On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT)

Electron density dual-energy CT can improve the detection of lumbar disc herniation with higher image quality than standard and virtual non-calcium images

OBJECTIVES

To compare the diagnostic performance and image quality of dual-energy computed tomography (DECT) with electron density (ED) image reconstruction with those of DECT with standard CT (SC) and virtual non-calcium (VNCa) image reconstructions, for diagnosing lumbar disc herniation (L-HIVD).

METHODS

A total of 59 patients (354 intervertebral discs from T12/L1 to L5/S1; mean age, 60 years; 30 women and 29 men) who underwent DECT with spectral reconstruction and 3-T MRI within 2 weeks were enrolled between March 2021 and February 2022. Four radiologists independently assessed three image sets of randomized ED, SC, and VNCa images to detect L-HIVD at 8-week intervals. The coefficient of variance (CV) and the Weber contrast of the ROIs in the normal and diseased disc to cerebrospinal fluid space (NCR-normal/-diseased, respectively) were calculated to compare the image qualities of the noiseless ED and other series.

RESULTS

Overall, 129 L-HIVDs were noted on MRI. In the detection of L-HIVD, ED showed a higher AUC and sensitivity than SC and VNCa; 0.871 vs 0.807 vs 833 (p = 0.002) and 81% vs 70% vs 74% (p = 0.006 for SC), respectively. CV was much lower in all measurements of ED than those for SC and VNCa (p < 0.001). Furthermore, NCR-normal and NCR-diseased were the highest in ED (ED vs SC in NCR-normal and NCR-diseased, p =  0.001 and p = 0.004, respectively; ED vs VNCa in NCR-diseased, p = 0.044).

CONCLUSION

Compared to SC and VNCa images, DECT with ED reconstruction can enhance the AUC and sensitivity of L-HIVD detection with a lower CV and higher NCR.

CLINICAL RELEVANCE STATEMENT

To our knowledge, this is the first study to quantify the image quality of noiseless ED images. ED imaging may be helpful for detecting L-HIVD in patients who cannot undergo MRI.

KEY POINTS

ED images have diagnostic potential, but relevant quantitative analyses of image quality are limited. ED images detect disc herniation, with a better coefficient of variance and normalized contrast ratio values. ED images could detect L-HIVD when MRI is not an option.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Noncontrast Myocardial Characterization in Acute Myocardial Infarction Using Electron Density Imaging

PURPOSE

Spectral computed tomography (CT) enables improved tissue characterization, although virtually all research has focused on contrast-enhanced examinations. We hypothesized that changes in myocardial tissue related to acute myocardial infarction (AMI) might potentially be identified without the need for contrast administration using electron density (ED) imaging.

PATIENTS AND METHODS

This retrospective observational study involved a small series (n = 15) of patients admitted to our institution with a first AMI without signs of hemodynamic instability and identification of a culprit vessel with invasive coronary angiography during the same admission, who also underwent a noncontrast, low-dose chest CT using a dual-layer spectral CT scanner. Images were assessed in search of dark areas with low density on ED imaging, and the mean percentage ED relative to water (%EDW) was calculated.

RESULTS

Using a qualitative approach, ED assessment enabled the identification of 11/15 (73%) affected coronary territories, with a sensitivity of 73% (95% CI: 45; 92%) and a specificity of 87% (95% CI: 69; 96%). AMI segments showed significantly lower ED values than the remote myocardium (103.8 ± 0.8 vs 104.3 ± 0.6 %EDW, P < 0.0001), and a threshold below 103.9 %EDW had a sensitivity of 66% and specificity of 79% for the identification of AMI. In a control group of patients without a history of cardiovascular disease, none had areas with focal reduction of ED following the shape of the myocardial wall.

CONCLUSIONS

In our preliminary series, ED imaging showed the potential to enable the identification of myocardial tissue changes related to AMI without iodinated contrast requirement.

