Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

Cao, Jinjin; Mroueh, Nayla; Pisuchpen, Nisanard; Parakh, Anushri; Lennartz, Simon; Pierce, Theodore T.; Kambadakone, Avinash

doi:10.1007/s00261-023-03992-0

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…The main advantage of model-based IR is the maintenance of CT image quality with low noise, even at low doses; however, its disadvantage is the need for high computational power and low capability in the detection rate of low-contrast structures on low-dose CT images [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Model-based IR algorithms are fully IR algorithms that use forward and backward reconstruction steps from the sinogram domain to the image domain [ 1 ]. The main advantage of model-based IR is the maintenance of CT image quality with low noise, even at low doses; however, its disadvantage is the need for high computational power and low capability in the detection rate of low-contrast structures on low-dose CT images [ 1 , 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there has been growing interest in the application of deep learning image reconstruction (DLIR) algorithms. DLIR is a recently introduced CT image reconstruction algorithm based on deep learning which employs convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at low radiation effective dose (ED) [ 4 ]. CNNs handle millions of parameters trained with thousands of paired high-quality, high-ED, and low-noise ground-truth CT images obtained from a large number of phantoms and patients.…”

Section: Introductionmentioning

confidence: 99%

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Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms

Quaia,

Kiyomi Lanza de Cristoforis,

Agostini

et al. 2024

Tomography

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Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms

Quaia,

Kiyomi Lanza de Cristoforis,

Agostini

et al. 2024

Tomography

View full text Add to dashboard Cite

show abstract

“…In recent years, there has been growing interest in the application of deep learning image reconstruction (DLIR) algorithms, which employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at low radiation doses [4]. The performance of DLIR algorithms for CT image reconstruction relies mainly on the quality and quantity of the training data and high quality reference ground-truth CT images [5,6].…”

mentioning

confidence: 99%

CT Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients

Quaia,

Kiyomi Lanza de Cristoforis,

Agostini

et al. 2024

Preprint

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Deep learning image reconstruction (DLIR) algorithms employs convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and im-proves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently FBP or IR algorithm (Advanced Mod-eled Iterative Reconstruction model-based algorithm [ADMIRE], or Adaptive Iterative Dose Reduction 3D [AIDR 3D]) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs FBP reduced ED (18.45 ± 13.16 mSv vs 22.06 ± 9.55 mSv, P < 0.05), while DLIR vs FBP and vs ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs 14.85 ± 2.73 vs 14.77 ± 32.77 and 11.17 ± 32.77\, P < 0.05) and increased SNR (11.53 ± 9.28 vs 3.99 ± 1.23 vs 5.84 ± 2.74 and 3.58 ± 2.74, P < 0.05). CT scanners employing DLIR reduced radiation ED and im-proved SNR compared to CT scanners using FBP, whereas CT scanners using DLIR improved SNR com-pared to CT scanners using FBP or IR algorithms in ICU patients.

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Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques

Cao,

Mroueh,

Lennartz

et al. 2024

Eur Radiol

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Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

Cited by 6 publications

References 32 publications

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms

CT Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques

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