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[Performance of low-dose CT image reconstruction for detecting intracerebral hemorrhage: selection of dose, algorithms and their combinations].
Fu, S; Li, M; Bian, Z; Ma, J.
Afiliación
  • Fu S; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Li M; Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Guangzhou 510515, China.
  • Bian Z; Pazhou Lab, Guangzhou 510515, China.
  • Ma J; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao ; 42(2): 223-231, 2022 Feb 20.
Article en Zh | MEDLINE | ID: mdl-35365446
ABSTRACT

OBJECTIVE:

To investigate the performance of different low-dose CT image reconstruction algorithms for detecting intracerebral hemorrhage.

METHODS:

Low-dose CT imaging simulation was performed on CT images of intracerebral hemorrhage at 30%, 25% and 20% of normal dose level (defined as 100% dose). Seven algorithms were tested to reconstruct low-dose CT images for noise suppression, including filtered back projection algorithm (FBP), penalized weighted least squares-total variation (PWLS-TV), non-local mean filter (NLM), block matching 3D (BM3D), residual encoding-decoding convolutional neural network (REDCNN), the FBP convolutional neural network (FBPConvNet) and image restoration iterative residual convolutional network (IRLNet). A deep learning-based model (CNN-LSTM) was used to detect intracerebral hemorrhage on normal dose CT images and low-dose CT images reconstructed using the 7 algorithms. The performance of different reconstruction algorithms for detecting intracerebral hemorrhage was evaluated by comparing the results between normal dose CT images and low-dose CT images.

RESULTS:

At different dose levels, the low-dose CT images reconstructed by FBP had accuracies of detecting intracerebral hemorrhage of 82.21%, 74.61% and 65.55% at 30%, 25% and 20% dose levels, respectively. At the same dose level (30% dose), the images reconstructed by FBP, PWLS-TV, NLM, BM3D, REDCNN, FBPConvNet and IRLNet algorithms had accuracies for detecting intracerebral hemorrhage of 82.21%, 86.80%, 89.37%, 81.43%, 90.05%, 90.72% and 93.51%, respectively. The images reconstructed by IRLNet at 30%, 25% and 20% dose levels had accuracies for detecting intracerebral hemorrhage of 93.51%, 93.51% and 93.06%, respectively.

CONCLUSION:

The performance of reconstructed low-dose CT images for detecting intracerebral hemorrhage is significantly affected by both dose and reconstruction algorithms. In clinical practice, choosing appropriate dose level and reconstruction algorithm can greatly reduce the radiation dose and ensure the detection performance of CT imaging for intracerebral hemorrhage.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Tomografía Computarizada por Rayos X Límite: Humans Idioma: Zh Revista: Nan Fang Yi Ke Da Xue Xue Bao Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Tomografía Computarizada por Rayos X Límite: Humans Idioma: Zh Revista: Nan Fang Yi Ke Da Xue Xue Bao Año: 2022 Tipo del documento: Article País de afiliación: China
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