offering lower x-ray dose than single-energy CT for the chest: a quantitative and qualitative comparison study of the two methods of acquisition

PURPOSE

We aimed to compare the size-specific dose estimates (SSDE), computed tomography (CT) dose indices and image quality parameters of the chest CTs obtained with fast kilovoltage-switching (FKS) dual-energy (DE) CT versus those with single-energy (SE) CT.

METHODS

Patients who had chest SECT within the last 6 months were prospectively scanned with chest FKS-DECT. Quantitative comparison was made by calculating the mean SSDE, CTDIvol, contrast, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) for both acquisitions. Two radiologists evaluated the chest SECT and DECT images qualitatively blinded to the technique used. The paired Student's t test was utilized for comparing the quantitative and qualitative data. Inter- and intraobserver agreement were also assessed.

RESULTS

A total of 42 patients were included. The mean SSDE, CTDIvol, contrast, noise, CNR, and SNR for SECT versus DECT were 12.7±2.2 mGy vs. 9.3±1.2 mGy (P = 0.001), 10.9±2.4 mGy vs. 8±1.2 mGy (P < 0.001), 211.9±44.7 vs. 216.3±59 (P = 0.350), 12.9±2.4 vs. 13.9±3.7 (P = 0.086), 13.5±5.2 vs. 13.3±8.4 (P = 0.548) and 12±3.5 vs. 11.5±3.4 (P = 0.774), respectively. Interobserver reproducibility was high for contrast, noise, CNR, and SNR (ICC = 0.89, 0.85, 0.93, and 0.82, respectively; all P < 0.05). Intraobserver reproducibility was high for contrast, noise, CNR, and SNR (ICC = 0.80, 0.77, 0.85, and 0.88, respectively; all P < 0.05).

CONCLUSION

The mean SSDE of the chest CTs obtained with FKS-DECT were 26.8% lower than those with SECT with significant difference for the objective assessment and there was no significant difference for the subjective assessment of the image qualities, in this series.

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning

OBJECTIVES

This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction.

METHODS

Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck.

RESULTS

Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy.

CONCLUSIONS

Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone.

KEY POINTS

• Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

The Role of Dual-Energy Computed Tomography in Assessment of Abdominal Oncology and Beyond

The added value and strength of dual energy computed tomography for the evaluation of oncologic patients revolve around the use of lower energy reconstructed images and iodine material density images. Lower keV simulated monoenergetic images optimize soft tissue tumor to nontumoral attenuation differences and increase contrast to noise ratios to improve lesion detection. Iodine material density images or maps are helpful from a qualitative standpoint for image interpretation because they result in improved detection and characterization of tumors and lymph node involvement, and from a quantitative assessment by enabling interrogation of specific properties of tissues to predict and assess therapeutic response.

Dual-Energy Computed Tomography: Physical Principles, Approaches to Scanning, Usage, and Implementation: Part 2

There are increasing applications and use of spectral computed tomography or dual-energy computed tomography (DECT) in neuroradiology and head and neck imaging in routine clinical practice. Part 1 of this 2-part review covered fundamental physical principles underlying DECT scanning and the different approaches for scanning. Part 2 focuses on important and practical considerations for implementing and using DECT in clinical practice, including a review of different images and reconstructions produced by these scanners and important and practical issues, ranging from image quality and radiation dose to workflow-related aspects of DECT scanning, that routinely come up during operationalization of DECT.

Comparison of MR Imaging and Dual-Energy CT for the Evaluation of Cartilage Invasion by Laryngeal and Hypopharyngeal Squamous Cell Carcinoma

BACKGROUND AND PURPOSE

Dual-energy CT can distinguish iodine-enhanced tumors from nonossified cartilage and has been investigated for evaluating cartilage invasion in patients with laryngeal and hypopharyngeal squamous cell carcinomas. In this study, we compared the diagnostic accuracy of MR imaging and of a combination of weighted-average and iodine overlay dual-energy CT images in detecting cartilage invasion by laryngeal and hypopharyngeal squamous cell carcinomas, in particular thyroid cartilage invasion.

MATERIALS AND METHODS

Fifty-five consecutive patients who underwent 3T MR imaging and 128-slice dual-energy CT for preoperative initial staging of laryngeal or hypopharyngeal squamous cell carcinomas were included. Two blinded observers evaluated laryngeal cartilage invasion on MR imaging and dual-energy CT using a combination of weighted-average and iodine-overlay images. Pathologic findings of surgically resected specimens were used as the reference standard for evaluating sensitivity, specificity, and the areas under the receiver operating characteristic curve of both modalities for cartilage invasion by each type of cartilage and for all cartilages together. Sensitivity and specificity were compared using the McNemar test and generalized linear mixed models.

