Research

13 pages, 3394 KiB

Open AccessArticle

Impact of Phantom Size on Low-Energy Virtual Monoenergetic Images of Three Dual-Energy CT Platforms

by Joël Greffier, Claire Van Ngoc Ty, Isabelle Fitton, Julien Frandon, Jean-Paul Beregi and Djamel Dabli

Diagnostics 2023, 13(19), 3039; https://doi.org/10.3390/diagnostics13193039 - 25 Sep 2023

Cited by 1 | Viewed by 664

Abstract

The purpose of this study was to compare the quality of low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according to the phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs [...] Read more.

The purpose of this study was to compare the quality of low-energy virtual monoenergetic images (VMIs) obtained with three Dual-Energy CT (DECT) platforms according to the phantom diameter. Three sections of the Mercury Phantom 4.0 were scanned on two generations of split-filter CTs (SFCT-1st and SFCT-2nd) and on one Dual-source CT (DSCT). The noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d’) were assessed on VMIs from 40 to 70 keV. The highest noise magnitude values were found with SFCT-1st and noise magnitude was higher with DSCT than with SFCT-2nd for 26 cm (10.2% ± 1.3%) and 31 cm (7.0% ± 2.5%), and the opposite for 36 cm (−4.2% ± 2.5%). The highest average NPS spatial frequencies and TTF values at 50% (f₅₀) values were found with DSCT. For all energy levels, the f₅₀ values were higher with SFCT-2nd than SFCT-1st for 26 cm (3.2% ± 0.4%) and the opposite for 31 cm (−6.9% ± 0.5%) and 36 cm (−5.6% ± 0.7%). The lowest d’ values were found with SFCT-1st. For all energy levels, the d’ values were lower with DSCT than with SFCT-2nd for 26 cm (−6.2% ± 0.7%), similar for 31 cm (−0.3% ± 1.9%) and higher for 36 cm (5.4% ± 2.7%). In conclusion, compared to SFCT-1st, SFCT-2nd exhibited a lower noise magnitude and higher detectability values. Compared with DSCT, SFCT-2nd had a lower noise magnitude and higher detectability for the 26 cm, but the opposite was true for the 36 cm. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

15 pages, 2474 KiB

Open AccessArticle

A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)

by Cassandre Garnier, Loïc Ferrer, Jennifer Vargas, Olivier Gallinato, Eva Jambon, Yann Le Bras, Jean-Christophe Bernhard, Thierry Colin, Nicolas Grenier and Clément Marcelin

Diagnostics 2023, 13(15), 2548; https://doi.org/10.3390/diagnostics13152548 - 31 Jul 2023

Cited by 4 | Viewed by 1225

Abstract

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for [...] Read more.

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. Materials and methods: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. Results: A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/− 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and “intensity mean value” was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Conclusion: Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

15 pages, 7719 KiB

Open AccessArticle

Optimal Abdominal CT Image Quality in Non-Lean Patients: Customization of CM Injection Protocols and Low-Energy Acquisitions

by Francesco Macri, Elina Khasanova, Bonnie T. Niu, Anushri Parakh, Manuel Patino, Avinash Kambadakone and Dushyant V. Sahani

Diagnostics 2023, 13(13), 2279; https://doi.org/10.3390/diagnostics13132279 - 05 Jul 2023

Cited by 1 | Viewed by 1295

Abstract

We compared the image quality of abdominopelvic single-energy CT with 100 kVp (SECT-100 kVp) and dual-energy CT with 65 keV (DECT-65 keV) obtained with customized injection protocols to standard abdominopelvic CT scans (SECT-120 kVp) with fixed volumes of contrast media (CM). We retrospectively [...] Read more.

