Virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in head and neck cancer

Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model

While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel method to generate synthetic low-energy virtual monochromatic images at 50 keV (sVMI50keV) from SECT images using a transformer-based deep learning model, SwinUNETR. Data were obtained from 85 patients who underwent head and neck radiotherapy. Among these, the model was built using data from 70 patients for whom only DECT images were available. The remaining 15 patients, for whom both DECT and SECT images were available, were used to predict from the actual SECT images. We used the SwinUNETR model to generate sVMI50keV. The image quality was evaluated, and the results were compared with those of the convolutional neural network-based model, Unet. The mean absolute errors from the true VMI50keV were 36.5 ± 4.9 and 33.0 ± 4.4 Hounsfield units for Unet and SwinUNETR, respectively. SwinUNETR yielded smaller errors in tissue attenuation values compared with those of Unet. The contrast changes in sVMI50keV generated by SwinUNETR from SECT were closer to those of DECT-derived VMI50keV than the contrast changes in Unet-generated sVMI50keV. This study demonstrated the potential of transformer-based models for generating synthetic low-energy VMIs from SECT images, thereby improving the image quality of head and neck cancer imaging. It provides a practical and feasible solution to obtain low-energy VMIs from SECT data that can benefit a large number of facilities and patients without access to DECT technology.

Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer

PURPOSE

While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images.

METHODS

This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI50keV and VMI100keV from VMI70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images.

RESULTS

The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality.

CONCLUSION

In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

Know your way around acute unenhanced CT during global iodinated contrast crisis: a refresher to ED radiologists

Due to a contrast shortage crisis resulting from the decreased supply of iodinated contrast agents, the American College of Radiology (ACR) has issued a guidance statement followed by memoranda from various hospitals to preserve and prioritize the limited supply of contrast. The vast majority of iodinated contrast is used by CT, with a minority used by vascular and intervention radiology, fluoroscopy, and other services. A direct consequence is a paradigm shift to large volume unenhanced CT scans being utilized for acute and post traumatic patients in EDs, an uncharted territory for most radiologists and trainees. This article provides radiological diagnostic guidance and a pictorial example through systematic review of common unenhanced CT findings in the acute setting.

Clinical applications of dual-energy computed tomography in neuroradiology

Dual-energy computed tomography (DECT) has developed into a robust set of techniques with increasingly validated clinical applications in neuroradiology. We review some of the most common applications in neuroimaging along with demonstrative case examples that showcase the use of this technology in intracranial hemorrhage, stroke imaging, trauma imaging, artifact reduction, and tumor characterization.

Photon Counting CT Angiography of the Head and Neck: Image Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions

Background: The purpose of the present study was the evaluation of the image quality of polyenergetic and monoenergetic reconstructions (PERs and MERs) of CT angiographies (CTAs) of the head and neck acquired with the novel photon counting CT (PCCT) method in clinical routine. Methods: Thirty-seven patients were enrolled in this retrospective study. Quantitative image parameters of the extracranial, intracranial and cerebral arteries were evaluated for the PER and MER (40–120 keV). Additionally, two radiologists rated the perceived image quality. Results: The mean CTDI_vol used in the PCCT was 8.31 ± 1.19 mGy. The highest signal within the vessels was detected in the 40 keV MER, whereas the lowest noise was detected in the 115 keV MER. The most favorable contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were detected in the PER and low keV MER. In the qualitative image analysis, the PER was superior to the MER in all rated criteria. For MER, 60–65 keV was rated as best image quality. Conclusion: Overall, PCCT offers excellent image quality for CTAs of the head and neck. At the current state, the PER of the PCCT seems to be the most favorable reconstruction for diagnostic reporting.

Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

Image quality comparison of single-energy and dual-energy computed tomography for head and neck patients: a prospective randomized study

OBJECTIVES

The aim of this study was to compare the quality of images obtained using single-energy computed tomography (SECT) performed with automated tube voltage adaptation (TVA) with dual-energy CT (DECT) weighted average images.

METHODS

Eighty patients were prospectively randomized to undergo either SECT with TVA (n = 40, ref. mAs 200) or radiation dose-matched DECT (n = 40, 80/Sn150 kV, ref. mAs tube A 91/tube B 61) on a dual-source CT scanner. Objective image quality was evaluated as dose-normalized contrast-to-noise ratio (CNRD) for the jugular veins relative to fatty tissue and muscle tissue and for muscle tissue relative to fatty issue. For subjective image quality, reproduction of anatomical structures, image artifacts, image noise, spatial resolution, and overall diagnostic acceptability were evaluated at sixteen anatomical substructures using Likert-type scales.

RESULTS

Effective radiation dose (ED) was comparable between SECT and DECT study groups (2.9 ± 0.6 mSv/3.1 ± 0.7 mSv, p = 0.5). All examinations were rated as excellent or good for clinical diagnosis. Compared to the CNRD in the SECT group, the CNRD in the DECT group was significantly higher for the jugular veins relative to fatty tissue (7.51/6.08, p < 0.001) and for muscle tissue relative to fatty tissue (4.18/2.90, p < 0.001). The CNRD for the jugular veins relative to muscle tissue (3.33/3.18, p = 0.51) was comparable between groups. Image artifacts were less pronounced and overall diagnostic acceptability was higher in the DECT group (all p = 0.01).

CONCLUSIONS

DECT weighted average images deliver higher objective and subjective image quality than SECT performed with TVA in head and neck imaging.

KEY POINTS

• Weighted average images derived from dual-energy CT deliver higher objective and subjective image quality than single-energy CT using automated tube voltage adaptation in head and neck imaging. • If available, dual-energy CT acquisition may be preferred over automated low tube voltage adopted single-energy CT for both malignant and non-malignant conditions.

