Thoracic Diseases: Technique and Applications of Dual-Energy CT

Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions

RATIONALE AND OBJECTIVES

To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

MATERIALS AND METHODS

A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction. Quantitative assessment was performed using region of interest (ROI) analysis of eleven different anatomical areas, measuring absolute attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). In addition, CNR of clot compared to normally opacified lumen was calculated in cases that were positive for PE. For qualitative analysis, four different keV levels (40-60-80-100) were evaluated.

RESULTS

The image noise was significantly lower, and the cardiovascular SNR (24.9 ± 5.85 vs. 21.98 ± 5.49) and CNR (23.72 ± 8.00 vs. 20.31 ± 6.44) were significantly higher, on strong Deep Learning Spectral reconstruction (DLSR) than standard DLSR (p < 0.0001). PE-specific CNR (8.58 ± 4.47 vs. 6.25 ± 3.19) was significantly higher on strong DLSR than standard (p < 0.0001). The subjective image quality scores were diagnostically acceptable at four different keV levels (40-60-80-100 keV) evaluated using both standard and strong DLSR, with no qualitative differences observed at those energies.

CONCLUSION

Strong DLSR improves image quality with an increase of the SNR and CNR in DECT-PA compared to standard DLSR.

Quantitative evaluation of axillary lymph nodes in breast cancer on dual-phase dual-energy CT by precise match with pathology

BackgroundIdentification of axillary lymph node (aLN) metastasis in breast cancer (BC) is important. Dual-energy computed tomography (DECT) is a promising innovation in the field of CT. However, its role in evaluating aLNs remains unclear.PurposeTo investigate the diagnostic performance of DECT in evaluating aLN metastasis in BC patients.Material and MethodsData were prospectively collected from treatment-naïve BC patients who underwent DECT for staging, ultrasound-guided biopsy for suspicious aLNs, and placement of tissue marker in the pathology-positive aLNs. Further cross-sectional imaging was performed preoperatively to locate the marker-labeled LN and help to identify the pathologically proven LN on DECT. Maximal short diameter (MSD) and 13 DECT parameters were measured on metastatic aLNs and contralateral normal aLNs. The univariate, least absolute shrinkage and selection operator and multivariable logistic regression were performed to find independent parameters for predicting metastasis. The diagnostic performance was assessed using receiver operating characteristics (ROC) analysis.ResultsA total of 76 axillary LNs (38 metastasized, 38 normal) from 38 patients were finally included. All DECT parameters showed significant difference between metastatic and normal LNs (all P < 0.001). Arterial enhancement fraction (AEF) and MSD were independent predictors of metastasis (P = 0.010 and 0.014, respectively). The area under the ROC curve (AUC) of AEF was the highest (0.967). The combined AUC of AEF and MSD was significantly higher than that of MSD alone (0.994 vs. 0.943; P = 0.025).ConclusionDECT is a promising tool for preoperative evaluation of aLNs in BC patients, with MSD and AEF having the best diagnostic performance.

[Research Progress on Imaging Diagnosis of Non-small Cell Lung Cancer Which Invades Pleura or Chest Wall]

Accurate staging is the fundamental basis for the treatment and prognosis of non-small cell lung cancer (NSCLC), and whether the tumor involves the pleura or chest wall is a critical aspect in assessing the staging of peripheral lung cancer. Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) and positron emission tomography (PET) are widely used to determine pleural invasion in NSCLC. There has been an increasing number of studies evaluating whether NSCLC invades the pleura and the extent of such invasion. This article provides a review of the staging and the imaging diagnostic criteria of pleural invasion, aiming to offer references for peers in the precise diagnosis of pleural or chest wall invasion.
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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.

Reply to Perrella et al. Coming Back to the Basics. Comment on "Cangir et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics 2022, 12, 416"

We thank to Dr. Perrella and and his fellow authors for your kind letter and thoughtful comments [...].