RESULTS

Dual-energy CT showed higher specificity than MR imaging for diagnosing all cartilage together (84% for MR imaging versus 98% for dual-energy CT, P < .004) and for thyroid cartilage (64% versus 100%, P < .001), with a similar average area under the curve (0.94 versus 0.95, P = .70). The sensitivity did not differ significantly for all cartilages together (97% versus 81%, P = .16) and for thyroid cartilage (100% versus 89%, P = .50), though there was a trend toward increased sensitivity with MR imaging.

CONCLUSIONS

Dual-energy CT showed higher specificity and acceptable sensitivity in diagnosing laryngeal cartilage invasion compared with MR imaging.

Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm

OBJECTIVE

There is a rich amount of quantitative information in spectral datasets generated from dual-energy CT (DECT). In this study, we compare the performance of texture analysis performed on multi-energy datasets to that of virtual monochromatic images (VMIs) at 65 keV only, using classification of the two most common benign parotid neoplasms as a testing paradigm.

METHODS

Forty-two patients with pathologically proven Warthin tumour (n = 25) or pleomorphic adenoma (n = 17) were evaluated. Texture analysis was performed on VMIs ranging from 40 to 140 keV in 5-keV increments (multi-energy analysis) or 65-keV VMIs only, which is typically considered equivalent to single-energy CT. Random forest (RF) models were constructed for outcome prediction using separate randomly selected training and testing sets or the entire patient set.

RESULTS

Using multi-energy texture analysis, tumour classification in the independent testing set had accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 86%, 100%, 100%, and 83%, compared to 75%, 57%, 100%, 100%, and 63%, respectively, for single-energy analysis.

CONCLUSIONS

Multi-energy texture analysis demonstrates superior performance compared to single-energy texture analysis of VMIs at 65 keV for classification of benign parotid tumours.

KEY POINTS

• We present and validate a paradigm for texture analysis of DECT scans. • Multi-energy dataset texture analysis is superior to single-energy dataset texture analysis. • DECT texture analysis has high accura\cy for diagnosis of benign parotid tumours. • DECT texture analysis with machine learning can enhance non-invasive diagnostic tumour evaluation.

The Utility of Dual Energy Computed Tomography in Musculoskeletal Imaging

The objective of this article is to review the mechanisms, advantages and disadvantages of dual energy computed tomography (DECT) over conventional tomography (CT) in musculoskeletal imaging as DECT provides additional information about tissue composition and artifact reduction. This provides clinical utility in detection of urate crystals, bone marrow edema, reduction of beam hardening metallic artifact, and ligament and tendon analysis.

Low-Energy Virtual Monochromatic Dual-Energy Computed Tomography Images for the Evaluation of Head and Neck Squamous Cell Carcinoma: A Study of Tumor Visibility Compared With Single-Energy Computed Tomography and User Acceptance

OBJECTIVE

Dual-energy computed tomography (CT) 40-keV virtual monochromatic images (VMIs) have been reported to improve visualization of head and neck squamous cell carcinoma, but a direct comparison to single-energy CT (SECT) is lacking, and there is debate regarding subjective user preference. We compared 40-keV VMIs with SECT and performed a subjective evaluation of their utility and acceptability for clinical use.

METHODS

A total of 60 dual-energy CT and 60 SECT scans from 2 different institutions were evaluated. Tumor conspicuity was evaluated objectively using absolute and relative attenuation and subjectively by 3 head and neck specialists and 3 general radiologists.

RESULTS

Tumors had significantly higher absolute and relative attenuation on 40-keV VMIs (P < 0.0001). Subjectively, the 40-keV VMIs improved visualization, with substantial (κ, 0.61-0.80) to almost perfect (κ, 0.81-1) interrater agreements.

CONCLUSIONS

The 40-keV VMIs improve tumor visibility objectively and subjectively both by head and neck specialists and general radiologists.

Dual energy CT iodine map for delineating inflammation of inflammatory arthritis

Iodine mapping is an image-processing technique used with dual-energy computed tomography (DECT) to improve iodine contrast resolution. CT, because of its high spatial resolution and thin slice reconstruction, is well suited to the evaluation of the peripheral joints. Recent developments in the treatment of inflammatory arthritis that require early diagnosis and precise therapeutic assessment encourage radiological evaluation. To facilitate such assessment, we describe DECT iodine mapping as a novel modality for evaluating rheumatoid arthritis and psoriatic arthritis of the hands and feet.

KEY POINTS

• Dual-energy CT iodine mapping can delineate inflammation of peripheral inflammatory arthritis. • DECT iodine mapping has high spatial resolution compared with MRI. • DECT iodine mapping has a high iodine contrast resolution. • DECT iodine mapping may reflect therapeutic effects.

Advanced Tissue Characterization and Texture Analysis Using Dual-Energy Computed Tomography: Horizons and Emerging Applications

In the last article of this issue, advanced analysis capabilities of DECT is reviewed, including spectral Hounsfield unit attenuation curves, virtual monochromatic images, material decomposition maps, tissue effective Z determination, and other advanced post-processing DECT tools, followed by different methods of analysis of the attenuation curves generated using DECT. The article concludes with exciting future horizons and potential applications, such as the use of the rich quantitative data in dual energy CT scans for texture or radiomic analysis and the use of machine learning methods for generation of prediction models using spectral data.

Dual-Energy Computed Tomography Applications for the Evaluation of Cervical Lymphadenopathy

There is recent interest in the use of dual-energy computed tomography (CT) in head and neck imaging, and the results are encouraging. This article reviews dual-energy CT applications as complementary tools to conventional CT scanning in the evaluation of cervical lymphadenopathy. The article cites the most relevant studies and highlights their results. Single-source and dual-source dual-energy applications including virtual noncontrast images, linear and nonlinear image blending, monochromatic images, iodine quantification, and spectral Hounsfield unit attenuation curve analysis are reviewed. Future directions and research suggestions are discussed in brief.

Applications of Dual-Energy Computed Tomography for Artifact Reduction in the Head, Neck, and Spine

Conventional computed tomography (CT) uses a polychromatic energy beam to offer superb anatomic detail of the head and spine. However, technical challenges remain that can degrade the diagnostic image quality of these examinations. Dual-energy CT analyzes the changes in attenuation of soft tissues at different energy levels, from which different reconstructions can be made to yield the optimal contrast-to-noise ratio, reduce beam-hardening artifact, or evaluate tissue composition. In this article, selective applications of the dual energy CT technique are discussed, highlighting a powerful tool in the diagnostic CT evaluation of the head, neck, and spine.

Advanced dual-energy CT applications for the evaluation of the soft tissues of the neck

There are multiple emerging advanced computed tomography (CT) applications for the evaluation of the neck, many based on dual-energy CT (DECT). DECT is an advanced form of CT in which scan acquisition is performed at two different energies, enabling spectral tissue characterisation beyond what is possible with conventional single-energy CT and potentially providing a new horizon for quantitative analysis and tissue characterisation, particularly in oncological imaging. The purpose of this review is to familiarise the reader with DECT principles and review different clinical applications for the evaluation of the soft tissues of the neck. The article will begin with an overview of DECT scan acquisition, material characterisation, reconstructions, and basic considerations for implementation in the clinical setting. This will then be followed by a review of different clinical applications. The focus will be on oncological imaging, but artefact reduction and other miscellaneous applications will also be discussed.

Dual-Energy CT Characteristics of Parathyroid Adenomas on 25-and 55-Second 4D-CT Acquisitions: Preliminary Experience

OBJECTIVE

The objective of this study was to compare the dual-energy computed tomography (CT) characteristics of parathyroid adenomas (PAs), thyroid tissue, and lymph nodes (LNs) and assess whether the spectral information can improve distinction of these tissues.

METHODS

Dual-energy CT scans from 20 patients with pathologically proven PAs were retrospectively evaluated, identifying 19 eligible PAs and region of interest analysis used for spectral characterization.

RESULTS

There was a significant difference in multiple spectral parameters between PAs, LNs, and the thyroid gland (P < 0.05-0.0001). The greatest difference in spectral characteristics of PAs compared with that of LNs was on the 25-second acquisition, whereas the 55-second acquisition was better for distinguishing PAs from the thyroid gland.

CONCLUSIONS

Four-dimensional CT acquired in dual-energy CT mode has the potential to further enhance diagnostic accuracy for PA identification on individual phases of the perfusion study.