Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Bernard, Angélique; Comby, Pierre-Olivier; Lemogne, Brivaël; Haioun, Karim; Ricolfi, F.; Chevallier, Olivier; Loffroy, Romaric

doi:10.21037/qims-20-626

Cited by 58 publications

(51 citation statements)

References 23 publications

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“…However, many studies have shown that the potential dose reduction obtained with IR algorithms was limited to low contrast liver lesions and the outcomes found on phantoms overestimated the real dose reduction in patients (44)(45)(46). Nevertheless, the first patient studies published on pulmonary or cardiac CT angiography and chest and abdominal CT have also confirmed that AiCE improved both image quality and lesion detection as compared with AIDR 3D for a given dose level (22,25,26,30) or with a dose reduction (26,29,34,35). Singh et al found that the dose could be reduced by −84% between a standard protocol (CTDI vol : 13.0±4.4 mGy) with AIDR 3D and a low-dose protocol (CTDI vol : 2.1±0.8 mGy) with AiCE for the detection of the same abdominal lesions and an overall image quality scored acceptable for more than 95% of patients (34).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the TrueFidelity DNN was trained with both patient and phantom data whereas it was trained only with patient data for AiCE. CT images obtained with these algorithms using denoising techniques showed suppressed noise with no change of noise texture or distortion of anatomical and pathological structures (19,(22)(23)(24)(25)(26)(27)(28)(29)(30).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in that same study, only the iterative level "Standard" was assessed and not the three levels available for each IR algorithms (Mild, Standard and Strong). On another note, many clinical studies have shown the impact of AiCE on dose reduction and image quality for different anatomical locations in both adult and pediatric patients (22,(25)(26)(27)29,34,35).…”

Section: Introductionmentioning

confidence: 99%

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Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Greffier¹,

Dabli²,

Hamard³

et al. 2022

Quant Imaging Med Surg

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Background: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm.Methods: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU).Results: NPS peaks were lower with AiCE than with AIDR 3D (−41%±6% for all levels) or FIRST (−15%±6% for Strong level and −41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (−9%±2% using Mild and −3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (−6%±6% and −13%±3%, respectively).For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (−19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; −21%±14%). Using the Standard level, dose could be reduced by −79% for the small calcification and −57% for the large mass using AICE compared to AIDR 3D. Conclusions:The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Greffier¹,

Dabli²,

Hamard³

et al. 2022

Quant Imaging Med Surg

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show abstract

“…In general, the complex algorithms of this technique require more time for image reconstruction. Recent developments have introduced image reconstruction techniques based on deep-learning, allowing to reduce radiation exposure and improving image quality even more while maintaining significant noise reduction [ 33 , 34 ]. Regarding emergency diagnostics, time is one of the most relevant factors, and therefore, deep learning algorithms such as AiCE (Canon Medical Systems, Otawara, Japan) and TrueFidelity™ (GE Healthcare, Chicago, IL, USA) may have the potential to boost diagnostic quality and to reduce reconstruction time simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

et al. 2021

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Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

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“…On the other hand, the TrueFidelity T M system is trained with high-dose filtered-back projection (FBP) images [12]. Several studies have utilized DLR in the abdominal, chest, and brain CT imaging and cardiopulmonary CTA and found better image quality than other image reconstruction algorithms [13][14][15][16][17][18]. No studies so far have investigated the application of AiCE to brain CTA protocols.…”

Section: Introductionmentioning

confidence: 99%

Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction

Otgonbaatar¹,

Ryu²,

Kim³

et al. 2021

Journal of Integrative Neuroscience

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To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions. The subjective image quality score was significantly higher for the deep learning reconstruction than hybrid iterative reconstruction and filtered-back-projection images. The deep learning reconstruction markedly improved the reduction of blooming artifacts in surgical clips and coiled aneurysms. The deep learning reconstruction method generally improves the image quality of brain computed tomography angiography in terms of objective measurement and subjective grading compared with filteredback-projection and hybrid iterative reconstruction. Especially, deep learning reconstruction is deemed advantageous for better depiction of small vessels compared to filtered-back projection and hybrid iterative reconstruction.

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Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Cited by 58 publications

References 23 publications

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction

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