Clinical routine use of virtual monochromatic datasets based on spectral CT in patients with hypervascularized abdominal tumors - evaluation of effectiveness and efficiency

Abstract

Background Virtual monochromatic images (VMI) generated using spectral computed tomography (CT) are promising recently available tools to improve diagnostic performance in oncologic patients. Purpose To investigate if virtual monochromatic datasets are suitable for clinical routine use in patients with hypervascularized abdominal tumors. Material and Methods A total of 41 patients with hypervascularized hepatocellular carcinoma (HCC), renal cell carcinoma (RCC), or neuroendocrine tumors (NET) were enrolled in the study; 451 CT series were analyzed. In an intra-individual study design, virtual monochromatic datasets of the arterial phase of each scan were computed. Image quality was assessed objectively by determining signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) and subjectively by using five-point Likert-scales. The volume CT dose index (CTDI) was taken from each radiation dose report. The increase in reading time was estimated from the increase in the number of images. Results Intra-individual comparison of the spectral mode in the arterial phase with the portal venous phase revealed no significant increase in the applied dose. SNR, CNR , and CNR were significantly increased by lowering virtual monochromatic energy. Subjective image quality scores revealed an increase of contrast in low energy datasets, resulting in significantly higher diagnostic confidence, but an increased image noise at low energies. While diagnostic confidence improved, taking all datasets into account resulted in a significantly longer estimated reading time. Conclusion In clinical practice, the use of low energy VMI improved diagnostic confidence without a significant increase in dose. The main disadvantage is a decrease in efficiency due to longer reading times.

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

“…Z eff reflected the atomic number of the compound material; the denser the compound, the higher the effective atomic number (37,38). In lung cancer, gastric cancer, and rectal cancer studies, Z eff has proved helpful in the preoperative identification of benign and malignant tumors (39)(40)(41). Our study found that Z eff (nonenhancement) and Z eff (arterial phase) was significantly higher in the malignant group than in the benign group.…”

“…Selecting the module as "monoenergetic plus ROI", the largest possible (round) ROI was placed on the thyroid nodule on the monoenergetic images (70 KeV) (26). Lastly, the dual-energy spectral curve was obtained from 40 Quantitative analysis of Z eff was carried out with Rho/ Z. Rho/Z was measured in the nonenhanced and arterial phases. The same ROI was applied for the Z eff measurement.…”

Noise reduction in dual‐energy computed tomography virtual monoenergetic imaging

“…Clinical applications of DECT‐based VMIs include metal artifact reduction, beam‐hardening correction, contrast and noise optimization, and material differentiation . In addition, DECT‐based VMIs can be used to assess fatty liver and hypervascularized abdominal tumors . Despite promising results obtained in recent investigations, the noise levels of DECT‐based VMIs were high at low and high keVs …”

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

“…Furthermore, VMIs and IMs have shown some potential to improve the diagnostic performance of CT in the differentiation of several types Diagnostics 2021, 11, 1539 2 of 10 of tumors and pathologies and therapy monitoring in these patients [11][12][13][14]. However, it is not possible to efficiently assess all kinds of images, as reading is too complex and time-consuming for clinical routine [15]. A solution to overcome this limitation could be to characterize lesions based on their Hounsfield unit (HU) slope to be extracted from VMIs [13,16].…”

“…A solution to overcome this limitation could be to characterize lesions based on their Hounsfield unit (HU) slope to be extracted from VMIs [ 13 , 16 ]. While low keV levels are favored for detection of hypervascular lesions in the literature [ 15 , 17 , 18 , 19 ], one must be aware that low keV levels increase image noise, which in turn affects sensitivity, especially for detection of small lesions. A solution could be a hybrid approach combining the high contrast of low-keV images and the acceptable noise levels of high-keV images.…”