Optimizing 3D-printed workpieces for radiotherapy application: modelling the CT number and print time

This study aims to analyze the influence of specific printing parameters, including infilling, print speed, and layer height, on the CT numbers and printing time of 3D-printed workpieces fabricated from Polylactic Acid (PLA). The primary objective is to optimize these parameters to attain desired CT numbers and print time for radiotherapy applications. To achieve this objective, we employed the Taguchi experimental design and regression analysis methodologies. A series of experiments were conducted to systematically assess the effects of varying infilling, print speed, and layer height values on the CT numbers and printing time of the PLA workpieces. The resulting data were then used to create mathematical models for predicting optimal parameter settings. Our investigations revealed that specific adjustments to infilling and layer height significantly influence the CT numbers and printing time of 3D-printed workpieces. By leveraging the developed mathematical models, precise predictions can be made to optimize independent parameters for the desired CT numbers and printing times, enhancing the efficacy of 3D-printed workpieces for radiotherapy applications. This research contributes to the advancement of 3D-printed workpieces utilized in radiotherapy, offering a pathway to enhance the accuracy and efficiency of treatment delivery. The optimization of printing parameters outlined in this study provides a valuable tool for clinicians and researchers in the field, ultimately benefiting patients undergoing radiotherapy treatments.

Dosimetric significance of manual density overrides in oropharyngeal cancer

Kilovoltage computed tomography plays a crucial role in radiotherapy planning. However, the presence of high-density metallic objects can introduce streaking artifacts in CT scans, resulting in inaccurate dose calculations by the treatment planning software. Previous studies have explored manual density overrides and artifact reduction algorithms individually to enhance dose calculation accuracy, but their combined application on patient plans within a treatment planning system remains unexplored. This research aims to assess the necessity of manual density overrides when an artifact reduction algorithm is already employed to address dental artifacts in oropharyngeal cancer treatment plans. A total of 20 previously treated volumetric modulated arc therapy plans were collected, and manual density overrides were removed followed by plan recalculation. Dosimetric parameters were then compared between the original and modified plans. Statistical analysis revealed several dosimetric parameters for the planning target volume (PTV), clinical target volume (CTV), and oral cavity that exhibited statistically significant differences upon removing the manual density override. However, these differences were found to be small in absolute terms. No other organs evaluated demonstrated statistically significant differences in dose. The most significant disparity observed was an 8.26 cGy increase in mean dose to the CTV, which represents only 0.12% of the prescription dose. Based on these findings, it can be concluded that manual density overrides are likely unnecessary when an artifact reduction algorithm is employed in oropharyngeal cancer cases.

Hounsfield Units as an Independent Predictor of Failed Percutaneous Drainage of Spinal Tuberculosis Paraspinal Abscess Under Computed Tomography Guidance

OBJECTIVE

To investigate the value of Hounsfield units (HUs) as an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under computed tomography (CT) guidance.

METHODS

A retrospective analysis was conducted on 61 patients who underwent CT-guided percutaneous drainage for spinal tuberculosis paraspinal abscess between October 2017 and October 2020. Preoperative CT scans were used to measure the HUs of the abscess. Patients were categorized into successful drainage (n = 49) and failed drainage (n = 12) groups. Statistical analysis involved independent sample t-tests and chi-square tests to compare between the 2 groups. Binary logistic regression was performed to identify independent predictive factors for drainage failure. Receiver operating characteristic (ROC) curves were employed to ascertain risk factor thresholds and diagnostic performance.

RESULTS

Among the patients, 49 experienced successful drainage while 12 faced drainage failure. The mean HUs of abscesses in the failed drainage group were significantly higher than those in the successful drainage group (p < 0.001). ROC analysis revealed an area under the curve of 0.897 (95% confidence interval, 0.808-0.986) for predicting drainage failure based on HUs. The optimal HU cutoff value for predicting drainage failure was 22.3, with a sensitivity of 91.7% and specificity of 69.4%.