We compared the image quality of abdominopelvic single-energy CT with 100 kVp (SECT-100 kVp) and dual-energy CT with 65 keV (DECT-65 keV) obtained with customized injection protocols to standard abdominopelvic CT scans (SECT-120 kVp) with fixed volumes of contrast media (CM). We retrospectively included 91 patients (mean age, 60.7 ± 15.8 years) with SECT-100 kVp and 83 (mean age, 60.3 ± 11.7 years) patients with DECT-65 keV in portovenous phase. Total body weight-based customized injection protocols were generated by a software using the following formula: patient weight (kg) × 0.40/contrast concentration (mgI/mL) × 1000. Patients had a prior abdominopelvic SECT-120 kVp with fixed injection. Iopamidol-370 was administered for all examinations. Quantitative and qualitative image quality comparisons were made between customized and fixed injection protocols. Compared to SECT-120 kVp, customized injection yielded a significant reduction in CM volume (mean difference = 9–12 mL; p ≤ 0.001) and injection rate (mean differences = 0.2–0.4 mL/s; p ≤ 0.001) in all weight categories. Improvements in attenuation, noise, signal-to-noise and contrast-to-noise ratios were observed for both SECT-100 kVp and DECT-65 keV compared to SECT-120 kVp in all weight categories (e.g., pancreas DECT-65 keV, 1.2-attenuation-fold increase vs. SECT-120 kVp; p < 0.001). Qualitative scores were ≥4 in 172 cases (98.8.4%) with customized injections and in all cases with fixed injections (100%). These findings suggest that customized CM injection protocols may substantially reduce iodine dose while yielding higher image quality in SECT-100 kVp and DECT-65 keV abdominopelvic scans compared to SECT-120 kVp using fixed CM volumes. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

12 pages, 1039 KiB

Open AccessArticle

Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance

by Carole Durot, Eric Durot, Sébastien Mulé, David Morland, François Godard, Anne Quinquenel, Alain Delmer, Philippe Soyer and Christine Hoeffel

Diagnostics 2023, 13(13), 2237; https://doi.org/10.3390/diagnostics13132237 - 30 Jun 2023

Viewed by 810

Abstract

The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next [...] Read more.

The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above −0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

12 pages, 2900 KiB

Open AccessArticle

Image-Based Numerical Analysis for Isolated Type II SLAP Lesions in Shoulder Abduction and External Rotation

by Javier A. Maldonado, Duvert A. Puentes, Ivan D. Quintero, Octavio A. González-Estrada and Diego F. Villegas

Diagnostics 2023, 13(10), 1819; https://doi.org/10.3390/diagnostics13101819 - 22 May 2023

Cited by 2 | Viewed by 1347

Abstract

The glenohumeral joint (GHJ) is one of the most critical structures in the shoulder complex. Lesions of the superior labral anterior to posterior (SLAP) cause instability at the joint. Isolated Type II of this lesion is the most common, and its treatment is [...] Read more.

The glenohumeral joint (GHJ) is one of the most critical structures in the shoulder complex. Lesions of the superior labral anterior to posterior (SLAP) cause instability at the joint. Isolated Type II of this lesion is the most common, and its treatment is still under debate. Therefore, this study aimed to determine the biomechanical behavior of soft tissues on the anterior bands of the glenohumeral joint with an Isolated Type II SLAP lesion. Segmentation tools were used to build a 3D model of the shoulder joint from CT-scan and MRI images. The healthy model was studied using finite element analysis. Validation was conducted with a numerical model using ANOVA, and no significant differences were shown (p = 0.47). Then, an Isolated Type II SLAP lesion was produced in the model, and the joint was subjected to 30 degrees of external rotation. A comparison was made for maximum principal strains in the healthy and the injured models. Results revealed that the strain distribution of the anterior bands of the synovial capsule is similar between a healthy and an injured shoulder (p = 0.17). These results demonstrated that GHJ does not significantly deform for an Isolated Type II SLAP lesion subjected to 30-degree external rotation in abduction. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

12 pages, 2867 KiB

Open AccessArticle

Image Quality Analysis of Photon-Counting CT Compared with Dual-Source CT: A Phantom Study for Chest CT Examinations

by Marine Deleu, Jean-Baptiste Maurice, Laura Devos, Martine Remy and François Dubus

Diagnostics 2023, 13(7), 1325; https://doi.org/10.3390/diagnostics13071325 - 03 Apr 2023

Cited by 3 | Viewed by 1730

Abstract

A comparison was made between the image quality of a photon-counting CT (PCCT) and a dual-source CT (DSCT). The evaluation of image quality was performed using a Catphan CT phantom, and the physical metrics, such as the noise power spectrum and task transfer [...] Read more.

