Technical Note: FreeCT_ICD: An open-source implementation of a model-based iterative reconstruction method using coordinate descent optimization for CT imaging investigations

Background In pediatric patients, computed tomography (CT) is important in the medical chain of diagnosing and monitoring various diseases. Because children are more radiosensitive than adults, they require minimal radiation exposure. One way to achieve this goal is to implement new technical solutions, like iterative reconstruction. Purpose To evaluate the potential of a new, iterative, model-based method for reconstructing (IMR) pediatric abdominal CT at a low radiation dose and determine whether it maintains or improves image quality, compared to the current reconstruction method. Material and Methods Forty pediatric patients underwent abdominal CT. Twenty patients were examined with the standard dose settings and 20 patients were examined with a 32% lower radiation dose. Images from the standard examination were reconstructed with a hybrid iterative reconstruction method (iDose4), and images from the low-dose examinations were reconstructed with both iDose4 and IMR. Image quality was evaluated subjectively by three observers, according to modified EU image quality criteria, and evaluated objectively based on the noise observed in liver images. Results Visual grading characteristics analyses showed no difference in image quality between the standard dose examination reconstructed with iDose4 and the low dose examination reconstructed with IMR. IMR showed lower image noise in the liver compared to iDose4 images. Inter- and intra-observer variance was low: the intraclass coefficient was 0.66 (95% confidence interval = 0.60–0.71) for the three observers. Conclusion IMR provided image quality equivalent or superior to the standard iDose4 method for evaluating pediatric abdominal CT, even with a 32% dose reduction.

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Improvements to image quality using hybrid and model-based iterative reconstructions: a phantom study

Acta Radiologica ◽

10.1177/0284185116631180 ◽

2016 ◽

Vol 58 (1) ◽

pp. 53-61 ◽

Cited By ~ 14

Author(s):

Marie-Louise Aurumskjöld ◽

Kristina Ydström ◽

Anders Tingberg ◽

Marcus Söderberg

Keyword(s):

Image Quality ◽

Radiation Dose ◽

Iterative Reconstruction ◽

Power Spectra ◽

Reconstruction Method ◽

Noise Power ◽

Contrast Resolution ◽

Low Contrast ◽

Model Based ◽

Reconstruction Methods

Background The number of computed tomography (CT) examinations is increasing and leading to an increase in total patient exposure. It is therefore important to optimize CT scan imaging conditions in order to reduce the radiation dose. The introduction of iterative reconstruction methods has enabled an improvement in image quality and a reduction in radiation dose. Purpose To investigate how image quality depends on reconstruction method and to discuss patient dose reduction resulting from the use of hybrid and model-based iterative reconstruction. Material and Methods An image quality phantom (Catphan® 600) and an anthropomorphic torso phantom were examined on a Philips Brilliance iCT. The image quality was evaluated in terms of CT numbers, noise, noise power spectra (NPS), contrast-to-noise ratio (CNR), low-contrast resolution, and spatial resolution for different scan parameters and dose levels. The images were reconstructed using filtered back projection (FBP) and different settings of hybrid (iDose4) and model-based (IMR) iterative reconstruction methods. Results iDose4 decreased the noise by 15–45% compared with FBP depending on the level of iDose4. The IMR reduced the noise even further, by 60–75% compared to FBP. The results are independent of dose. The NPS showed changes in the noise distribution for different reconstruction methods. The low-contrast resolution and CNR were improved with iDose4, and the improvement was even greater with IMR. Conclusion There is great potential to reduce noise and thereby improve image quality by using hybrid or, in particular, model-based iterative reconstruction methods, or to lower radiation dose and maintain image quality.

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Generalized deep iterative reconstruction for sparse-view CT imaging

Physics in Medicine and Biology ◽

10.1088/1361-6560/ac3eae ◽

2021 ◽

Author(s):

Ting Su ◽

Zhuoxu Cui ◽

Jiecheng Yang ◽

Yunxin Zhang ◽

Jian Liu ◽

...

Keyword(s):

Iterative Reconstruction ◽

Ct Imaging ◽

Reconstruction Method ◽

Model Driven ◽

Iterative Model ◽

Network Training ◽

Image Reconstruction Method ◽

Reconstruction Performance ◽

Data Fidelity ◽

Mathematical Operations

Abstract Sparse-view CT is a promising approach in reducing the X-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection (FBP) algorithm suffer from severe streaking artifacts. Iterative reconstruction (IR) algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, model-driven deep learning (DL) CT image reconstruction method, which unrolls the iterative optimization procedures into the deep neural network, has shown exciting prospect in improving the image quality and shortening the reconstruction time. In this work, we explore the generalized unrolling scheme for such iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via the network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

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