CONCLUSION

HUs are an independent predictor of failed percutaneous drainage of spinal tuberculosis paraspinal abscess under CT guidance. The HU value of 22.3 can be used as an initial screening threshold for predicting the success or failure of drainage.

A novel CT-responsive hydrogel for the construction of an organ simulation phantom for the repeatability and stability study of radiomic features

Radiomic features have demonstrated reliable outcomes in tumor grading and detecting precancerous lesions in medical imaging analysis. However, the repeatability and stability of these features have faced criticism. In this study, we aim to enhance the repeatability and stability of radiomic features by introducing a novel CT-responsive hydrogel material. The newly developed CT-responsive hydrogel, mineralized by in situ metal ions, exhibits exceptional repeatability, stability, and uniformity. Moreover, by adjusting the concentration of metal ions, it achieves remarkable CT similarity comparable to that of human organs on CT scans. To create a phantom, the hydrogel was molded into a universal model, displaying controllable CT values ranging from 53 HU to 58 HU, akin to human liver tissue. Subsequently, 1218 radiomic features were extracted from the CT-responsive hydrogel organ simulation phantom. Impressively, 85-97.2% of the extracted features exhibited good repeatability and stability during coefficient of variability analysis. This finding emphasizes the potential of CT-responsive hydrogel in consistently extracting the same features, providing a novel approach to address the issue of repeatability in radiomic features.

HU-based material conversion for BNCT accurate dose estimation

NeuMANTA is a new generation boron neutron capture therapy (BNCT)-specific treatment planning system developed by the Neuboron Medical Group and upgraded to an important feature, a Hounsfield unit (HU)-based material conversion algorithm. The range of HU values was refined to 96 specific groups and established corresponding to tissue information. The elemental compositions and mass densities have an important effect on the calculated dose distribution. The region of interest defined in the treatment plan can be converted into multiple material compositions based on HU values or assigned specified single material composition in NeuMANTA. Different material compositions may cause normal tissue maximum dose rates to differ by more than 10% in biologically equivalent doses and to differ by up to 6% in physically absorbed doses. Although the tumor has a lower proportion of BNCT background dose, the material composition difference may affect the minimum dose of biologically equivalent dose and physically absorbed dose by more than 3%. In addition, the difference in material composition could lead to a change in neutron moderation as well as scattering. Therefore, the material composition has a significant impact on the assessment of normal tissue side effects and tumor control probability. It is essential for accurate dose estimation in BNCT.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using ex vivo animal tissues and tissue-mimicking phantoms

Objective. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (Im), electron density, and stopping power ratio (SPR). We aimed to develop a novel MR-based multimodal method to accurately determine SPR and molecular compositions. This method was evaluated in tissue-mimicking andex vivoporcine phantoms, and in a brain radiotherapy patient.Approach. Four tissue-mimicking phantoms with known compositions, two porcine tissue phantoms, and a brain cancer patient were imaged with kVCT and MRI. Three imaging-based values were determined: SPRCM(CT-based Multimodal), SPRMM(MR-based Multimodal), and SPRstoich(stoichiometric calibration). MRI was used to determine two tissue-specific quantities of the Bethe Bloch equation (Im, electron density) to compute SPRCMand SPRMM. Imaging-based SPRs were compared to measurements for phantoms in a proton beam using a multilayer ionization chamber (SPRMLIC).Main results. Root mean square errors relative to SPRMLICwere 0.0104(0.86%), 0.0046(0.45%), and 0.0142(1.31%) for SPRCM, SPRMM, and SPRstoich, respectively. The largest errors were in bony phantoms, while soft tissue and porcine tissue phantoms had <1% errors across all SPR values. Relative to known physical molecular compositions, imaging-determined compositions differed by approximately ≤10%. In the brain case, the largest differences between SPRstoichand SPRMMwere in bone and high lipids/fat tissue. The magnitudes and trends of these differences matched phantom results.Significance. Our MR-based multimodal method determined molecular compositions and SPR in various tissue-mimicking phantoms with high accuracy, as confirmed with proton beam measurements. This method also revealed significant SPR differences compared to stoichiometric kVCT-only calculation in a clinical case, with the largest differences in bone. These findings support that including MRI in proton therapy treatment planning can improve the accuracy of calculated SPR values and reduce range uncertainties.

