Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium

BACKGROUND

Photon-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets.

METHODS

An organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients' left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test.

RESULTS

There was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%).

CONCLUSIONS

VMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV.

RELEVANCE STATEMENT

Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation.

KEY POINTS

• Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.

Influence of beam hardening in dual-energy CT imaging: phantom study for iodine mapping, virtual monoenergetic imaging, and virtual non-contrast imaging

In this study, we investigated the influence of beam hardening on the dual-energy computed tomography (DECT) values of iodine maps, virtual monoenergetic (VME) images, and virtual non-contrast (VNC) images. 320-row DECT imaging was performed by changing the x-ray tube energy for the first and second rotations. DECT values of 5 mg/mL iodine of the multi-energy CT phantom were compared with and without a 2-mm-thick attenuation rubber layer (~700 HU) wound around the phantom. It was found that the CT density values UH, with/without the rubber layer had statistical differences in the iodine map (184 ± 0.7 versus 186 ± 1.8), VME images (125 ± 0.3 versus 110 ± 0.4), and VNC images (−58 ± 0.7 versus −76 ± 1.7) (p < 0.010 for all). This suggests that iodine mapping may be underestimated by DECT and overestimated by VME imaging because of x-ray beam hardening. The use of VNC images instead of plain CT images requires further investigation because of underestimation.

Deep learning to convert unstructured CT pulmonary angiography reports into structured reports

Background

Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed to convert unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports.

Methods

A self-supervised convolutional neural network-based algorithm was trained on a dataset of 475 manually structured CTPA reports. Labels for individual statements included “pulmonary arteries,” “lungs and airways,” “pleura,” “mediastinum and lymph nodes,” “cardiovascular,” “soft tissues and bones,” “upper abdomen,” and “lines/tubes.” The algorithm was applied to a test set of 400 unstructured CTPA reports, generating a predicted label for each statement, which was evaluated by two independent observers. Per-statement accuracy was calculated based on strict criteria (algorithm label counted as correct if the statement unequivocally contained content only related to that particular label) and a modified criteria, accounting for problematic statements, including typographical errors, statements that did not fit well into the classification scheme, statements containing content for multiple labels, etc.

Results

Of the 4,157 statements, 3,806 (91.6%) and 3,986 (95.9%) were correctly labeled by the algorithm using strict and modified criteria, respectively, while 274 (6.6%) were problematic for the manual observers to label, the majority of which (n = 173) were due to more than one section being included in one statement.

Conclusion

This algorithm showed high accuracy in converting free-text findings into structured reports, which could improve communication between radiologists and clinicians without loss of productivity and provide more structured data for research/data mining applications.

Evaluation of response to conventional chemotherapy and radiotherapy by perfusion computed tomography in non-small cell lung cancer (NSCLC)

Background

To evaluate changes in perfusion computed tomography (PCT) parameters induced by treatment with conventional chemotherapy (CCT) alone or with CCT and radiation therapy (RT) in patients with non-small cell lung cancer (NSCLC) and to determine whether these changes correlate with response as defined by the response evaluation criteria in solid tumours version 1.1 (RECIST-1.1).

Methods

Fifty-three patients with a histological diagnosis of NSCLC prospectively underwent PCT of the whole tumour, before/after CCT or before/after CCT and RT. Blood flow (BF), blood volume (BV), permeability (PMB), and mean transit time (MTT) were compared before and after treatment and with the response as defined by RECIST-1.1. The relationship between changes in the perfusion parameters and in tumour size was also evaluated.

Results

PCT parameters decreased after treatment, significantly for BV (p = 0.002) and MTT (p = 0.027). The 30 patients with partial response had a significant decrease of 21% for BV (p = 0.006) and 17% for MTT (p = 0.031). A non-significant decrease in all perfusion parameters was found in patients with stable disease (p > 0.137). In patients with progressive disease, MTT decreased by 10% (p = 0.465) and the other parameters did not significantly vary (p > 0.809). No significant correlation was found between changes in size and PCT parameters (p > 0.145).

Conclusions

Treatment of NSCLC with platinum derivatives, with or without RT, induces changes in PCT parameters. Partial response is associated with a significant decrease in BV and MTT, attributable to the effect of the treatment on tumour vascularisation.

Using body mass index to estimate individualised patient radiation dose in abdominal computed tomography

BACKGROUND

The size-specific dose estimate (SSDE) is a dose-related metrics that incorporates patient size into its calculation. It is usually derived from the volume computed tomography dose index (CTDI_vol) by applying a conversion factor determined from manually measured anteroposterior and lateral skin-to-skin patient diameters at the midslice level on computed tomography (CT) localiser images, an awkward, time-consuming, and not highly reproducible technique. The objective of this study was to evaluate the potential for the use of body mass index (BMI) as a size-related metrics alternative to the midslice effective diameter (D_E) to obtain a size-specific dose (SSDE) in abdominal CT.

METHODS

In this retrospective study of patients who underwent abdominal CT for the investigation of inflammatory bowel disease, the D_E was measured on the midslice level on CT-localiser images of each patient. This was correlated with patient BMI and the linear regression equation relating the quantities was calculated. The ratio between the internal and the external abdominal diameters (D_RATIO) was also measured to assess correlation with radiation dose. Pearson correlation analysis and linear regression models were used.

RESULTS

There was good correlation between D_E and patient BMI (r = 0.88). An equation allowing calculation of D_E from BMI was calculated by linear regression analysis as follows: D_E = 0.76 (BMI) + 9.4. A weak correlation between radiation dose and D_RATIO was demonstrated (r = 0.45).

CONCLUSIONS

Patient BMI can be used to accurately estimate D_E, obviating the need to measure anteroposterior and lateral diameters in order to calculate a SSDE for abdominal CT.

Dual-layer spectral computed tomography: measuring relative electron density

Background

X-ray and particle radiation therapy planning requires accurate estimation of local electron density within the patient body to calculate dose delivery to tumour regions. We evaluate the feasibility and accuracy of electron density measurement using dual-layer computed tomography (DLCT), a recently introduced dual-energy CT technique.

Methods

Two calibration phantoms were scanned with DLCT and virtual monoenergetic images (VMIs) at 50 keV and 200 keV were generated. We investigated two approaches to obtain relative electron densities from these VMIs: to fit an analytic interaction cross-sectional model and to empirically calibrate a conversion function with one of the phantoms. Knowledge of the emitted x-ray spectrum was not required for the presented work.

Results

The results from both methods were highly correlated to the nominal values (R > 0.999). Except for the water and lung inserts, the error was within 1.79% (average 1.53%) for the cross-sectional model and 1.61% (average 0.87%) for the calibrated conversion. Different radiation doses did not have a significant influence on the measurement (p = 0.348, 0.167), suggesting that the methods are reproducible. Further, we applied these methods to routine clinical data.

Conclusions

Our study shows a high validity of electron density estimation based on DLCT, which has potential to improve the procedure and accuracy of measuring electron density in clinical practice.