Development of a method to create uniform phantoms for task-based assessment of CT image quality

Iterative reconstruction with multifrequency signal recognition technology to improve low-contrast detectability: A phantom study

Background

Brain CT needs more attention to improve the extremely low image contrast and image texture.

Purpose

To evaluate the performance of iterative progressive reconstruction with visual modeling (IPV) for the improvement of low-contrast detectability (IPV-LCD) compared with filtered backprojection (FBP) and conventional IPV.

Materials and methods

Low-contrast and water phantoms were used. Helical scans were conducted with the use of a CT scanner with 64 detectors. The tube voltage was set at 120 kVp; the tube current was adjusted from 60 to 300 mA with a slice thickness of 0.625 mm and from 20 to 150 mA with a slice thickness of 5.0 mm. Images were reconstructed with the FBP, conventional IPV, and IPV-LCD algorithms. The channelized Hotelling observer (CHO) model was applied in conjunction with the use of low-contrast modules in the low-contrast phantom. The noise power spectrum (NPS) and normalized NPS were calculated.

Results

At the same standard and strong levels, the IPV-LCD method improved low-contrast detectability compared with the conventional IPV, regardless of contrast-rod diameters. The mean CHO values at a slice thickness of 0.625 mm were 1.83, 3.28, 4.40, 4.53, and 5.27 for FBP, IPV STD, IPV-LCD STD, IPV STR, and IPV-LCD STR, respectively. The normalized NPS for the IPV-LCD STD and STR images were slightly shifted to the higher frequency compared with that for the FBP image.

Conclusion

IPV-LCD images further improve the low-contrast detectability compared with FBP and conventional IPV images while maintaining similar FBP image appearances.

Human Observer Net: A Platform Tool for Human Observer Studies of Image Data

Background Current software applications for human observer studies of images lack flexibility in study design, platform independence, multicenter use, and assessment methods and are not open source, limiting accessibility and expandability. Purpose To develop a user-friendly software platform that enables efficient human observer studies in medical imaging with flexibility of study design. Materials and Methods Software for human observer imaging studies was designed as an open-source web application to facilitate access, platform-independent usability, and multicenter studies. Different interfaces for study creation, participation, and management of results were implemented. The software was evaluated in human observer experiments between May 2019 and March 2021, in which duration of observer responses was tracked. Fourteen radiologists evaluated and graded software usability using the 100-point system usability scale. The application was tested in Chrome, Firefox, Safari, and Edge browsers. Results Software function was designed to allow visual grading analysis (VGA), multiple-alternative forced-choice (m-AFC), receiver operating characteristic (ROC), localization ROC, free-response ROC, and customized designs. The mean duration of reader responses per image or per image set was 6.2 seconds ± 4.8 (standard deviation), 5.8 seconds ± 4.7, 8.7 seconds ± 5.7, and 6.0 seconds ± 4.5 in four-AFC with 160 image quartets per reader, four-AFC with 640 image quartets per reader, localization ROC, and experimental studies, respectively. The mean system usability scale score was 83 ± 11 (out of 100). The documented code and a demonstration of the application are available online (https://github.com/genskeu/HON, https://hondemo.pythonanywhere.com/). Conclusion A user-friendly and efficient open-source application was developed for human reader experiments that enables study design versatility, as well as platform-independent and multicenter usability. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Thompson in this issue.

Comparison of low-contrast detectability between uniform and anatomically realistic phantoms-influences on CT image quality assessment

Objectives

To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background.

Methods

Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment.

Results

Anatomical phantom structure impaired lesion detection at all lesion contrasts (p < 0.01). Detectability in the anatomical phantom at 30 HU contrast was similar to 9 HU contrast in uniform images (91.1% vs. 89.5%). Detection accuracy decreased from 83.6% at 5.6 mGy to 55.4% at 1.4 mGy in uniform FBP images (p < 0.001), whereas AIDR 3D preserved detectability at 1.4 mGy (80.7% vs. 85% at 5.6 mGy, p = 0.375) and was superior to FBP (p < 0.001). In the assessment of anatomical images, superiority of AIDR 3D was not confirmed and dose reduction moderately affected detectability (74.6% vs. 68.2%, p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D).

Conclusions

A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects.

Key Points

• A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images.

• Phantom background structure influences task-based assessment of iterative reconstruction and dose effects.

• Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08248-3.

Visualizing patterns of intervertebral disc damage with dual-energy computed tomography: assessment of diagnostic accuracy in an ex vivo spine biophantom

Background

Previously, dual-energy computed tomography (DECT) has been established for imaging spinal fractures as an alternative modality to magnetic resonance imaging (MRI).

Purpose

To analyze the diagnostic accuracy of DECT in visualizing intervertebral disc (IVD) damage.

Material and Methods

The lumbar spine of a Great Dane dog was used as an ex vivo biophantom. DECT was performed as sequential volume technique on a single-source CT scanner. IVDs were imaged before and after an injection of sodium chloride solution and after anterior discectomy in single-source sequential volume DECT technique using 80 and 135 kVp. Chondroitin/Collagen maps (cMaps) were reconstructed at 1 mm and compared with standard CT. Standardized regions of interest (ROI) were placed in the anterior anulus fibrosus, nucleus pulposus, and other sites. Three blinded readers classified all images as intact disc, nucleus lesion, or anulus lesion. Additionally, clinical examples from patients with IVD lesions were retrospectively identified from the radiological database.

Results

Interrater reliability was almost perfect with a Fleiss kappa of 0.833 (95% confidence interval [CI] 0.83–0.835) for DECT, compared with 0.780 (95% CI 0.778–0.782) for standard CT. For overall detection accuracy of IVD, DECT achieved 91.0% sensitivity (95% CI 83.6–95.8) and 92.0% specificity (95% CI 80.8–97.8). Standard CT showed 91.0% sensitivity (95% CI 83.6–95.8) and 78.0% specificity (95% CI 64.0–88.5).

Conclusion

DECT reliably identified IVD damage in an ex vivo biophantom. Clinical examples of patients with different lesions illustrate the accurate depiction of IVD microstructure. These data emphasize the diagnostic potential of DECT cMaps.

The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

Purpose

To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.

Methods and materials

Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.

Results

FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.

Conclusions

Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01837-y.