Optimization technique for increasing resolution in computed tomography imaging

Starting from the importance of conforming to biological reality in medicine, in this paper we propose an optimization technique for increasing resolution of computed tomography (CT) images acquired using various existing scanners. Considering a three-dimensional Hounsfield Units (HU) array, together with the corresponding spatial metadata of interest (pixel sizes and slice thickness), the procedure is based on halving each voxel along the directions of the device's Cartesian frame of reference and find those values which are both satisfying the X-Rays attenuation coefficient average requirement and minimizing the HU distance to classical interpolation points. The discussed method was tested by implementing a C# .Net 6, cross-platform library containing two algorithm flavors that could be independently applied: "Z" for doubling the number of slices, and "XY" for doubling the resolution of individual slices. This design allows also chaining (e.g. one could apply the "Z,XY,Z" sequence in order to reduce four times slice thickness). In the context of existing unavoidable limitations, the first results are suggesting the "CT compatible" interpolation technique could provide a reasonable approximation of reality. However, the main advantage comes from satisfying mass conservation, which is of high importance in medical diagnosis and treatment.•The Hounsfield Units scale is defined as a linear transformation of the X-Rays attenuation coefficients. Thus, splitting a computed tomography voxel into two congruent volumes must satisfy the HU average requirement (the initial value must equal the average of the two output HU values).•Existing interpolation methods (linear, spline, etc.) are not compatible with the computed tomography HU average requirement. This could also result in mass estimate anomalies with significant impact in medical diagnosis.•The proposed "CT compatible" interpolation method is based on finding those values which are both satisfying the X-Rays attenuation coefficient average requirement and minimizing the Hounsfield Units distance to classical interpolation points.

Clinical use of positron emission tomography for radiotherapy planning - Medical physics considerations

PET/CT imaging plays an increasing role in radiotherapy treatment planning. The aim of this article was to identify the major use cases and technical as well as medical physics challenges during integration of these data into treatment planning. Dedicated aspects, such as (i) PET/CT-based radiotherapy simulation, (ii) PET-based target volume delineation, (iii) functional avoidance to optimized organ-at-risk sparing and (iv) functionally adapted individualized radiotherapy are discussed in this article. Furthermore, medical physics aspects to be taken into account are summarized and presented in form of check-lists.

Usefulness of Cervical Computed Tomography Hounsfield Units to Differentiate Kawasaki Disease From a Deep-neck Abscess

We measured the Hounsfield units (HUs) value of cervical plain computed tomography images to differentiate between Kawasaki disease (KD) and a deep-neck abscess (DNA). The HUs value was significantly lower in KD than in DNA, making it a useful marker for differentiating between these 2 diseases.

Entrance Skin Dose (ESD) and Bucky Table Induced Backscattered Dose (BTI-BSD) in Abdominal Radiography With nanoDot Optically Stimulated Luminescence Dosimeter (OSLD)

In radiography, inconsistencies in patients' measured entrance skin dose (ESD) exist. There is no published research on the bucky table induced backscattered radiation dose (BTI-BSD). Thus, we aimed to ascertain ESD, calculate the BTI-BSD in abdominal radiography with a nanoDot OSLD, and compare the ESD results with the published data. A Kyoto Kagaku PBU-50 phantom (Kyoto, Japan) in an antero-posterior supine position was exposed, selecting a protocol used for abdominal radiography. The central ray of x-ray beam was pointed at the surface of abdomen at the navel, where a nanoDot dosimeter was placed to measure ESD. For the BTI-BSD, exit dose (ED) was determined by placing a second dosimeter on the exact opposite side (backside) of the phantom from the dosimeter used to determine (ESD) with and without bucky table at identical exposure parameters. The BTI-BSD was calculated as the difference between ED with and without bucky table. The ESD, ED, and BTI-BSD were measured in milligray (mGy). ESD mean values with and without bucky table were 1.97 mGy and 1.84 mGy, whereas ED values were 0.062 mGy and 0.052 mGy, respectively. Results show 2-26% lower ESD values with nanoDot OSLD. The BTI-BSD mean value was found to be approximately 0.01 mGy. A local dose reference level (LDRL) can be established using ESD data to safeguard patients from unnecessary radiation. In addition, to minimize the risk of BTI-BSD in patients in radiography, the search for the use or fabrication of a new, lower atomic number material for the bucky table is suggested.