Principles and Applications of Dual Energy Computed Tomography in Neuroradiology

Dual-energy computed tomography (DE CT) is a promising tool with many current and evolving applications. Available DE CT scanners usually consist of one or two tubes, or use layered detectors for spectral separation. Most DE CT scanners can be used in single energy or dual-energy mode, except for the layered detector scanners that always acquire data in dual-energy mode. However, the layered detector scanners can retrospectively integrate the data from two layers to obtain conventional single energy images. DE CT mode enables generation of virtual monochromatic images, blended images, iodine quantification, improving conspicuity of iodinated contrast enhancement, and material decomposition maps or more sophisticated quantitative analysis not possible with conventional SE CT acquisition with an acceptable or even lower dose than the SE CT. This article reviews the basic principles of dual-energy CT and highlights many of its clinical applications in the evaluation of neurological conditions.

Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia

Background

Stage T1 nasopharyngeal carcinoma (NPC_T1) and benign hyperplasia (BH) are 2 common causes of nasopharyngeal mucosa/submucosa thickening without specific clinical symptoms. The treatment management of these 2 entities is significantly different. Reliable differentiation between the 2 entities is critical for the treatment decision and prognosis of patients. Therefore, our study aims to explore the optimal energy level of noise-optimized virtual monoenergetic images [VMI (+)] derived from dual-energy computed tomography (DECT) to display NPC_T1 and BH and to explore the clinical value of DECT for differentiating these 2 diseases.

Methods

A total of 91 patients (44 NPC_T1, 47 BH) were enrolled. The demarcation of the lesion margins and overall image quality, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were evaluated for 40-80 kiloelectron volts (keV) VMIs (+) and polyenergetic images in the contrast-enhanced phase. Image features were assessed in the contrast-enhanced images with optimal visualization of NPC_T1 and BH. The demarcation of NPC_T1 and BH in iodine-water maps was also assessed. The contrast-enhanced images were used to calculate the slope of the spectral Hounsfield unit curve (λ_HU) and normalized iodine concentration (NIC). The nonenhanced phase images were used to calculate the normalized effective atomic number (NZ_eff). The attenuation values on 40-80 keV VMIs (+) in the contrast-enhanced phase were recorded. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis.

Results

The 40 keV VMI (+) in the enhanced phase yielded higher demarcation of the lesion margins scores, overall image quality scores, noise, SNR, and CNR values than 50-80 keV VMIs (+) and polyenergetic images. NPC_T1 yielded higher attenuation values on VMI (+) at 40 keV (A₄₀), NIC, λ_HU, and NZ_eff values than BH. The multivariate logistic regression model combining image features (tumor symmetry) with quantitative parameters (A₄₀, NIC, λ_HU, and NZ_eff) yielded the best performance for differentiating the 2 diseases (AUC: 0.963, sensitivity: 89.4%, specificity: 93.2%).

Conclusions

The combination of DECT-derived image features and quantitative parameters contributed to the differentiation between NPC_T1 and BH.

Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters

PURPOSE

Virtual monoenergetic images (VMI) obtained from Dual-Energy Computed Tomography (DECT) with iodinated contrast are used in radiotherapy of the Head and Neck to improve the delineation of target volumes and organs at-risk (OAR). The energies used to vary from 40 to 70 keV, but noise at low keV and the use of Single Energy CT (SECT) at low kV_p settings may shrink this interval. There is no guide about how to find out the optimal range where VMI has a significant improvement related to SECT images. Our study proposes a procedure to determine this optimal range, based on common image quality parameters, and establishes this range in a Siemens Somatom Confidence and a Head and Neck protocol.

METHODS

We compared the quality of the VMI series at 40-60 keV versus single X-ray tube voltage computed tomography (SECT) at 80 and 120 kV_p . Our reference was 120 kV_p . DECT images were sequentially acquired using the Siemens Somatom Confidence RT Pro CT according to the head and neck protocol in our department. VMI series were constructed using the Syngo Via software Monoenergetic+ algorithm. Quality parameters were: image uniformity, high- and low-contrast resolution, noise, and sensitivity to the iodinated contrast. We used the Catphan 604 phantom for quality control, except when assessing iodine sensitivity. To evaluate high contrast resolution, we calculated the modulation transfer function (MTF) using the point spread function estimation of a point bead and the slanted edge methods. For the low-contrast resolution, we used a statistical method for assessing differences between contrast structures and local noise. To measure the absolute value of noise and compare its texture, we used the standard deviation and the noise power spectrum. We measured iodine sensitivity by dissolving the Optiray Ultraject iodinated contrast in water in concentrations of 0 to 4500 mg/l and then compared the contrast to noise ratio (CNR) and analyzed the linear correlation between concentration and HU.

RESULTS

The entire series met the minimum quality requirements. However, the one at 40 keV presented uniformity at the limits of acceptability. The high- and low-contrast resolutions were similar between series. The noise of the VMI series decreased with increasing energy, while sensitivity to the contrast displayed the opposite behavior. All series showed linearity of HUs from very low iodine concentrations. Images at 60 keV presented lower iodine sensitivity than SECT at 80 kV_p , while those at 55 keV were similar to them.

CONCLUSIONS

Our method of image comparison based on standard quality parameters in phantom gave clear results about the optimal range and can be used as a guide to characterize any other DECT imaging protocols. The optimal range for using VMI images in iodinated contrasts in the Siemens system was 45-55 keV. Lower energies lacked noise and uniformity, while higher ones could be substituted by SECT images at low kilovoltage (80 kV_p ).

Improvement of image quality of laryngeal squamous cell carcinoma using noise-optimized virtual monoenergetic image and nonlinear blending image algorithms in dual-energy computed tomography

BACKGROUND

Dual-energy computed tomography (DECT) has been used to improve image quality of head and neck squamous cell carcinoma (SCC). This study aimed to assess image quality of laryngeal SCC using linear blending image (LBI), nonlinear blending image (NBI), and noise-optimized virtual monoenergetic image (VMI+) algorithms.

METHODS

Thirty-four patients with laryngeal SCC were retrospectively enrolled between June 2019 and December 2020. DECT images were reconstructed using LBI (80 kV and M_0.6), NBI, and VMI+ (40 and 55 keV) algorithms. Contrast-to-noise ratio (CNR), tumor delineation, and overall image quality were assessed and compared.

