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Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.
Caruso, Damiano; De Santis, Domenico; Tremamunno, Giuseppe; Santangeli, Curzio; Polidori, Tiziano; Bona, Giovanna G; Zerunian, Marta; Del Gaudio, Antonella; Pugliese, Luca; Laghi, Andrea.
Afiliación
  • Caruso D; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • De Santis D; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Tremamunno G; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Santangeli C; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Polidori T; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Bona GG; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Zerunian M; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Del Gaudio A; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Pugliese L; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy.
  • Laghi A; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy. andrea.laghi@uniroma1.it.
Eur Radiol ; 2024 Sep 19.
Article en En | MEDLINE | ID: mdl-39299952
ABSTRACT

OBJECTIVE:

To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. MATERIALS AND

METHODS:

From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups.

RESULTS:

The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score 4, interquartile range 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001).

CONCLUSION:

DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. CLINICAL RELEVANCE STATEMENT Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. KEY POINTS Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Alemania
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Alemania