Material classification using basis material decomposition from spectral X-ray CT

Jumanazarov, Doniyor; Alimova, Asalkhon; Abdikarimov, Azamat; Koo, Jakeoung; Poulsen, Henning F.; Olsen, Ulrik L.; Iovea, Mihai

doi:10.1016/j.nima.2023.168637

Cited by 3 publications

(1 citation statement)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Imaging systems using X-rays are essential in the field of diagnostic medical imaging [ 1 , 2 ]. This imaging system uses a detector to acquire information about how X-rays penetrate and attenuate materials (information for linear attenuation coefficient) as a final image [ 3 ]. Because images acquired with X-rays in one direction may result in overlapping structures, computed tomography (CT) using 360-degree projection data and 3D reconstruction is widely used [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

Shim,

Lee

2024

Bioengineering

View full text Add to dashboard Cite

Cinematic rendering (CR) is a new 3D post-processing technology widely used to produce bone computed tomography (CT) images. This study aimed to evaluate the performance quality of CR in bone CT images using blind quality and noise level evaluations. Bone CT images of the face, shoulder, lumbar spine, and wrist were acquired. Volume rendering (VR), which is widely used in the field of diagnostic medical imaging, was additionally set along with CR. A no-reference-based blind/referenceless image spatial quality evaluator (BRISQUE) and coefficient of variation (COV) were used to evaluate the overall quality of the acquired images. The average BRISQUE values derived from the four areas were 39.87 and 46.44 in CR and VR, respectively. The difference between the two values was approximately 1.16, and the difference between the resulting values increased, particularly in the bone CT image, where metal artifacts were observed. In addition, we confirmed that the COV value improved by 2.20 times on average when using CR compared to VR. This study proved that CR is useful in reconstructing bone CT 3D images and that various applications in the diagnostic medical field will be possible.

show abstract

Section: Introductionmentioning

confidence: 99%

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

Shim,

Lee

2024

Bioengineering

View full text Add to dashboard Cite

show abstract

Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning

Weiss,

Meisen

2024

NDT

View full text Add to dashboard Cite

Computed tomography (CT) is a widely utilised imaging technique in both clinical and industrial applications. CT scan results, presented as a volume revealing linear attenuation coefficients, are intricately influenced by scan parameters and the sample’s geometry and material composition. Accurately mapping these coefficients to specific materials is a complex task. Traditionally, material decomposition in CT relied on classical algorithms using handcrafted features based on X-ray physics. However, there is a rising trend towards data-driven approaches, particularly deep learning, which offer promising improvements in accuracy and efficiency. This survey explores the transition from classical to data-driven approaches in material-sensitive CT, examining a comprehensive corpus of literature identified through a detailed and reproducible search using Scopus. Our analysis addresses several key research questions: the origin and generation of training datasets, the models and architectures employed, the extent to which deep learning methods reduce the need for domain-specific expertise, and the hardware requirements for training these models. We explore the implications of these findings on the integration of deep learning into CT practices and the potential reduction in the necessity for extensive domain knowledge. In conclusion, this survey highlights a significant shift towards deep learning in material-resolving CT and discusses the challenges and opportunities this presents. The transition suggests a future where data-driven approaches may dominate, offering enhanced precision and robustness in material-resolving CT while potentially transforming the role of domain experts in the field.

show abstract

X射线ct正交基材料分解成像方法及其在校正金属伪影中的应用

Luo Ting,

Zhao Xing,

Zhao Yunsong

et al. 2024

光学学报

View full text Add to dashboard Cite

Material classification using basis material decomposition from spectral X-ray CT

Cited by 3 publications

References 80 publications

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

No-Reference-Based and Noise Level Evaluations of Cinematic Rendering in Bone Computed Tomography

Reviewing Material-Sensitive Computed Tomography: From Handcrafted Algorithms to Modern Deep Learning

X射线ct正交基材料分解成像方法及其在校正金属伪影中的应用

Contact Info

Product

Resources

About