Comparison of uniform and realistic phantoms for image quality evaluation in computed tomography – phantom effects on low-contrast detectability

Die Computertomographie (CT) spielt eine Schlüsselrolle bei der Diagnose und Verlaufskontrolle zahlreicher Erkrankungen, geht allerdings auch mit der Strahlenexposition von Patienten und Patientinnen einher. Ein Hauptziel moderner CT-Technologien und klinischer Bildgebungsprotokolle besteht daher darin, ein optimales Gleichgewicht zwischen Strahlenexposition und angemessener Bildqualität für eine zuverlässige Diagnose zu erreichen. Phantome sind Referenzobjekte, die zu Testzwecken untersucht werden, um CT-Technologien und Bildgebungsverfahren zu überprüfen und zu verbessern. Phantome ermöglichen wiederholte CT-Aufnahmen, vermeiden die Strahlenexposition von Patienten und Patientinnen für Testzwecke und liefern eine bekannte Grundwahrheit zum Abgleich mit dem Scanergebnis. Derzeit besitzen Phantome häufig homogene Hintergrundstrukturen, wohingegen Patienten und Patientinnen anatomische Details und unterschiedliche Gewebestrukturen aufweisen. Es ist daher fraglich, inwieweit die Bewertung von CT-Aufnahmen mit homogenen Phantomen auf die klinische Bildgebung übertragen werden kann. Vor diesem Hintergrund wurde in der vorliegenden Arbeit der Einfluss der Phantomhintergrundstruktur auf die Bewertung von CT-Bildqualität in Niedrigkontrastdetektierbarkeitsexperimenten untersucht. Hierfür wurden zwei Halsphantome mit identischer Form und Größe verwendet, die sich durch ihre homogenen beziehungsweise anatomischen Hintergrundstrukturen unterschieden. Sieben Radiologen und Radiologinnen bewerteten die Detektierbarkeit von Niedrigkontrastläsionen. Darüber hinaus wurden Rauschparameter zwischen den beiden Phantomarten vergleichen. Die Ergebnisse zeigten, dass die Detektion von Niedrigkontrastläsionen bei gleichem Läsionskontrast zu umliegenden Arealen in beiden Phantomarten signifikant durch anatomische Hintergrundstrukturen beeinträchtigt wird. Darüber hinaus wurde die Bewertung von Dosis und Bildrekonstruktionsverfahren durch Phantomeigenschaften beeinflusst und die Rauschunterdrückung durch iterative Rekonstruktion war abhängig von den im Phantom vorliegenden Hintergrundstrukturen. Zusammenfassend ergibt sich, dass Hintergrundstrukturen und anatomische Details von Phantomen die Bewertung von diagnostischer Bildqualität und Bildgebungstechnologien beeinflussen. Es ist davon auszugehen, dass die Aussagekraft von CT-Bewertungen für die klinische Bildgebung mit der Realitätsnähe der Untersuchungsumgebung zunimmt.Computed tomography (CT) plays a key role for the diagnosis and follow up of many diseases but also involves the exposure of patients to ionizing radiation. A main goal of modern CT technologies and clinical imaging protocols is therefore to achieve an optimal balance between dose exposure and adequate image quality for a reliable diagnosis. Phantoms are reference objects that are scanned for test purposes to verify and improve CT technologies and imaging methods. Phantoms enable repeated CT scans without exposing patients to radiation and provide a ground truth for comparison with the scanner output. Phantoms frequently have a uniform background structure, whereas patients have anatomical details and tissue structures. It is therefore questionable to which extent CT assessment with uniform phantoms can be transferred to clinical imaging. This work investigated the influence of phantom background structure on CT assessment using the detectability of low-contrast lesions as an image quality parameter. Two neck phantoms of identical shape and with uniform and anatomical background structures were examined. Low-contrast detectability was assessed by seven radiologists. Additionally, noise metrics were compared between both phantom types. Results showed that lesion detection was significantly impaired by anatomical background structure in a comparison of lesions with identical contrast to surrounding structures. Moreover, assessment of dose and image reconstruction was influenced by the phantom type and the denoising power of iterative reconstruction was dependent on phantom background structure. In conclusion, phantom background structure and anatomical detail influence the evaluation of diagnostic image quality and imaging technologies. The validity of CT assessment for clinical imaging can be expected to increase as the experimental design becomes more realistic

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

Task-based assessment of neck CT protocols using patient-mimicking phantoms—effects of protocol parameters on dose and diagnostic performance

