Soft Reconstruction Kernels Improve HCC Imaging on a Photon-Counting Detector CT

RATIONALE AND OBJECTIVES

Hepatocellular carcinoma (HCC) is the only tumor entity that allows non-invasive diagnosis based on imaging without further histological proof. Therefore, excellent image quality is of utmost importance for HCC diagnosis. Novel photon-counting detector (PCD) CT improves image quality via noise reduction and higher spatial resolution, inherently providing spectral information. The aim of this study was to investigate these improvements for HCC imaging with triple-phase liver PCD-CT in a phantom and patient population study focusing on identification of the optimal reconstruction kernel.

MATERIALS AND METHODS

Phantom experiments were performed to analyze objective quality characteristics of the regular body and quantitative reconstruction kernels, each with four sharpness levels (36-40-44-48). For 24 patients with viable HCC lesions on PCD-CT, virtual monoenergetic images at 50 keV were reconstructed using these kernels. Quantitative image analysis included contrast-to-noise ratio (CNR) and edge sharpness. Three raters performed qualitative analyses evaluating noise, contrast, lesion conspicuity, and overall image quality.

RESULTS

In all contrast phases, the CNR was highest using the kernels with a sharpness level of 36 (all p < 0.05), with no significant influence on lesion sharpness. Softer reconstruction kernels were also rated better regarding noise and image quality (all p < 0.05). No significant differences were found in image contrast and lesion conspicuity. Comparing body and quantitative kernels with equal sharpness levels, there was no difference in image quality criteria, neither regarding in vitro nor in vivo analysis.

CONCLUSION

Soft reconstruction kernels yield the best overall quality for the evaluation of HCC in PCD-CT. As the image quality of quantitative kernels with potential for spectral post-processing is not restricted compared to regular body kernels, they should be preferred.

Photon-counting detector CT improves quality of arterial phase abdominal scans: A head-to-head comparison with energy-integrating CT

PURPOSE

Photon-counting detector (PCD)-CT is expected to have a substantial impact on oncologic abdominal imaging. We compared subjective and objective image quality between PCD-CT and conventional energy-integrating detector (EID-)CT arterial phase abdominal scans.

METHODS

This study included 84 patients undergoing both types of abdominal CT. EID-CT scans were acquired with a tube voltage of 100 kVp. With PCD-CT, acquired with 120-kVp, we reconstructed polychromatic T3D images and virtual monoenergetic images (VMIs) in 10-keV intervals from 40 to 90 keV. Quantitative image analysis included noise and contrast-to-noise ratio (CNR) of hepatic vessels, kidney cortex, and hypervascular liver lesions to liver parenchyma. Three raters used a 5-point Likert scale for qualitative image analysis of image noise and contrast, lesion conspicuity, and overall image quality. Radiation dose exposure (CT dose index) was compared between the two CT types.

RESULTS

Mean CT dose index and effective dose were respectively 18 % and 26 % lower with PCD-CT versus EID-CT. Compared with EID-CT, CNRs of kidney cortex and vessel to liver parenchyma were significantly higher in PCD-CT VMIs at energies ≤ 60 keV and in polychromatic T3D images (p < 0.004). Overall image quality of PCD-CT VMIs at 50 and 60 keV was rated as significantly better (p < 0.01) than the EID-CT images (inter-reader agreement alpha = 0.80). Lesion conspicuity was significantly better in low-keV VMIs (p < 0.03) and worse in > 70-keV VMIs.

CONCLUSIONS

With low-keV VMI, PCD-CT yields significantly improved objective and subjective quality of arterial phase oncological imaging compared with EID-CT. This advantage may translate into higher diagnostic confidence and lower radiation dose protocols.

Natural language processing of radiology reports to investigate the effects of the COVID-19 pandemic on the incidence and age distribution of fractures

OBJECTIVE

During the COVID-19 pandemic, the number of patients presenting in hospitals because of emergency conditions decreased. Radiology is thus confronted with the effects of the pandemic. The aim of this study was to use natural language processing (NLP) to automatically analyze the number and distribution of fractures during the pandemic and in the 5 years before the pandemic.

MATERIALS AND METHODS

We used a pre-trained commercially available NLP engine to automatically categorize 5397 radiological reports of radiographs (hand/wrist, elbow, shoulder, ankle, knee, pelvis/hip) within a 6-week period from March to April in 2015-2020 into "fracture affirmed" or "fracture not affirmed." The NLP engine achieved an F₁ score of 0.81 compared to human annotators.

RESULTS

In 2020, we found a significant decrease of fractures in general (p < 0.001); the average number of fractures in 2015-2019 was 295, whereas it was 233 in 2020. In children and adolescents (p < 0.001), and in adults up to 65 years (p = 0.006), significantly fewer fractures were reported in 2020. The number of fractures in the elderly did not change (p = 0.15). The number of hand/wrist fractures (p < 0.001) and fractures of the elbow (p < 0.001) was significantly lower in 2020 compared with the average in the years 2015-2019.

CONCLUSION

NLP can be used to identify relevant changes in the number of pathologies as shown here for the use case fracture detection. This may trigger root cause analysis and enable automated real-time monitoring in radiology.

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing

Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.

Two-dimensional parametric parenchymal blood flow in transarterial chemoembolisation for hepatocellular carcinoma: perfusion change quantification and tumour response prediction at 3 months post-intervention

AIM

To evaluate the feasibility and potential value of two-dimensional (2D) parametric parenchymal blood flow (2D-PPBF) for the assessment of perfusion changes during transarterial chemoembolisation with drug-eluting beads (DEB-TACE) and to analyse correlations of 2D-PPBF parameters and tumour response.

MATERIALS AND METHODS

Thirty-two patients (six women, 26 men, mean age: 67±8.9 years) with unresectable hepatocellular carcinoma (HCC) who underwent their first DEB-TACE were included in this study. To quantify perfusion changes using 2D-PPBF, the acquired digital subtraction angiography (DSA) series were post-processed. Ratios were calculated between the reference region of interest (ROI) and the wash-in rate (WIR), the arrival to peak (AP) and the area under the curve (AUC) of the generated time-density curves. Comparisons between pre- and post-embolisation data were made using the Wilcoxon signed-rank test. Tumour response was assessed at 3 months using the modified Response Evaluation Criteria in Solid Tumours (mRECIST) and correlated to changes of 2D-PPBF parameters.

RESULTS

All 2D-PPBF parameters derived from the ROI-based time-attenuation curves were significantly different pre-versus post-DEB-TACE. Although the AUC, the WIR and target lesion size measured in accordance with mRECIST decreased (p≤0.0001) significantly, AP values showed a significant increase (p = 0.0033). Tumour response after DEB-TACE correlated with changes in the AUC (p = 0.01, r = -0.45).

CONCLUSION

2D-PPBF offers an objective approach to analyse perfusion changes of embolised tumour tissue following DEB-TACE and can therefore be used to predict tumour response.

[Reporting initiatives. An update on treatment in radiology]

The written radiological report is the most important means of communication between the radiologist and the referring medical doctor. There is no universal definition of a radiological report concerning its structure and content. The majority of clinicians and radiologists prefer structured reporting rather than free text reports of findings. Structured reporting does not increase the quality of a radiological report but has many advantages in research, teaching and quality management. Using standard RadLex terms facilitates translation and ontological assignment of a report. The Reporting Initiative of the Radiological Society of North America (RSNA) offers free and freely available extensively validated best practices radiology report templates in the new management of radiology report templates (MRRT) format according to the guidelines of the Integrating the Healthcare Enterprise (IHE).