Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

Greffier, Joël; Durand, Quentin; Frandon, Julien; Loisy, Maeliss; Oliveira, Fabien de; Beregi, Jean‐Paul; Dabli, Djamel

doi:10.1007/s00330-022-09003-y

Cited by 26 publications

(22 citation statements)

References 33 publications

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“…The outcomes of task-based image quality assessment confirmed the results found on conventional CT images with IR algorithms [5][6][7][19][20][21][22]24]. The NPS results showed that, for the 3 CBCT acquisition modes evaluated, the noise magnitude decreased with IR compared to FBP but the NPS curves shifted towards lower frequencies, resulting in more pronounced image smoothing.…”

Section: Discussionsupporting

confidence: 71%

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Task-Based Image Quality Assessment Comparing Classical and Iterative Cone Beam CT Images on Halcyon®

et al. 2023

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Background: Despite the development of iterative reconstruction (IR) in diagnostic imaging, CBCT are generally reconstructed with filtered back projection (FBP) in radiotherapy. Varian medical systems, recently released with their latest Halcyon® V2.0 accelerator, a new IR algorithm for CBCT reconstruction. Purpose: To assess the image quality of radiotherapy CBCT images reconstructed with FBP and an IR algorithm. Methods: Three CBCT acquisition modes (head, thorax and pelvis large) available on a Halcyon® were assessed. Five acquisitions were performed for all modes on an image quality phantom and reconstructed with FBP and IR. Task-based image quality assessment was performed with noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d’). To illustrate the image quality obtained with both reconstruction types, CBCT acquisitions were made on 6 patients. Results: The noise magnitude and the spatial frequency of the NPS peak was lower with IR than with FBP for all modes. For all low and high-contrast inserts, the values for TTF at 50% were higher with IR than with FBP. For all inserts and all modes, the contrast values were similar with FBP and IR. For all low and high-contrast simulated lesions, d’ values were higher with IR than with FBP for all modes. These results were also found on the 6 patients where the images were less noisy but smoother with IR-CBCT. Conclusions: Using the IR algorithm for CBCT images in radiotherapy improve image quality and thus could increase the accuracy of online registration and limit positioning errors during processing.

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Section: Discussionsupporting

confidence: 71%

“…The reconstructed images were analyzed using iQMetrix-CT software [18], with which a task-based image quality assessment can be made by computing the TTF, the NPS and the detectability index. The methodology used by this software is well-known [18] and has been cited in several publications [19][20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Task-Based Image Quality Assessment Comparing Classical and Iterative Cone Beam CT Images on Halcyon®

et al. 2023

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“…This CT system is equipped with the fourth generation of the hybrid IR algorithm iDose (iDose 4 ) and the Precise Image DLR algorithm. The characteristics and overall working principle of this new DLR algorithm have already been defined in several studies [ 4 , 19 , 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…Studies performed on the first two DLR algorithms have shown a better detection of these lesions than when using an IR algorithm at the same dose level [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ] or at a lower dose level [ 15 , 16 , 17 , 18 ]. Recently, Philips Healthcare also developed an artificial intelligence DLR algorithm called Precise Image [ 4 , 19 , 20 ]. The algorithm uses a convolutional neural network (CNN), a subtype of the DNN in which each layer performs a convolution operation.…”

Section: Introductionmentioning

confidence: 99%

“…According to the manufacturer, the CNN was validated by comparing low-dose images generated by AI-DLR with routine-dose images reconstructed using standard methods. Preliminary phantom studies using the Precise Image algorithm have shown that abdomen–pelvic CT examinations are optimized without modifying the image texture [ 20 ]. Radiologists have validated the overall quality of the abdominal images for a CT dose index volume (CTDI vol ) of close to 6 mGy using the Smooth level of this algorithm.…”

Section: Introductionmentioning

confidence: 99%

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First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

et al. 2023

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The study’s aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDIvol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (−26 ± 10%; p < 0.01) and from Smooth to Smoother (−37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

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Image quality evaluation in deep‐learning‐based CT noise reduction using virtual imaging trial methods: Contrast‐dependent spatial resolution

Zhou,

Gong,

Hsieh

et al. 2024

Medical Physics

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BackgroundDeep‐learning‐based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images.PurposeIn this work, we aim to develop a patient‐data‐based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method.MethodsThe patient‐data‐based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR‐processed images from projection or image data; (4) creation of ensembles of DLIR‐processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (−500, −100, −50, −20, −10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered‐backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN‐weak, DCNN‐medium, and DCNN‐strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion‐present images from the lesion absent images. Modulation transfer functions (MTFs) both in‐plane and along the z‐axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t‐test was used to compare the z‐axis and in‐plane spatial resolution of five algorithms for five different contrasts and three dose levels.ResultsThe in‐plane and z‐axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in‐plane and z‐axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (−10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in‐plane and z‐axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of −100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in‐plane and cross‐plane spatial resolution: FBP > DCNN‐Weak > IR > DCNN‐Medium > DCNN‐Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels.ConclusionsA patient‐data‐based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non‐linear image reconstruction and post‐processing techniques, the evaluated DCNN method degraded the in‐plane and z‐axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

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Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

Cited by 26 publications

References 33 publications

Task-Based Image Quality Assessment Comparing Classical and Iterative Cone Beam CT Images on Halcyon®

Task-Based Image Quality Assessment Comparing Classical and Iterative Cone Beam CT Images on Halcyon®

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

Image quality evaluation in deep‐learning‐based CT noise reduction using virtual imaging trial methods: Contrast‐dependent spatial resolution

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