RESULTS

VMI+ (40 keV) had the highest CNR and provided better tumor delineation than VMI+ (55 keV), LBI, and NBI, while NBI provided better overall image quality than VMI+ and LBI (all corrected p < 0.05).

CONCLUSIONS

VMI+ (40 keV) and NBI improve image quality of laryngeal SCC and may be preferable in DECT examination.

Head and neck single- and dual-energy CT: differences in radiation dose and image quality of 2nd and 3rd generation dual-source CT

OBJECTIVES

To compare radiation dose and image quality of single-energy (SECT) and dual-energy (DECT) head and neck CT examinations performed with second- and third-generation dual-source CT (DSCT) in matched patient cohorts.

METHODS

200 patients (mean age 55.1 ± 16.9 years) who underwent venous phase head and neck CT with a vendor-preset protocol were retrospectively divided into four equal groups (n = 50) matched by gender and BMI: second (Group A, SECT, 100-kV; Group B, DECT, 80/Sn140-kV), and third-generation DSCT (Group C, SECT, 100-kV; Group D, DECT, 90/Sn150-kV). Assessment of radiation dose was performed for an average scan length of 27 cm. Contrast-to-noise ratio measurements and dose-independent figure-of-merit calculations of the submandibular gland, thyroid, internal jugular vein, and common carotid artery were analyzed quantitatively. Qualitative image parameters were evaluated regarding overall image quality, artifacts and reader confidence using 5-point Likert scales.

RESULTS

Effective radiation dose (ED) was not significantly different between SECT and DECT acquisition for each scanner generation (p = 0.10). Significantly lower effective radiation dose (p < 0.01) values were observed for third-generation DSCT groups C (1.1 ± 0.2 mSv) and D (1.0 ± 0.3 mSv) compared to second-generation DSCT groups A (1.8 ± 0.1 mSv) and B (1.6 ± 0.2 mSv). Figure-of-merit/contrast-to-noise ratio analysis revealed superior results for third-generation DECT Group D compared to all other groups. Qualitative image parameters showed non-significant differences between all groups (p > 0.06).

CONCLUSION

Contrast-enhanced head and neck DECT can be performed with second- and third-generation DSCT systems without radiation penalty or impaired image quality compared with SECT, while third-generation DSCT is the most dose efficient acquisition method.

ADVANCES IN KNOWLEDGE

Differences in radiation dose between SECT and DECT of the dose-vulnerable head and neck region using DSCT systems have not been evaluated so far. Therefore, this study directly compares radiation dose and image quality of standard SECT and DECT protocols of second- and third-generation DSCT platforms.

Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks

Organ delineation is crucial to diagnosis and therapy, while it is also labor-intensive and observer-dependent. Dual energy CT (DECT) provides additional image contrast than conventional single energy CT (SECT), which may facilitate automatic organ segmentation. This work aims to develop an automatic multi-organ segmentation approach using deep learning for head-and-neck region on DECT. We proposed a mask scoring regional convolutional neural network (R-CNN) where comprehensive features are firstly learnt from two independent pyramid networks and are then combined via deep attention strategy to highlight the informative ones extracted from both two channels of low and high energy CT. To perform multi-organ segmentation and avoid misclassification, a mask scoring subnetwork was integrated into the Mask R-CNN framework to build the correlation between the class of potential detected organ's region-of-interest (ROI) and the shape of that organ's segmentation within that ROI. We evaluated our model on DECT images from 127 head-and-neck cancer patients (66 training, 61 testing) with manual contours of 19 organs as training target and ground truth. For large- and mid-sized organs such as brain and parotid, the proposed method successfully achieved average Dice similarity coefficient (DSC) larger than 0.8. For small-sized organs with very low contrast such as chiasm, cochlea, lens and optic nerves, the DSCs ranged between around 0.5 and 0.8. With the proposed method, using DECT images outperforms using SECT in almost all 19 organs with statistical significance in DSC (p<0.05). Meanwhile, by using the DECT, the proposed method is also significantly superior to a recently developed FCN-based method in most of organs in terms of DSC and the 95th percentile Hausdorff distance. Quantitative results demonstrated the feasibility of the proposed method, the superiority of using DECT to SECT, and the advantage of the proposed R-CNN over FCN on the head-and-neck patient study. The proposed method has the potential to facilitate the current head-and-neck cancer radiation therapy workflow in treatment planning.

Comparison of noise-optimized linearly blended images and noise-optimized virtual monoenergetic images evaluated by dual-source, dual-energy CT in cardiac vein assessment

BACKGROUND

The coronary venous system is frequently used as an entry route to the heart and treatment modalities for many cardiac diseases and many procedures. Consequently, evaluation of the coronary venous system and understanding cardiac vein anatomy is crucial.

PURPOSE

To determine the optimal image set in a comparison of noise-optimized linearly blended images (F_0.6) and noise-optimized virtual monoenergetic images (VMI+) evaluated by dual-energy computed tomography (DECT) for cardiac vein assessment.

MATERIAL AND METHODS

Thirty-four patients (mean age 58.2 ± 14.2 years) who underwent DECT due to chest pain were enrolled. Images were post-processed with the F_0.6, and VMI+ algorithms at energy levels in the range of 40-100 keV in 10-keV increments. Enhancement (HU), noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively measured at two points in the great cardiac vein by consensus of two radiologists. Two blinded observers evaluated the subjective image quality of the great cardiac vein on a 4-point scale.

RESULTS

HU, noise, and SNR peaked at 40 keV VMI+ (P < 0.05) among 50-100 keV VMI+. CNR peaked at 100 keV VMI+; however, there were no significant differences compared to CNR images processed at 40-90 keV VMI+. HU and noise were significantly higher in 40 keV VMI+ than F_0.6 images; however, both SNR and CNR were significantly higher in F_0.6 images. An assessment of subjective vein delineation revealed that F_0.6 images had the highest scores.

CONCLUSION

F_0.6 images were superior to VMI+ and provided the optimal image set for cardiac vein assessment.

