Computed Tomography Window Blending: Feasibility in Thoracic Trauma

Comparison of detection of trauma-related injuries using combined "all-in-one" fused images and conventionally reconstructed images in acute trauma CT

OBJECTIVES

To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT.

METHODS

In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection.

RESULTS

Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection.

CONCLUSIONS

Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT.

KEY POINTS

• Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy.

Lesion measurement on a combined "all-in-one" window for chest CT: effect on intra- and interobserver variability

PURPOSE

A newly developed image processing technique fuses conventional windows into a single 'All-In-One' (AIO) window. This study aims to evaluate variability of CT measurement of lesions in thoracic oncology patients on this novel AIO-window.

METHODS

Six radiologists with different levels of expertise measured 368 lesions of various size, origin and sharpness. All lesions were measured twice on the AIO-window and twice on the conventional window settings. Intraclass correlation coefficients and Bland-Altman plots were used to assess intra- and interobserver variability.

RESULTS

Overall intra-observer agreement for lesion diameters on the AIO-window and conventional window settings was 0.986 (95% Confidence interval (CI): 0.983-0.989) and 0.991 (95% CI 0.989-0.993) respectively. For interobserver agreement this was 0.982 (95% CI 0.979-0.985) (AIO) and 0.979 (95% CI 0.957-0.982) (conventional). For both the AIO and conventional windows, intra- and interobserver agreement were dependent on size, sharpness and reader experience. Measurement variability decreased with increasing lesion size. Regarding sharpness, inter- and intra-observer agreement ranged from 0.986-0.989 (AIO) and 0.985-0.992 (conventional) for well-defined lesions and from 0.978-0.983 (AIO) and 0.974-0.991 (conventional) for ill-defined lesions.

CONCLUSIONS

Lesion diameters were consistently smaller on the AIO-window compared to conventional window settings. Overall intra- and interobserver variability rates were similar for the AIO-window and conventional window settings. We conclude that the AIO-window offers a reliable and reproducible alternative for measurement of thoracic lesions.

Computed tomography window blending in maxillofacial imaging

PURPOSE

The purpose of this study was to demonstrate the ability of a custom window blending algorithm to depict multicompartmental disease processes of the maxillofacial region in a single image, using routine computed tomography (CT) DICOM data.

METHODS

Five cases were selected from case files demonstrating trauma, infection, and malignancy of the maxillofacial region on routine CT examinations. Images were processed with a modified Relative Attenuation-Dependent Image Overlay (RADIO) window-blending algorithm in Adobe Photoshop controlled by ExtendScript.

RESULTS

The modified RADIO algorithm was able to demonstrate pertinent multicompartmental imaging findings in each of the examinations, allowing simultaneous visualization of clinically relevant bone and soft tissue findings in a single image, without needing to change window and level settings.

CONCLUSION

A custom window blending algorithm can demonstrate a range of multicompartmental pathology in the maxillofacial region in a single image.

CT Window Blending for Evaluation of Multicompartmental Thoracic Pathology

Standard computed tomography windows are limited in the thorax because they can only display 1 tissue at a time. Computed tomography window blending allows for simultaneous viewing of all 3 tissues in a single image, which may improve evaluation of diseases that contiguously affect multiple compartments. In this article, we use examples to illustrate the utility of window blending in aggressive infections, metastatic cancer, and penetrating trauma.