Dataset Information

Assessment of image reconstruction algorithm coupled with fine-resolution array of Cherenkov detectors.

ABSTRACT: The ability to reconstruct fine-resolution images in a high-count-rate environment is an ongoing challenge to the fields of nuclear security, medicine, and high energy physics. This study presents the characterization and performance of an image reconstruction algorithm and detector array in such an environment. The detector array is composed of quartz Cherenkov radiators and lutetium-yttrium oxyorthosilicate inorganic scintillators detector elements with light collection via silicon photomultipliers (SiPM). The reconstruction algorithm was evaluated using ANSI testing standard N42.46-2008 for imaging performance of active interrogation systems for national security applications; this included spatial resolution, wire detection, and penetration studies. The array was tested using a 6-MVp pulsed photon beam where test objects were translated through the detector field of view demonstrating a capability to resolve a 2.05-mm wire at a source standoff of 2.2 m, a horizontal spatial resolution of 3 mm, and a contrast sensitivity of 1.5%.

SUBMITTER: Maloney L

PROVIDER: S-EPMC8918355 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Assessment of image reconstruction algorithm coupled with fine-resolution array of Cherenkov detectors.

Maloney Luke L Duce Mackenzie M Erickson Anna A

Scientific reports 20220312 1

The ability to reconstruct fine-resolution images in a high-count-rate environment is an ongoing challenge to the fields of nuclear security, medicine, and high energy physics. This study presents the characterization and performance of an image reconstruction algorithm and detector array in such an environment. The detector array is composed of quartz Cherenkov radiators and lutetium-yttrium oxyorthosilicate inorganic scintillators detector elements with light collection via silicon photomultip ...[more]

PMID: 35279676

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Project description:ObjectivesTo perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm.Materials and methodsOncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale.ResultsFifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%.ConclusionsDLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm.Clinical relevance statementDeep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm.Key points• Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.

Dataset Information

Assessment of image reconstruction algorithm coupled with fine-resolution array of Cherenkov detectors.

Publications

Assessment of image reconstruction algorithm coupled with fine-resolution array of Cherenkov detectors.

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