A New Outlook on the Ability to Accumulate an Iodine Contrast Agent in Solid Lung Tumors Based on Virtual Monochromatic Images in Dual Energy Computed Tomography (DECT): Analysis in Two Phases of Contrast Enhancement

For some time, dual energy computed tomography (DECT) has been an established method used in a vast array of clinical applications, including lung nodule assessment. The aim of this study was to analyze (using monochromatic DECT images) how the X-ray absorption of solitary pulmonary nodules (SPNs) depends on the iodine contrast agent and when X-ray absorption is no longer dependent on the accumulated contrast agent. Sixty-six patients with diagnosed solid lung tumors underwent DECT scans in the late arterial phase (AP) and venous phase (VP) between January 2017 and June 2018. Statistically significant correlations (p ≤ 0.001) of the iodine contrast concentration were found in the energy range of 40–90 keV in the AP phase and in the range of 40–80 keV in the VP phase. The strongest correlation was found between the concentrations of the contrast agent and the scanning energy of 40 keV. At the higher scanning energy, no significant correlations were found. We concluded that it is most useful to evaluate lung lesions in DECT virtual monochromatic images (VMIs) in the energy range of 40–80 keV. We recommend assessing SPNs in only one phase of contrast enhancement to reduce the absorbed radiation dose.

Incremental Diagnostic Value of Virtual Noncalcium Dual-Energy Computed Tomography for the Depiction of Cervical Disk Herniation Compared With Standard Gray-Scale Computed Tomography

OBJECTIVES

The aim of this study was to investigative the diagnostic accuracy of colored dual-energy computed tomography (CT) virtual noncalcium (VNCa) series for analyzing cervical disk herniation compared with standard gray-scale CT images, with magnetic resonance imaging (MRI) serving as standard of reference.

MATERIALS AND METHODS

Data from 57 patients who underwent noncontrast dual-source CT and 3.0-Tesla (T) MRI within 2 weeks between January 2017 and December 2018 were retrospectively analyzed. Five radiologists analyzed standard gray-scale dual-energy CT scans for the presence and degree of cervical disk herniation and spinal nerve root impingement. Readers reassessed scans after 8 weeks using colored VNCa series. Two experienced radiologists set the reference standard in consensus MRI reading sessions. Primary indices of diagnostic accuracy for both CT approaches were sensitivity and specificity, which were compared by application of the McNemar test.

RESULTS

A total of 57 patients (mean age, 64 ± 11 years; 30 women) were evaluated (337 intervertebral disks). Magnetic resonance imaging indicated a total of 103 cervical disk herniations. The VNCa reconstructions had higher overall sensitivity compared with gray-scale CT (487/515 [95%; 95% confidence interval (CI), 91%-98%] vs 392/515 [76%; 95% CI, 70%-83%]), as well as higher specificity (1107/1170 [95%; 95% CI, 90%-99%] vs 906/1170 [77%; 95% CI, 72%-82%]) for assessing cervical disk herniation (all P < 0.001). The VNCa reconstructions had higher diagnostic accuracy for analyzing spinal nerve root impingement in comparison with gray-scale CT (sensitivity, 195/230 [85%; 95% CI, 79%-90%] vs 115/230 [50%; 95% CI, 40%-59%]; specificity, 1430/1455 [98%; 95% CI, 94%-100%] vs 1325/1455 [91%; 95% CI, 88%-98%]; accuracy, 1625/1685 [96%; 95% CI, 93%-99%] vs 1440/1685 [86%; 95% CI, 82%-90%]; all P < 0.001).

CONCLUSIONS

Color-coded VNCa series improved the diagnostic accuracy for assessing cervical disk herniation and spinal nerve root impingement compared with standard gray-scale CT.

Clinical Applications of Dual-Energy CT

Dual-energy CT (DECT) provides insights into the material properties of tissues and can differentiate between tissues with similar attenuation on conventional single-energy imaging. In the conventional CT scanner, differences in the X-ray attenuation between adjacent structures are dependent on the atomic number of the materials involved, whereas in DECT, the difference in the attenuation is dependent on both the atomic number and electron density. The basic principle of DECT is to obtain two datasets with different X-ray energy levels from the same anatomic region and material decomposition based on attenuation differences at different energy levels. In this article, we discuss the clinical applications of DECT and its potential robust improvements in performance and postprocessing capabilities.

Multiparametric dual-energy CT for distinguishing nasopharyngeal carcinoma from nasopharyngeal lymphoma

OBJECTIVES

To determine the optimal kiloelectron volt of noise-optimized virtual monoenergetic images [VMI (+)] for visualization of nasopharyngeal carcinoma (NPC) and nasopharyngeal lymphoma (NPL), and to explore the clinical value of quantitative parameters derived from dual-energy computed tomography (DECT) for distinguishing the two entities.

MATERIALS AND METHODS

Eighty patients including 51 with NPC and 29 with NPL were enrolled. The VMIs (+) at 40-80 keV with an interval of 10 keV were reconstructed by contrast enhanced images. The overall image quality and demarcation of lesion margins, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed in VMIs (+) and polyenergetic images (PEI). Normalized iodine concentration (NIC), slope of the spectral Hounsfield unit curve (λ_HU) and effective atomic number (Z_eff) were calculated. Diagnostic performance was assessed by receiver operating characteristic (ROC) curve.

RESULTS

The 40 keV VMI (+) yielded highest overall image quality scores, demarcation of lesion margins scores, SNR and CNR. The values of NIC, λ_HU and Z_eff in NPL were higher than those in NPC (P <  0.001). Multivariate logistic regression model combining NIC, λ_HU and Z_eff showed the best performance for distinguishing NPC from NPL (AUC: 0.947, sensitivity: 93.1 % and specificity: 92.2 %).

CONCLUSION

VMI (+) reconstruction at 40 keV was optimal for visualizing NPC and NPL. Quantitative parameters derived from DECT were helpful for differentiating NPC from NPL.

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.