A comparison was made between the image quality of a photon-counting CT (PCCT) and a dual-source CT (DSCT). The evaluation of image quality was performed using a Catphan CT phantom, and the physical metrics, such as the noise power spectrum and task transfer function, were measured for both PCCT and DSCT at three CT dose indices (1, 5 and 10 mGy). Polyenergetic and virtual monoenergetic reconstructions were used to evaluate the performance differences by simulating a Gaussian spot with a radius of 5 mm and calculating the detectability index. The highest iterative reconstruction level was able to decrease the noise by about 70% compared with the filtered back projection using a parenchyma reconstruction kernel. The PCCT task transfer functions remained constant, while those of the DSCT increased with the reconstruction strength level. At monoenergetic 70 keV, a 50% decrease in noise was observed for DSCT with image smoothing, while PCCT had the same 50% decrease in noise without any smoothing. The PCCT detectability index at a reconstruction strength level of two was equivalent to the highest level of ADMIRE 5 for DSCT. The PCCT showed its superiority over the DSCT, especially for lung nodule detection. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

10 pages, 1553 KiB

Open AccessArticle

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

by Joël Greffier, Quentin Durand, Chris Serrand, Renaud Sales, Fabien de Oliveira, Jean-Paul Beregi, Djamel Dabli and Julien Frandon

Diagnostics 2023, 13(6), 1182; https://doi.org/10.3390/diagnostics13061182 - 20 Mar 2023

Cited by 2 | Viewed by 1500

Abstract

The study’s aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one [...] Read more.

The study’s aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDI_vol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (−26 ± 10%; p < 0.01) and from Smooth to Smoother (−37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

10 pages, 5340 KiB

Open AccessArticle

Lung Dual-Energy CT Perfusion Blood Volume as a Marker of Severity in Chronic Thromboembolic Pulmonary Hypertension

by Salim A. Si-Mohamed, Léa Zumbihl, Ségolène Turquier, Sara Boccalini, Jean-Francois Mornex, Philippe Douek, Vincent Cottin and Loic Boussel

Diagnostics 2023, 13(4), 769; https://doi.org/10.3390/diagnostics13040769 - 17 Feb 2023

Cited by 3 | Viewed by 1819

Abstract

In chronic thromboembolic pulmonary hypertension (CTEPH), assessment of severity requires right heart catheterization (RHC) through cardiac index (CI). Previous studies have shown that dual-energy CT allows a quantitative assessment of the lung perfusion blood volume (PBV). Therefore, the objective was to evaluate the [...] Read more.

In chronic thromboembolic pulmonary hypertension (CTEPH), assessment of severity requires right heart catheterization (RHC) through cardiac index (CI). Previous studies have shown that dual-energy CT allows a quantitative assessment of the lung perfusion blood volume (PBV). Therefore, the objective was to evaluate the quantitative PBV as a marker of severity in CTEPH. In the present study, thirty-three patients with CTEPH (22 women, 68.2 ± 14.8 years) were included from May 2017 to September 2021. Mean quantitative PBV was 7.6% ± 3.1 and correlated with CI (r = 0.519, p = 0.002). Mean qualitative PBV was 41.1 ± 13.4 and did not correlate with CI. Quantitative PBV AUC values were 0.795 (95% CI: 0.637–0.953, p = 0.013) for a CI ≥ 2 L/min/m² and 0.752 (95% CI: 0.575–0.929, p = 0.020) for a CI ≥ 2.5 L/min/m². In conclusion, quantitative lung PBV outperformed qualitative PBV for its correlation with the cardiac index and may be used as a non-invasive marker of severity in CTPEH patients. Full article

(This article belongs to the Special Issue Quantitative Imaging in Computed Tomography)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Quantitative Imaging in Computed Tomography

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI