Differential diagnosis of hepatic cancerous nodules and cirrhosis nodules by spectral CT imaging: a feasibility study

Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction

AIM

To investigate the improvement in image quality of triple-low-protocol (low radiation, low contrast medium dose, low injection speed) renal artery computed tomography (CT) angiography (RACTA) using deep-learning image reconstruction (DLIR), in comparison with standard-dose single- and dual-energy CT (DECT) using adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithm.

MATERIALS AND METHODS

Ninety patients for RACTA were divided into different groups: standard-dose single-energy CT (S group) using ASIR-V at 60% strength (60%ASIR-V), DECT (DE group) with 60%ASIR-V including virtual monochromatic images at 40 keV (DE40 group) and 70 keV (DE70 group), and the triple-low protocol single-energy CT (L group) with DLIR at high level (DLIR-H). The effective dose (ED), contrast medium dose, injection speed, standard deviation (SD), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal aorta (AA), and left/right renal artery (LRA, RRA), and subjective scores were compared among the different groups.

RESULTS

The L group significantly reduced ED by 37.6% and 31.2%, contrast medium dose by 33.9% and 30.5%, and injection speed by 30% and 30%, respectively, compared to the S and DE groups. The L group had the lowest SD values for all arteries compared to the other groups (p<0.001). The SNR of RRA and LRA in the L group, and the CNR of all arteries in the DE40 group had highest value compared to others (p<0.05). The L group had the best comprehensive score with good consistency (p<0.05).

CONCLUSIONS

The triple-low protocol RACTA with DLIR-H significantly reduces the ED, contrast medium doses, and injection speed, while providing good comprehensive image quality.

Dual-Energy CT of the Abdomen: Radiology In Training

A 61-year-old man with an esophageal cancer diagnosis underwent staging dual-energy CT of the chest and abdomen in the portal venous phase after contrast media administration. Aside from the primary tumor and suspicious local lymph nodes, CT revealed hypoattenuating ambiguous liver lesions, an incidental right adrenal nodule, and a right renal lesion with soft-tissue attenuation. In addition, advanced atherosclerosis of the abdominal aorta and its major branches was noted. This article provides a case-based review of dual-energy CT technologies and their applications in the abdomen. The clinical utility of virtual monoenergetic images, virtual unenhanced images, and iodine maps is discussed.

Dual-Energy Computed Tomography for Evaluation of Breast Cancer Follow-Ups: Comparison of Virtual Monoenergetic Images and Iodine-Map

Differentiating tumor tissue from dense breast tissue can be difficult. Dual-energy CT (DECT) could be suitable for making diagnoses at breast cancer follow-ups. This study investigated the contrast in DECT images and iodine maps for patients with breast cancer being followed-up. Chest CT images captured in 2019 were collected. Five cases of metastatic breast cancer in the lungs were analyzed; the contrast-to-noise ratio (for breast tissue and muscle: CNRb and CNRm, respectively), tumor-to-breast mammary gland ratio (T/B), and tumor-to-muscle ratio (T/M) were calculated. For 84 cases of no metastasis, monochromatic spectral and iodine maps were obtained to compare differences under various breast densities using the K-means algorithm. The optimal T/B, T/M, and CNRb (related to mammary glands) were achieved for the 40-keV image. Conversely, CNRm (related to lungs) was better for higher energy. The optimal balance was achieved at 80 keV. T/B, T/M, and CNR were excellent for iodine maps, particularly for density > 25%. In conclusion, energy of 80 keV is the parameter most suitable for observing the breast and lungs simultaneously by using monochromatic spectral images. Adding iodine mapping can be appropriate when a patient’s breast density is greater than 25%.

Spectral CT of the abdomen: Where are we now?

Spectral CT adds a new dimension to radiological evaluation, beyond assessment of anatomical abnormalities. Spectral data allows for detection of specific materials, improves image quality while at the same time reducing radiation doses and contrast media doses, and decreases the need for follow up evaluation of indeterminate lesions. We review the different acquisition techniques of spectral images, mainly dual-source, rapid kV switching and dual-layer detector, and discuss the main spectral results available. We also discuss the use of spectral imaging in abdominal pathologies, emphasizing the strengths and pitfalls of the technique and its main applications in general and in specific organs.

Material decomposition using iodine quantification on spectral CT for characterising nodules in the cirrhotic liver: a retrospective study

Background

There is limited scientific evidence on the potential of spectral computed tomography (SCT) for differentiation of nodules in the cirrhotic liver. We aimed to assess SCT-generated material density (MD) parameters for nodule characterisation in cirrhosis.

Methods

Dynamic dual-energy SCT scans of cirrhotic patients performed over 3 years were retrospectively reviewed. They were classified as hepatocellular carcinoma (HCC), regenerative or indeterminate, according to the European Association for the Study of the Liver criteria. MD maps were generated to calculate the area under the curve (AUC) and cutoff values to discriminate these nodules in the hepatic arterial phase (HAP) and portal venous phase (PVP). MD maps included iodine concentration density (ICD) of the liver and nodule, lesion-to-normal liver ICD ratio (LNR) and difference in nodule ICD between HAP and PVP.

Results

Three hundred thirty nodules belonging to 300 patients (age 53.0 ± 12.7 years, mean ± standard deviation) were analysed at SCT (size 2.3 ± 0.8 cm, mean ± SD). One hundred thirty-three (40.3%) nodules were classified as HCC, 147 (44.5%) as regenerative and 50 (15.2%) as indeterminate. On histopathology, 136 (41.2%) nodules were classified as HCC, 183 (55.5%) as regenerative and 11 (3.3%) as dysplastic. All MD parameters on HAP and the nodule  difference in ICD could discriminate pathologically proven HCC or potentially malignant nodules from regenerative nodules (p < 0.001). The AUC was 82.4% with a cutoff > 15.5 mg/mL for nodule ICD, 81.3% > 1.8 for LNR-HAP and 81.3% for difference in ICD > 3.5 mg/mL.

Conclusion

SCT-generated MD parameters are viable diagnostic tools for differentiating malignant or potentially malignant from benign nodules in the cirrhotic liver.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41747-021-00220-6.