Multi-energy computed tomography and material quantification: Current barriers and opportunities for advancement

Computed tomography (CT) technology has rapidly evolved since its introduction in the 1970s. It is a highly important diagnostic tool for clinicians as demonstrated by the significant increase in utilization over several decades. However, much of the effort to develop and advance CT applications has been focused on improving visual sensitivity and reducing radiation dose. In comparison to these areas, improvements in quantitative CT have lagged behind. While this could be a consequence of the technological limitations of conventional CT, advanced dual-energy CT (DECT) and photon-counting detector CT (PCD-CT) offer new opportunities for quantitation. Routine use of DECT is becoming more widely available and PCD-CT is rapidly developing. This review covers efforts to address an unmet need for improved quantitative imaging to better characterize disease, identify biomarkers, and evaluate therapeutic response, with an emphasis on multi-energy CT applications. The review will primarily discuss applications that have utilized quantitative metrics using both conventional and DECT, such as bone mineral density measurement, evaluation of renal lesions, and diagnosis of fatty liver disease. Other topics that will be discussed include efforts to improve quantitative CT volumetry and radiomics. Finally, we will address the use of quantitative CT to enhance image-guided techniques for surgery, radiotherapy and interventions and provide unique opportunities for development of new contrast agents.

Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging

The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.

Advanced monoenergetic reconstruction technique in dual-energy computed tomography for evaluation of vascular anatomy before adrenal vein sampling

Background

In adrenal vein sampling, selecting the right adrenal vein is technically difficult and it is important to evaluate the right adrenal vein anatomy before adrenal vein sampling. The advanced monoenergetic reconstruction technique has recently become available and we hypothesized that this technique may be useful.

Purpose

To evaluate the usefulness of the advanced monoenergetic reconstruction technique in dual-energy computed tomography (CT; advanced monoenergetic images) for evaluation of vascular anatomy before adrenal vein sampling.

Material and Methods

Twenty-one patients underwent three-phase (20, 30, and 70 s) contrast-enhanced CT before adrenal vein sampling. Advanced monoenergetic images were reconstructed at 40 keV and analyzed objectively and subjectively in comparison with the standard 120-kVp images. As objective evaluation, the signal-to-noise ratio and contrast-to-noise ratio of the right adrenal vein were assessed. As subjective evaluation, two radiologists assessed the delineation of the right adrenal vein using a 5-point Likert scale (1 = poor, 5 = excellent). Furthermore, the technical success rate of adrenal vein sampling and procedure time were also evaluated.

Results

There was no difference in the signal-to-noise ratio between the two groups. The contrast-to-noise ratios of the right adrenal vein of advanced monoenergetic images were higher than those of the standard images ( P < 0.05). The Likert scores of advanced monoenergetic images were higher than those of the standard images ( P < 0.05). The technical success rate of adrenal vein sampling was 95% (20/21) and the median of procedure time was 103 min (range = 59–197 min).

Conclusion

Advanced monoenergetic imaging appears to be useful in the delineation of the right adrenal vein before adrenal vein sampling.

Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator

To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images. Using an polynomial attenuation mapping model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of aluminum (Al) and polymethyl methacrylate (PMMA). All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and an iterative algorithm (iCBCT, Varian Medical Systems). Hounsfield units (HU), contrast-to-noise ratio (CNR) and RED values were compared against known values. The results after VM-CBCT production showed good material decomposition and consistent HU_VM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements, when compared to polychromatic images, were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. RED values measured for our method resulted in a mean percentage error of 0.0%  ±  1.8%. This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.

Quantitative Assessment of Thyroid Nodules Using Dual-Energy Computed Tomography: Iodine Concentration Measurement and Multiparametric Texture Analysis for Differentiating between Malignant and Benign Lesions

RESULTS

The 34 nodules comprised 14 benign nodules and 20 malignant nodules. Iodine content and Hounsfield unit curve slopes did not differ significantly between benign and malignant thyroid nodules (P = 0.480-0.670). However, significant differences in the texture features of monochromatic images were observed between benign and malignant nodules: histogram mean and median, co-occurrence matrix contrast, gray-level gradient matrix (GLGM) skewness, and mean gradients and variance of gradients for GLGM at 80 keV (P = 0.014-0.044). The highest AUC was 0.77, for the histogram mean and median of images acquired at 80 keV.

CONCLUSIONS

Texture features extracted from monochromatic images using DECT, specifically acquired at high keV, may be a promising diagnostic approach for thyroid nodules. A further large study for incidental thyroid nodules using DECT texture analysis is required to validate our results.

Advanced Virtual Monoenergetic Imaging: Improvement of Visualization and Differentiation of Intramuscular Lesions in Portal-Venous-phase Contrast-enhanced Dual-energy CT

PURPOSE

To evaluate the effect of advanced monoenergetic imaging (MEI+) postprocessing algorithm on the visualization of various intramuscular lesions on portal-venous-phase contrast-enhanced dual-energy computed tomography (DECT).

MATERIAL AND METHODS

Thirty-nine patients (64.3 ± 11.1 years; 26 males) with various intramuscular lesions ranging from malignancy, bleeding, inflammation, edematous changes, and benign neoplasms were included and underwent DECT (100/Sn150kV). Postprocessing with MEI+ technique was used to reconstruct images at four different keV levels (40, 60, 80, 100) and compared to the standard portal-venous-phase CT (CT_pv) images. Image quality was assessed qualitatively (conspicuity, delineation, sharpness, noise, and confidence) by two independent readers using 5-point Likert scales, 5 = excellent; as well as quantitatively by calculating signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR), and area under the receiver operating characteristic (ROC) curve (AUC) for lesion characterization.

RESULTS

Highest lesion enhancement and diagnostic confidence were observed in MEI+ 40 keV, with significant differences to CT_pv (p < 0.001), as well as for malignant lesions (highest conspicuity, noise, and sharpness in MEI+ 40 keV; p < 0.001). CNR calculations revealed highest values for MEI+ 40 keV followed by 60 keV with significant differences to CT_pv, and increasing energy levels. ROC analysis showed highest diagnostic accuracy for 40-keV MEI+ datasets regarding the detection of malignant/benign lesions with AUC values of 98.9% (95%-confidence interval: 96.5, 100) and a standard error of 1.2, further AUC values decreased to 83.6% for MEI+100.

