Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

Shehata, Mostafa; Saad, Ahmed; Kamel, Serageldin; Stanietzky, Nir; Roman-Colon, Alicia M.; Morani, Ajaykumar C.; Elsayes, Khaled M.; Jensen, Corey T.

doi:10.1007/s00261-023-03966-2

Cited by 11 publications

(6 citation statements)

References 68 publications

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“…Although noise levels progressively decreased in the order of HIR, AiCE, HIR + SEMAR, and AiCE + SEMAR sequences, no statistically significant difference was observed between HIR and AiCE. This finding contradicts previous literature [ 29 ]. We hypothesized that artifacts interfered with the potential noise reduction capabilities of AiCE despite the artifact mitigation ability of SEMAR.…”

Section: Discussioncontrasting

confidence: 99%

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

Cai,

Jiang,

Xie

et al. 2024

BMC Med Imaging

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Background The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. Materials This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. Results The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. Conclusions In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

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Section: Discussioncontrasting

confidence: 99%

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

Cai,

Jiang,

Xie

et al. 2024

BMC Med Imaging

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“…Our study demonstrated that DL-based reconstructions can significantly improve the CNR both at the frontal lobe and basal ganglia levels and provide a low image noise at the supratentorial level, with a mean noise reduction of 19.6%. These findings are consistent with previous studies evaluating CT image reconstruction using DL-based algorithms in the brain [9][10][11][12][13][14] and other anatomical regions [7,8,[18][19][20][21]. These results at the supratentorial level confirm the utility of DL-based reconstructions, especially in the emergency setting, where subtle definition of anatomical structures boosts the diagnostic performance of CT in the detection of ischemic and hemorrhagic alterations.…”

Section: Discussionsupporting

confidence: 91%

“…In recent years, deep learning (DL) has emerged as a transformative technology in medical imaging [6]. DL methods leverage complex neural network architectures to learn intricate patterns and features directly from vast amounts of training data, enabling them to achieve high performances in various imaging-related tasks, one of them indeed being the improvement of image reconstruction [7,8]. Of note, the training process behind DL-based image reconstruction can deal with a fundamental issue of iterative reconstruction, i.e., the loss of algorithm convergence due to the high number of factors that must be balanced during iteration [9].…”

Section: Introductionmentioning

confidence: 99%

“…DL-based reconstructions, with the True Fidelity (GE Healthcare, Chicago, IL, USA) and the Advanced Intelligent Clear-IQ Engine (AiCE, Canon Medical Systems, Otawara, Japan) algorithms, have been extensively investigated in abdominal imaging [8] and have also been the object of pilot studies in unenhanced brain CT for adult and pediatric populations. For example, comparisons of image quality between iterative reconstructions and the True Fidelity algorithm have been reported in adult populations by Kim et al [10] and Alagic et al [11] in adult patients, and by Sun et al [12] and Lee et al [13] in pediatric patients.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality

Cozzi,

Cè,

De Padova

et al. 2023

Tomography

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This exploratory retrospective study aims to quantitatively compare the image quality of unenhanced brain computed tomography (CT) reconstructed with an iterative (AIDR-3D) and a deep learning-based (AiCE) reconstruction algorithm. After a preliminary phantom study, AIDR-3D and AiCE reconstructions (0.5 mm thickness) of 100 consecutive brain CTs acquired in the emergency setting on the same 320-detector row CT scanner were retrospectively analyzed, calculating image noise reduction attributable to the AiCE algorithm, artifact indexes in the posterior cranial fossa, and contrast-to-noise ratios (CNRs) at the cortical and thalamic levels. In the phantom study, the spatial resolution of the two datasets proved to be comparable; conversely, AIDR-3D reconstructions showed a broader noise pattern. In the human study, median image noise was lower with AiCE compared to AIDR-3D (4.7 vs. 5.3, p < 0.001, median 19.6% noise reduction), whereas AIDR-3D yielded a lower artifact index than AiCE (7.5 vs. 8.4, p < 0.001). AiCE also showed higher median CNRs at the cortical (2.5 vs. 1.8, p < 0.001) and thalamic levels (2.8 vs. 1.7, p < 0.001). These results highlight how image quality improvements granted by deep learning-based (AiCE) and iterative (AIDR-3D) image reconstruction algorithms vary according to different brain areas.

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“…Our study confirmed that DLIR reconstruction for CT images provides significant benefits in terms of dose reduction and image quality over FBP and improves image quality in comparison to both IR algorithms we used in ICU patients, both in terms of image noise and SNR, which emphasizes the advantage of the DLIR approach and its potential in daily clinical practice in keeping with previously published papers [14][15][16].…”

Section: Discussionsupporting

confidence: 87%

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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Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis

Cited by 11 publications

References 68 publications

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques

Deep Learning-Based Versus Iterative Image Reconstruction for Unenhanced Brain CT: A Quantitative Comparison of Image Quality

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

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