The automated measurement of CT number linearity using an ACR accreditation phantom

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R², were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R²> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR²= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR²< 0.99.

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network

BACKGROUND

Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation.

PURPOSE

In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning.

METHODS

MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs).

RESULTS

MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively.

CONCLUSIONS

MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

Image-guided intratumoral immunotherapy: Developing a clinically practical technology

Immunotherapy has revolutionized the contemporary oncology landscape, with durable responses possible across a range of cancer types. However, the majority of cancer patients do not respond to immunotherapy due to numerous immunosuppressive barriers. Efforts to overcome these barriers and increase systemic immunotherapy efficacy have sparked interest in the local intratumoral delivery of immune stimulants to activate the local immune response and subsequently drive systemic tumor immunity. While clinical evaluation of many therapeutic candidates is ongoing, development is hindered by a lack of imaging confirmation of local delivery, insufficient intratumoral drug distribution, and a need for repeated injections. The use of polymeric drug delivery systems, which have been widely used as platforms for both image guidance and controlled drug release, holds promise for delivery of intratumoral immunoadjuvants and the development of an in situ cancer vaccine for patients with metastatic cancer. In this review, we explore the current state of the field for intratumoral delivery and methods for optimizing controlled drug release, as well as practical considerations for drug delivery design to be optimized for clinical image guided delivery particularly by CT and ultrasound.

Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning

Objective. Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio. For the latter, kVCT may have greater uncertainty in determining relative electron density ( ρ e ) and proton stopping power ratio (SPR). Conversely, megavoltage CT (MVCT) may result in superior dose calculation accuracy. The purpose of this work was to convert kVCT HU to MVCT HU using deep learning to obtain higher accuracy ρ e and SPR. Approach. Tissue-mimicking phantoms were created to compare kVCT- and MVCT-determined ρ e and SPR to physical measurements. Using 100 head-and-neck datasets, an unpaired deep learning model was trained to learn the relationship between kVCTs and MVCTs, creating synthetic MVCTs (sMVCTs). Similarity metrics were calculated between kVCTs, sMVCTs, and MVCTs in 20 test datasets. An anthropomorphic head phantom containing bone-mimicking material with known composition was scanned to provide an independent determination of ρ e and SPR accuracy by sMVCT. Main results. In tissue-mimicking bone, ρ e errors were 2.20% versus 0.19% and SPR errors were 4.38% versus 0.22%, for kVCT versus MVCT, respectively. Compared to MVCT, in vivo mean difference (MD) values were 11 and 327 HU for kVCT and 2 and 3 HU for sMVCT in soft tissue and bone, respectively. ρ e MD decreased from 1.3% to 0.35% in soft tissue and 2.9% to 0.13% in bone, for kVCT and sMVCT, respectively. SPR MD decreased from 1.8% to 0.24% in soft tissue and 6.8% to 0.16% in bone, for kVCT and sMVCT, respectively. Relative to physical measurements, ρ e and SPR error in anthropomorphic bone decreased from 7.50% and 7.48% for kVCT to <1% for both MVCT and sMVCT. Significance. Deep learning can be used to map kVCT to sMVCT, suggesting higher accuracy ρ e and SPR is achievable with sMVCT versus kVCT.

Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting

The diagnostic efficacy of coronary computed tomography angiography (CTA) images of coronary arteries in restenosis after coronary stenting based on the combination of the convolutional neural network (CNN) algorithm and the automatic segmentation algorithm for region growth of vascular similarity features was explored to provide a more effective diagnostic method for patients. 130 patients with coronary artery disease were randomly selected as the research objects, and they were averagely classified into the control group (conventional coronary CTA image diagnosis) and the observation group (coronary CTA image diagnosis based on an improved automatic segmentation algorithm). Based on the diagnostic criteria of coronary angiography (CAG), the efficacy of two kinds of coronary CTA images on the postoperative subsequent visit of coronary heart disease (CHD) stenting was evaluated. The results showed that the accuracy of the CNN algorithm was 87.89%, and the average voxel error of the improved algorithm was signally lower than that of the traditional algorithm (1.8921 HU/voxel vs. 7.10091 HU/voxel) (p < 0.05). The average score of the coronary CTA image in the observation group was higher than that in the control group (2.89 ± 0.11 points vs. 2.01 ± 0.73 points) (p < 0.05). The diagnostic sensitivity (91.43%), specificity (86.76%), positive predictive value (88.89%), negative predictive value (89.66%), and accuracy (89.23%) of the observation group were higher than those of the control group (p < 0.05). In conclusion, the region growth algorithm under the CNN algorithm and vascular similarity features had an accurate segmentation effect, which was helpful for the diagnosis of CTA image in restenosis after coronary stenting.

Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings

Background: Accurate nodal staging is essential to guide treatment selection in patients with non-small cell lung cancer (NSCLC). To our knowledge, measurement of electron density (ED) using dual-energy CT (DECT) is unexplored for this purpose. Objective: To assess the utility of ED from DECT in diagnosing metastatic mediastinal lymph nodes in patients with NSCLC, in comparison with conventional CT and FDG PET/CT. Methods: This retrospective study included 57 patients (36 men, 21 women; mean age 68.4±8.9 years) with NSCLC and surgically resected mediastinal lymph nodes who underwent preoperative DECT and FDG PET/CT. The patients had a total of 117 resected mediastinal lymph nodes (33 metastatic, 84 nonmetastatic). Two radiologists independently reviewed nodes' morphologic features on the 120 kVp images and also measured nodes' iodine concentration (IC) and ED using maps generated from DECT data; consensus was reached for discrepancies. Two separate radiologists assessed FDG PET/CT examinations in consensus for positive node uptake. Diagnostic performance was evaluated for individual and pairwise combinations of features. Results: The sensitivity, specificity, and accuracy for nodal metastasis were 15.2%, 98.8%, and 75.2% for presence of necrosis; 54.5%, 85.7%, and 76.9% for short-axis diameter >8.5 mm; 63.6%, 73.8%, and 70.9% for long-axis diameter >13.0 mm; 51.5%, 79.8%, and 71.8% for attenuation on 120 kVp images ≤95.8 HU; 87.9%, 58.3%, and 66.7% for ED ≤3.48×1023/cm3; and 66.7%, 75.0%, and 72.6% for positive FDG uptake, respectively. Among pairwise combinations of features, accuracy was highest for the combination of ED and short-axis diameter (accuracy 82.9%, sensitivity 54.5%, specificity 94.0%) and the combination of ED and positive FDG uptake (accuracy 82.1%, sensitivity 60.6%, specificity 90.5%); these accuracies were greater than for the individual features (p<.05). Remaining combinations exhibited accuracies ranging from 74.4% to 77.8%. Interobserver agreement analysis demonstrated intraclass correlation coefficient of 0.90 for ED. IC was not significantly different between metastatic and nonmetastatic nodes (p=.18) and was excluded from the diagnostic performance analysis. Conclusion: ED derived from DECT may help diagnose metastatic lymph nodes in NSCLC given decreased ED in metastatic nodes. Clinical Impact: ED may complement conventional CT findings and FDG uptake on PET/CT in diagnosing metastatic nodes.