CONCLUSION

MEI+ at low keV levels can significantly improve lesion detection of benign versus malignant intramuscular entities in patients undergoing portal-venous-phase DECT scans due to increased CNR.

Volumetric modulated arc therapy treatment planning based on virtual monochromatic images for head and neck cancer: effect of the contrast-enhanced agent on dose distribution

Virtual monochromatic images (VMIs) at a lower energy level can improve image quality but the computed tomography (CT) number of iodine contained in the contrast‐enhanced agent is dramatically increased. We assessed the effect of the use of contrast‐enhanced agent on the dose distributions in volumetric modulated arc therapy (VMAT) planning for head and neck cancer (HNC). Based on the VMIs at 40 keV (VMI_40keV), 60 keV(VMI_60keV), and 77 keV (VMI_77keV) of a tissue characterization phantom, lookup tables (LUTs) were created. VMAT plans were generated for 15 HNC patients based on contrast‐enhanced‐ (CE‐) VMIs at 40‐, 60‐, and 77 keV using the corresponding LUTs, and the doses were recalculated based on the noncontrast‐enhanced‐ (nCE‐) VMIs. For all structures, the difference in CT numbers owing to the contrast‐enhanced agent was prominent as the energy level of the VMI decreased, and the mean differences in CT number between CE‐ and nCE‐VMI was the largest for the clinical target volume (CTV) (125.3, 55.9, and 33.1 HU for VMI_40keV, VMI_60keV, and VMI_77keV, respectively). The mean difference of the dosimetric parameters (D_99%, D_50%, D_1%, D_mean, and D_0.1cc) for CTV and OARs was <1% in the treatment plans based on all VMIs. The maximum difference was observed for CTV in VMI_40keV (2.4%), VMI_60keV (1.9%), and VMI_77keV (1.5%) plans. The effect of the contrast‐enhanced agent was larger in the VMAT plans based on the VMI at a lower energy level for HNC patients. This effect is not desirable in a treatment planning procedure.

Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

Purpose

Virtual monoenergetic images (VMIs) derived from dual‐energy computed tomography (DECT) have been explored for several clinical applications in recent years. However, VMIs at low and high keVs have high levels of noise. The aim of this study was to reduce image noise in VMIs by using a two‐step noise reduction technique.

Methods

VMI was first denoised using a modified highly constrained backprojection (HYPR) method. After the first‐step denoising, a general‐threshold filtering method was performed. Two sets of anthropomorphic phantoms were scanned with a clinical dual‐source DECT system. DECT data (80/140Sn kV) were reconstructed as VMI series at 12 different energy levels (range, 40‐150 keV, interval, 10 keV). For comparison, the averaged VMIs obtained from 10 repeated DECT scans were used as the reference standard. The signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR) and root‐mean‐square error (RMSE) were used to evaluate the quality of VMIs.

Results

Compared to the original HYPR method, the proposed two‐step image denoising method could provide better performance in terms of SNR, CNR, and RMSE. In addition, the proposed method could achieve effective noise reduction while preserving edges and small structures, especially for low‐keV VMIs.

Conclusion

The proposed two‐step image denoising method is a feasible method for reducing noise in VMIs obtained from a clinical DECT scanner. The proposed method can also reduce edge blurring and the loss of intensity in small lesions.

Effect of a saline flush technique for head and neck imaging in dual-energy CT: improvement of image quality and perivenous artefact reduction using virtual monochromatic imaging

AIM

To evaluate the effect of the saline flush (SF) technique on the depiction of lesions and the reduction of perivenous artefacts in the head and neck region using dual-energy computed tomography (CT) with virtual monochromatic imaging (VMI).

MATERIALS AND METHODS

Fifty patients with head and neck cancer were divided into two groups: group A, without a SF and group B, with a 30-ml SF. All images were acquired using fast kilovolt-switching CT (Revolution HD, GE Healthcare, Milwaukee, WI, USA). Contrast-to-noise ratios (CNRs) of the lesions were calculated at VMI energy levels ranging from 40 to 80 keV. Subjective analysis of overall image quality, delineation of lesions, and perivenous artefacts was conducted by two reviewers at both VMI energy level 40 keV and the optimal energy level (which showed optimal CNR by objective analysis).

RESULTS

Optimal energy level was 63 keV for group A and 61 keV for group B. At VMI energy levels ranging from 40 to 80 keV, the CNR was higher for group B. The highest subjective overall image quality was shown for group B at the optimal energy level (subjective image quality mean value, 3.40). Subjective delineation of lesions was comparable. The perivenous artefact score was significantly higher for group B (2.44 versus 2.74 [p<0.05] at 40 keV, 3.20 versus 3.46 [p<0.05] at the optimal energy level).

CONCLUSION

The SF technique results in an improvement of lesion CNR and a reduction of perivenous artefacts in VMI using duel-energy CT, especially at 40 keV.

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.

Third generation dual-energy CT with 80/150 Sn kV for head and neck tumor imaging

BACKGROUND

Dual-energy CT (DECT) provides additional image datasets which enable improved tumor delineation or reduction of beam hardening artifacts in patients with head and neck squamous cell carcinoma (SCC).

PURPOSE

To assess radiation dose and image quality of third-generation DECT of the head and neck in comparison to single-energy CT (SECT).

MATERIAL AND METHODS

Thirty patients with SCC who underwent both SECT (reference tube voltage 120 kVp) and DECT (80/150 Sn kVp) of the head and neck region for staging were retrospectively selected. Attenuation measurements of the sternomastoid muscle, internal jugular vein, submandibular gland and tongue were compared. Image noise was assessed at five anatomic levels. Subjective image quality was evaluated by two radiologists in consensus.