Objectives: To assess how modifying multiple protocol parameters affects the dose and diagnostic performance of a neck CT protocol using patient-mimicking phantoms and task-based methods. Methods: Six patient-mimicking neck phantoms containing hypodense lesions of 1 cm diameter and 30 HU contrast and one non-lesion phantom were examined with 36 CT protocols. All possible combinations of the following parameters were investigated: 100- and 120-kVp tube voltage; tube current modulation (TCM) noise levels of SD 7.5, 10, and 14; pitches of 0.637, 0.813, and 1.388; filtered back projection (FBP); and iterative reconstruction (AIDR 3D). Dose-length products (DLPs) and lesion detectability (assessed by 14 radiologists) were compared with the clinical standard protocol (120 kVp, TCM SD 7.5, 0.813 pitch, AIDR 3D). Results: The DLP of the standard protocol was 25 mGy•cm; the area under the curve (AUC) was 0.839 (95%CI: 0.790-0.888). Combined effects of tube voltage reduction to 100 kVp and TCM noise level increase to SD 10 optimized protocol performance by improving dose (7.3 mGy•cm) and detectability (AUC 0.884, 95%CI: 0.844-0.924). Diagnostic performance was significantly affected by the TCM noise level at 120 kVp (AUC 0.821 at TCM SD 7.5 vs. 0.776 at TCM SD 14, p = 0.003), but not at 100-kVp tube voltage (AUC 0.839 at TCM SD 7.5 vs. 0.819 at TCM SD 14, p = 0.354), the reconstruction method at 100 kVp (AUC 0.854 for AIDR 3D vs. 0.806 for FBP, p < 0.001), but not at 120-kVp tube voltage (AUC 0.795 for AIDR 3D vs. 0.793 for FBP, p = 0.822), and the tube voltage for AIDR 3D reconstruction (p < 0.001), but not for FBP (p = 0.226). Conclusions: Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D resulted in an optimal neck protocol in terms of dose and diagnostic performance. Protocol parameters were subject to complex interactions, which created opportunities for protocol improvement. Key points: • A task-based approach using patient-mimicking phantoms was employed to optimize a CT system for neck imaging through systematic testing of protocol parameters. • Combined effects of 100-kVp tube voltage, TCM noise level of SD 10, a pitch of 0.813, and AIDR 3D reconstruction resulted in an optimal protocol in terms of dose and diagnostic performance. • Interactions of protocol parameters affect diagnostic performance and should be considered when optimizing CT techniques

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

3D printing of anatomically realistic phantoms with detection tasks to assess the diagnostic performance of CT images

Objectives: Detectability experiments performed to assess the diagnostic performance of computed tomography (CT) images should represent the clinical situation realistically. The purpose was to develop anatomically realistic phantoms with low-contrast lesions for detectability experiments. Methods: Low-contrast lesions were digitally inserted into a neck CT image of a patient. The original and the manipulated CT images were used to create five phantoms: four phantoms with lesions of 10, 20, 30, and 40 HU contrast and one phantom without any lesion. Radiopaque 3D printing with potassium-iodide-doped ink (600 mg/mL) was used. The phantoms were scanned with different CT settings. Lesion contrast was analyzed using HU measurement. A 2-alternative forced choice experiment was performed with seven radiologists to study the impact of lesion contrast on detection accuracy and reader confidence (1 = lowest, 5 = highest). Results: The phantoms reproduced patient size, shape, and anatomy. Mean ± SD contrast values of the low-contrast lesions were 9.7 ± 1.2, 18.2 ± 2, 30.2 ± 2.7, and 37.7 ± 3.1 HU for the 10, 20, 30, and 40 HU contrast lesions, respectively. Mean ± SD detection accuracy and confidence values were not significantly different for 10 and 20 HU lesion contrast (82.1 ± 6.3% vs. 83.9 ± 9.4%, p = 0.863 and 1.7 ± 0.4 vs. 1.8 ± 0.5, p = 0.159). They increased to 95 ± 5.7% and 2.6 ± 0.7 for 30 HU lesion contrast and 99.5 ± 0.9% and 3.8 ± 0.7 for 40 HU lesion contrast (p < 0.005). Conclusions: A CT image was manipulated to produce anatomically realistic phantoms for low-contrast detectability experiments. The phantoms and our initial experiments provide a groundwork for the assessment of CT image quality in a clinical context

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

Purpose: To develop a customized method to produce uniform phantoms for task-based assessment of CT image quality. Methods: Contrasts between polymethyl methacrylate (PMMA) and fructose solutions of different concentrations (240, 250, 260, 280, 290, 300, 310, 320, 330, and 340 mg/mL) were calculated. A phantom was produced by laser cutting PMMA slabs to the shape of a patient's neck. An opening of 10 mm diameter was cut into the left parapharyngeal space. An angioplasty balloon was inserted and filled with the fructose solutions to simulate low-contrast lesions. The phantom was scanned with six tube currents. Images were reconstructed with filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Calculated and measured contrasts were compared. The phantom was evaluated in a detectability experiment using images with 4 and 20 HU lesion contrast. Results: Low-contrast lesions of 4, 9, 11, 13, 18, 20, 24, 30, 35, and 37 HU contrast were simulated. Calculated and measured contrasts correlated excellently (r = 0.998; 95% confidence interval: 0.991 to 1). The mean +/- SD difference was 0.41 +/- 2.32 HU (P < 0.0001). Detection accuracy and reader confidence were 62.9 +/- 18.2% and 1.58 +/- 0.68 for 4 HU lesion contrast and 99.6 +/- 1.3% and 4.27 +/- 0.92 for 20 HU lesion contrast (P < 0.0001), confirming that the method produced lesions at the threshold of detectability. Conclusion: A cost-effective and flexible approach was developed to create uniform phantoms with low-contrast signals. The method should facilitate access to customized phantoms for task-based image quality assessment