Cone Beam CT vs. Fan Beam CT: A Comparison of Image Quality and Dose Delivered Between Two Differing CT Imaging Modalities

Lechuga, Lawrence; Weidlich, Georg A.

doi:10.7759/cureus.778

Cited by 98 publications

(104 citation statements)

References 8 publications

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“…However, a direct comparison is deteriorated by the fact that our test set consists of more than 80% of CBCT scans, whereas the ones presented in literature use only CT scans. Typically, the image quality is worse in terms of contrast-to-noise ratio in CBCT scans compared to CT scans [25], making the segmentation more challenging. The decreased image quality is further likely to increase the interobserver variability, as seen in the comparison between two different CT modalities and MRI [18].…”

Section: Tablementioning

confidence: 99%

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT

Schreier

Genghi

Laaksonen

et al. 2020

Radiotherapy and Oncology

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a b s t r a c tAim: The segmentation of organs from a CT scan is a time-consuming task, which is one hindrance for adaptive radiation therapy. Through deep learning, it is possible to automatically delineate organs. Metrics like dice score do not necessarily represent the impact for clinical practice. Therefore, a clinical evaluation of the deep neural network is needed to verify the segmentation quality. Methods: In this work, a novel deep neural network is trained on 300 CT and 300 artificially generated pseudo CBCTs to segment bladder, prostate, rectum and seminal vesicles from CT and cone beam CT scans. The model is evaluated on 45 CBCT and 5 CT scans through a clinical review performed by three different clinics located in Europe, North America and Australia. Results: The deep learning model is scored either equally good (prostate and seminal vesicles) or better (bladder and rectum) than the structures from routine clinical practice. No or minor corrections are required for 97.5% of the segmentations of the bladder, 91.5% of the prostate, 94% of the rectum and seminal vesicles. Overall, for 82.5% of the patients none of the organs need major corrections or a redraw. Conclusion: This study shows that modern deep neural networks are capable of producing clinically applicable organ segmentation for the male pelvis. The model is able to produce acceptable structures as frequently as current clinical routine. Therefore, deep neural networks can simplify the clinical workflow by offering initial segmentations. The study further shows that to retain the clinicians' personal preferences a structure review and correction is necessary for structures both created by other clinicians and deep neural networks.

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Section: Tablementioning

confidence: 99%

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT

Schreier

Genghi

Laaksonen

et al. 2020

Radiotherapy and Oncology

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“…The success of CBCT is mainly due to a relatively low radiation dose [11], limited metal artefacts [12], high spatial resolution (0.075-0.4 mm isotropic voxel) [13], and low maintenance and operating costs [14]. Nevertheless, CBCT is not used for contrast-enhanced examinations, does not have a high contrast resolution [15], and the scan time is long with non-negligible motion artefacts [16].…”

Section: Introductionmentioning

confidence: 99%

Radiation dose in non-dental cone beam CT applications: a systematic review

Nardi

Salerno

Molteni³

et al. 2018

Radiol med

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“…The use of IV contrast did help with identification of blood vessels within the head and neck region. Although gross soft tissue structures, including large lymph nodes and blood vessels, could be identified, the microCT scans had a much superior soft tissue definition [17]. Furthermore, our CBCT machine is one of the earliest generation scanners (built in 2002).…”

Section: Intraoperative Imaging With Cone Beam Ctmentioning

confidence: 99%

Sentinel lymph node mapping using ICG fluorescence or cone beam CT - a rabbit model feasibility study

Muhanna

Chan

Douglas

et al. 2020

Preprint

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Background Current sentinel lymph node biopsy (SLNB) techniques, including use of radioisotopes, have disadvantages including the use of a radioactive tracer. Indocyanine green (ICG) based near-infrared (NIR) fluorescence imaging and cone beam CT (CBCT) have advantages for intraoperative use. However, limited literature exists regarding their use in head and neck cancer SLNB.Methods This was a prospective, non-randomized study using a rabbit oral cavity VX2 squamous cell carcinoma model which develops lymph node metastasis. Pre-operatively, images were acquired by MicroCT and DCE-MRI. CBCT and NIR fluorescence imaging of ICG was used to map and guide the SLNB resection.Results Intraoperative use of ICG to guide fluorescence resection resulted in identification of all lymph nodes identified by pre-operative CT. CBCT was useful for near real time intraoperative imaging and 3D reconstruction.Conclusions This pre-clinical study further demonstrates the technical feasibility, limitations and advantages of intraoperative NIR-guided ICG imaging for SLN identification as a complementary method during head and neck surgery.

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Cone Beam CT vs. Fan Beam CT: A Comparison of Image Quality and Dose Delivered Between Two Differing CT Imaging Modalities

Cited by 98 publications

References 8 publications

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT

Clinical evaluation of a full-image deep segmentation algorithm for the male pelvis on cone-beam CT and CT

Radiation dose in non-dental cone beam CT applications: a systematic review

Sentinel lymph node mapping using ICG fluorescence or cone beam CT - a rabbit model feasibility study

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