RESULTS

CTDI_vol was 55% lower with DECT (4.2 vs. 9.3 mGy; P = 0.002). Median image noise was equal or lower in DECT at all levels (nasopharynx: 3.9 vs. 5.8, P < 0.0001; floor of mouth: 3.6 vs. 4.5, P = 0.0002; arytenoids: 3.6 vs. 3.1, P = 0.096; lower thyroid: 4.4 vs. 5.7, P = 0.002; arch of aorta: 5.6 vs. 6.5, P = 0.001). Attenuation was significantly lower in DECT ( P < 0.05). Subjective image analysis revealed that DECT is equal or superior to SECT with regard to overall image quality (nasopharynx: 5 vs. 5, P = 1; floor of mouth: 5 vs. 5, P = 0.0041; arytenoids: 5 vs. 5, P = 0.6; lower thyroid: 5 vs. 3, P < 0.0001; arch of aorta: 5 vs. 4, P < 0.0001).

CONCLUSION

Head and neck imaging with third-generation DECT can reduce radiation dose by half compared to SECT, while maintaining excellent image quality.

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.

Dual energy computed tomography virtual monoenergetic imaging: technique and clinical applications

Dual energy CT (DECT) has evolved into a commonly applied imaging technique in clinical routine due to its unique post-processing opportunities for improved evaluation of all body areas. Reconstruction of virtual monoenergetic imaging (VMI) series has shown beneficial effects for both non-contrast and contrast-enhanced DECT due to the flexibility to calculate low-keV VMI reconstructions to increase contrast and iodine attenuation, or to compute high-keV VMI reconstructions to reduce beam-hardening artefacts. The goal of this review article is to explain the technical background of VMI and noise-optimized VMI+ algorithms and to give an overview of useful clinical applications of the VMI technique in DECT of various body regions.

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset. However, so far no studies investigated the performance of these MA or DL methods on dual-energy CT (DECT) datasets, which have been shown to improve the image quality compared to conventional 120 kVp single-energy CT. In this study, the performance of an in-house developed MA and a DL method (two-step three-dimensional U-net) was quantitatively and qualitatively evaluated on various DECT-derived pseudo-monoenergetic CT datasets ranging from 40 keV to 170 keV. At lower energies, the MA method resulted in more accurate OAR segmentations. Both the qualitative and quantitative metric analysis showed that the DL approach often performed better than the MA method.

Optimal virtual monoenergetic image in "TwinBeam" dual-energy CT for organs-at-risk delineation based on contrast-noise-ratio in head-and-neck radiotherapy

PURPOSE

Dual-energy computed tomography (DECT) using TwinBeam CT (TBCT) is a new option for radiation oncology simulators. TBCT scanning provides virtual monoenergetic images which are attractive in treatment planning since lower energies offer better contrast for soft tissues, and higher energies reduce noise. A protocol is needed to achieve optimal performance of this feature. In this study, we investigated the TBCT scan schema with the head-and-neck radiotherapy workflow at our clinic and selected the optimal energy with best contrast-noise-ratio (CNR) in organs-at-risks (OARs) delineation for head-and-neck treatment planning.

METHODS AND MATERIALS

We synthesized monochromatic images from 40 keV to 190 keV at 5 keV increments from data acquired by TBCT. We collected the Hounsfield unit (HU) numbers of OARs (brainstem, mandible, spinal cord, and parotid glands), the HU numbers of marginal regions outside OARs, and the noise levels for each monochromatic image. We then calculated the CNR for the different OARs at each energy level to generate a serial of spectral curves for each OAR. Based on these spectral curves of CNR, the mono-energy corresponding to the max CNR was identified for each OAR of each patient.

RESULTS

Computed tomography scans of ten patients by TBCT were used to test the optimal monoenergetic image for the CNR of OAR. Based on the maximized CNR, the optimal energy values were 78.5 ± 5.3 keV for the brainstem, 78.0 ± 4.2 keV for the mandible, 78.5 ± 5.7 keV for the parotid glands, and 78.5 ± 5.3 keV for the spinal cord. Overall, the optimal energy for the maximum CNR of these OARs in head-and-neck cancer patients was 80 keV.

CONCLUSION

We have proposed a clinically feasible protocol that selects the optimal energy level of the virtual monoenergetic image in TBCT for OAR delineation based on the CNR in head-and-neck OAR. This protocol can be applied in TBCT simulation.

Unenhanced Dual-Energy Computed Tomography: Visualization of Brain Edema

PURPOSE

The aim of this study was to determine whether dual-energy computed tomography (DECT) imaging is superior to conventional noncontrast computed tomography (CT) imaging for the detection of acute ischemic stroke.

MATERIALS AND METHODS

This was a retrospective, single-center study of 40 patients who presented to the emergency department (ED) of a major, acute care, teaching center with signs and symptoms of acute stroke. Only those patients who presented to the ED within 4 hours of symptom onset were included in this study. All 40 patients received a noncontrast DECT of the head at the time of presentation. Each patient also received standard noncontrast CT of the head 24 hours after their initial presentation to the ED. "Brain edema" images were then reconstructed using 3-material decomposition with parameters adjusted to suppress gray/white matter contrast while preserving edema and increasing its conspicuity. The initial unenhanced, mixed images, brain edema, and 24-hour follow-up true noncontrast (TNC) images were reviewed and assigned Alberta Stroke Program Early CT scores. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

RESULTS

Of the 40 patients, 28 (70%) were diagnosed with an acute infarction. Brain edema reconstructions were better able to predict end infarction volume, with Alberta Stroke Program Early CT scores similar to the 24-hour follow-up TNC CT (7.75 vs 7.7; P > 0.05), whereas the mixed images routinely underestimated the extent of infarction (8.975 vs 7.7; P < 0.001). Initial TNC images had a sensitivity, specificity, PPV, and NPV of 80% (95% confidence interval [CI], 51.9%-95.7%), 72.7% (95% CI, 39%-94%), 80% (95% CI, 51.9%-95.7%), and 72.73% (95% CI, 51.91%-95.67%), respectively. The DECT brain edema images provided a sensitivity, specificity, PPV, and NPV of 93.33% (95% CI, 68.05%-99.83%), 100% (95% CI, 71.51%-100%), 100% (95% CI, 76.84%-100%), and 91.67% (95% CI, 61.52%-99.79%), respectively. There was very good interrater reliability across all 3 imaging techniques.

