Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning

Jang, Seokhwan; Kim, Seungeon; Kim, Mina; Son, Kyu-Yeol; Lee, Kyoung‐Yong; Beom, Jong

doi:10.1109/tmi.2019.2953974

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…As resolution drastically improves, the sensitivity to patient motion and geometric misalignment becomes high and can be the limiting factor of image resolution. This increased sensitivity also challenges the assumption of smooth patient movement across views [279]- [281].…”

Section: Image Generation For Clinical Applicationsmentioning

confidence: 99%

Systematic Review on Learning-Based Spectral CT

Bousse,

Kandarpa,

Rit

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

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Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

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Section: Image Generation For Clinical Applicationsmentioning

confidence: 99%

Systematic Review on Learning-Based Spectral CT

Bousse,

Kandarpa,

Rit

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

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“…According to the Image Biomarker Standardization Initiative (IBSI), data from different modalities may fit different methods for image interpolation [20]. Motion correction is an approach to remove the motion artefacts caused by uncertain motion, which is also a vital preprocessing step to obtain reconstructed images with significantly improved quality [21].…”

Section: Imaging Preprocessingmentioning

confidence: 99%

A review of radiomics and genomics applications in cancers: the way towards precision medicine

Zhou

2022

Radiat Oncol

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The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

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“…Autofocus methods have demonstrated successful estimation of rigid and multi-body rigid motion in CBCT for musculoskeletal imaging, 24,25 neuroimaging, [25][26][27] and cardiac applications. [28][29][30] Furthermore, recent work extended the application of autofocus to estimation of deformable abdominal motion via a multi-region approach under assumptions of local motion stationarity. 11 The performance of autofocus methods has been proven highly dependent on the suitability of the autofocus metric to the imaging task, anatomical site, and the severity and appearance of motion artifacts.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, estimation of motion in interventional CBCT can be achieved with autofocus methods, in which numerical optimization is used to search for motion trajectories that optimize an autofocus metric of the reconstructed volume associated with motion‐free images. Autofocus methods have demonstrated successful estimation of rigid and multi‐body rigid motion in CBCT for musculoskeletal imaging, 24,25 neuroimaging, 25–27 and cardiac applications 28–30 . Furthermore, recent work extended the application of autofocus to estimation of deformable abdominal motion via a multi‐region approach under assumptions of local motion stationarity 11 …”

Section: Introductionmentioning

confidence: 99%

Deformable motion compensation in interventional cone‐beam CT with a context‐aware learned autofocus metric

Huang,

Liu,

Siewerdsen

et al. 2024

Medical Physics

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PurposeInterventional Cone‐Beam CT (CBCT) offers 3D visualization of soft‐tissue and vascular anatomy, enabling 3D guidance of abdominal interventions. However, its long acquisition time makes CBCT susceptible to patient motion. Image‐based autofocus offers a suitable platform for compensation of deformable motion in CBCT, but it relies on handcrafted motion metrics based on first‐order image properties and that lack awareness of the underlying anatomy. This work proposes a data‐driven approach to motion quantification via a learned, context‐aware, deformable metric, , that quantifies the amount of motion degradation as well as the realism of the structural anatomical content in the image.MethodsThe proposed was modeled as a deep convolutional neural network (CNN) trained to recreate a reference‐based structural similarity metric—visual information fidelity (VIF). The deep CNN acted on motion‐corrupted images, providing an estimation of the spatial VIF map that would be obtained against a motion‐free reference, capturing motion distortion, and anatomic plausibility. The deep CNN featured a multi‐branch architecture with a high‐resolution branch for estimation of voxel‐wise VIF on a small volume of interest. A second contextual, low‐resolution branch provided features associated to anatomical context for disentanglement of motion effects and anatomical appearance. The deep CNN was trained on paired motion‐free and motion‐corrupted data obtained with a high‐fidelity forward projection model for a protocol involving 120 kV and 9.90 mGy. The performance of was evaluated via metrics of correlation with ground truth and with the underlying deformable motion field in simulated data with deformable motion fields with amplitude ranging from 5 to 20 mm and frequency from 2.4 up to 4 cycles/scan. Robustness to variation in tissue contrast and noise levels was assessed in simulation studies with varying beam energy (90–120 kV) and dose (1.19–39.59 mGy). Further validation was obtained on experimental studies with a deformable phantom. Final validation was obtained via integration of on an autofocus compensation framework, applied to motion compensation on experimental datasets and evaluated via metric of spatial resolution on soft‐tissue boundaries and sharpness of contrast‐enhanced vascularity.ResultsThe magnitude and spatial map of showed consistent and high correlation levels with the ground truth in both simulation and real data, yielding average normalized cross correlation (NCC) values of 0.95 and 0.88, respectively. Similarly, achieved good correlation values with the underlying motion field, with average NCC of 0.90. In experimental phantom studies, properly reflects the change in motion amplitudes and frequencies: voxel‐wise averaging of the local across the full reconstructed volume yielded an average value of 0.69 for the case with mild motion (2 mm, 12 cycles/scan) and 0.29 for the case with severe motion (12 mm, 6 cycles/scan). Autofocus motion compensation using resulted in noticeable mitigation of motion artifacts and improved spatial resolution of soft tissue and high‐contrast structures, resulting in reduction of edge spread function width of 8.78% and 9.20%, respectively. Motion compensation also increased the conspicuity of contrast‐enhanced vascularity, reflected in an increase of 9.64% in vessel sharpness.ConclusionThe proposed , featuring a novel context‐aware architecture, demonstrated its capacity as a reference‐free surrogate of structural similarity to quantify motion‐induced degradation of image quality and anatomical plausibility of image content. The validation studies showed robust performance across motion patterns, x‐ray techniques, and anatomical instances. The proposed anatomy‐ and context‐aware metric poses a powerful alternative to conventional motion estimation metrics, and a step forward for application of deep autofocus motion compensation for guidance in clinical interventional procedures.

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Head Motion Correction Based on Filtered Backprojection in Helical CT Scanning

Cited by 8 publications

References 22 publications

Systematic Review on Learning-Based Spectral CT

Systematic Review on Learning-Based Spectral CT

A review of radiomics and genomics applications in cancers: the way towards precision medicine

Deformable motion compensation in interventional cone‐beam CT with a context‐aware learned autofocus metric

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