CONCLUSION

Brain edema reconstructions are able to more accurately detect edema and end-infarct volume as compared with initial TNC images. This provides a better assessment of the degree and extent of infarction and may serve to better guide therapy in the future.

Comparison of dual- and single-source dual-energy CT in head and neck imaging

OBJECTIVES

The aim of this study was to compare image quality of single-source dual-energy CT (SS-DECT) with third-generation dual-source dual-energy CT (DS-DECT) in head and neck cancer.

MATERIALS AND METHODS

One hundred two patients with histologically proven head and neck cancer were prospectively randomized to undergo radiation dose-matched SS-DECT (n = 51, 120 kV, split-filter technique, 384 ref. mAs) or DS-DECT (n = 51, 80/Sn150 kV, tube A 100/tube B 67 ref. mAs). Inline default images (DI) and virtual monoenergetic images (VMI) for two different low energies (40 and 60 keV) were reconstructed. Objective image quality was evaluated as dose-normalized contrast to noise ratio (CNRD), and subjective image quality was rated on a 5-point Likert scale.

RESULTS

In both groups, highest CNRD values for vessel and tumor attenuation were obtained at 40 keV. DS-DECT was significantly better than SS-DECT regarding vessel and tumor attenuation. Overall subjective image quality in the SS-DECT group was highest on the DI followed by 40 keV and 60 keV. In the DS-DECT group, subjective image quality was highest at 40 keV followed by 60 keV and the DI. Forty kiloelectron volts and 60 keV were significantly better in the DS-DECT compared to the SS-DECT group (both p < 0.01).

CONCLUSIONS

In split-filter SS-DECT as well as in DS-DECT, highest overall image quality in head and neck imaging can be obtained with a combination of DI and low keV reconstructions. DS-DECT is superior to split-filter SS-DECT in terms of subjective image quality and vessel and tumor attenuation.

KEY POINTS

• Image quality was diagnostic with both dual-energy techniques; however, the dual-source technique delivered significantly better results. • Highest overall image quality in head and neck imaging can be obtained with a combination of default images and low keV reconstructions with both dual-energy techniques. • The results of this study may have relevance for the decision-making process regarding replacement of CT scanners and focused patient examination considering image quality and subsequent therapeutic decision-making.

Metal Artifact Reduction in Virtual Monoenergetic Spectral Dual-Energy CT of Patients With Metallic Orthopedic Implants in the Distal Radius

OBJECTIVE

The purpose of this study was to evaluate the image quality of virtual monoenergetic images obtained from dual-layer-detector spectral CT of patients with metallic orthopedic implants of the distal radius.

MATERIALS AND METHODS

A retrospective analysis was performed between April 2016 and January 2017. Forty-three consecutively registered patients (33 women, 10 men; mean age, 50.7 ± 15.4 years) with metallic implants for distal radius fractures underwent dual-layer-detector spectral CT. Sixteen virtual monoenergetic image sets ranging from 50 to 200 keV were generated from the single slice with the most pronounced low-attenuation artifact from implants. Image quality was quantitatively assessed on the basis of the attenuation of the artifacts and reference tissue, background image noise, and artifact index. Qualitative assessment included degree of artifact, diagnostic image quality of the periimplant bones, and delineation of fracture lines. The Friedman rank sum test and kappa analysis were used for statistical analysis.

RESULTS

There were statistically significant differences in quantitative and qualitative parameters at different monoenergy levels (all p < 0.001). Artifact index was the lowest at 120 keV. Low-attenuation artifacts in the periimplant regions were least pronounced at 110 keV, and the diagnostic image quality of periimplant bone was best at 130 keV. Fracture lines were well delineated in all cases at 80-110 keV (p < 0.001).

CONCLUSION

The optimal energy setting for incurring the fewest metallic artifacts and obtaining the best diagnostic image quality from distal radius implants during dual-layer-detector spectral CT is the range of 110-130 keV.

Technical Note: Enhancing soft tissue contrast and radiation-induced image changes with dual-energy CT for radiation therapy

PURPOSE

The purpose of this work is to investigate the use of low-energy monoenergetic decompositions obtained from dual-energy CT (DECT) to enhance image contrast and the detection of radiation-induced changes of CT textures in pancreatic cancer.

METHODS

The DECT data acquired for 10 consecutive pancreatic cancer patients during routine nongated CT-guided radiation therapy (RT) using an in-room CT (Definition AS Open, Siemens Healthcare, Malvern, PA) were analyzed. With a sequential DE protocol, the scanner rapidly performs two helical acquisitions, the first at a tube voltage of 80 kVp and the second at a tube voltage of 140 kVp. Virtual monoenergetic images across a range of energies from 40 to 140 keV were reconstructed using an image-based material decomposition. Intravenous (IV) bolus-free contrast enhancement in pancreas patient tumors was measured across a spectrum of monoenergies. For treatment response assessment, the changes in CT histogram features (including mean CT number (MCTN), entropy, kurtosis) in pancreas tumors were measured during treatment. The results from the monoenergetic decompositions were compared to those obtained from the standard 120 kVp CT protocol for the same subjects.

RESULTS

Data of monoenergetic decompositions of the 10 patients confirmed the expected enhancement of soft tissue contrast as the energy is decreased. The changes in the selected CT histogram features in the pancreas during RT delivery were amplified with the low-energy monoenergetic decompositions, as compared to the changes measured from the 120 kVp CTs. For the patients studied, the average reduction in the MCTN in pancreas from the first to the last (the 28th) treatment fraction was 4.09 HU for the standard 120 kVp and 11.15 HU for the 40 keV monoenergetic decomposition.

CONCLUSIONS

Low-energy monoenergetic decompositions from DECT substantially increase soft tissue contrast and increase the magnitude of radiation-induced changes in CT histogram textures during RT delivery for pancreatic cancer. Therefore, quantitative DECT may assist the detection of early RT response.