Optimized Camera-Based Patient Positioning in CT

Manava, Panagiota; Galster, Marco; Ammon, Josefin; Singer, Julian M.; Lell, Michael; Rieger, Vera

doi:10.1097/rli.0000000000000904

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Automatic exposure control can increase the applied radiation exposure in off-centreed patients, due to the shape of the bowtie filter, if a real-time angular tube current modulation is available. Some vendors already offer camera systems that automatically position the patient inside the scanner gantry, based on the patient shape and contours using anatomical landmarks [11,17,18]. The optimal table height for the desired scan region is automatically calculated.…”

Section: Discussionmentioning

confidence: 99%

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Influence of tube and patient positioning in thoracoabdominal CT examinations on radiation exposure–towards a better patient positioning

Valentin,

Kamp,

Henke

et al. 2023

J. Radiol. Prot.

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Objectives: Although iso-centric patient positioning is enormously important in CT, it is complicated in thoracoabdominal imaging by the varying dimensions of the body. Patient positioning can affect the appearance of the patient on the localizer. Positioned too close to the x-ray tube, a patient appears considerably more voluminous. The goal of this study is to assess the difference in radiation exposure of combined chest and abdomen CT scans between scans with prior 0°- and 180°-localizers in conjunction with patient positioning. Materials and Methods: In this IRB-approved retrospective study, patients who had two routine thoracoabdominal CT scans on the same CT scanner, one with a prior 0°- and one with a prior 180°-localizer, were included. To evaluate the radiation exposure of the thoracoabdominal CT examination regarding the tube position during the localizer, CTDIvol, SSDE, patient diameter and positioning within the iso-center for three positions (heart, abdomen, femur level) were compared with regard to the tube position during the prior localizer. Results: CT examinations of 114 patients were included. Despite similar patient weight and diameter between the two examinations, SSDE and CTDIvol was significantly larger (up to 73%) with 180°-localizers. Patient offset from the iso-center ranged between -9 mm at the center slice (abdomen level) to -43 mm at the most caudal slice at the pelvis (femur level), causing a significant magnification (p<0.001) on 180°-localizers with a subsequent increase of the apparent attenuation. Conclusions: The results of this study emphasize the use of 0°-localizers in thoracoabdominal CTs, since 180°-localizers caused patient magnification with subsequent increase in radiation exposure. The advantage of 180°-localizers, namely reducing the dose in thyroid and breast, is eliminated if the dose of the CT scan increases significantly in the abdomen and pelvis. 

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Section: Discussionmentioning

confidence: 99%

“…However, it is questionable how well this works on a day-to-day basis when patients (especially ICU patients) are placed on the bed with foreign material and blankets. In the study of Manava et al, patients were examined positioned using a camera system, however, only patients without blankets were included [17].…”

Section: Discussionmentioning

confidence: 99%

Influence of tube and patient positioning in thoracoabdominal CT examinations on radiation exposure–towards a better patient positioning

Valentin,

Kamp,

Henke

et al. 2023

J. Radiol. Prot.

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“…Optical and depth information are processed to automatically adjust the table height to match the isocenter of the patient with the isocenter of the scanner and to support the definition of the scan range. [30][31][32][33] Manava et al 31 analyzed data from 3118 patients who underwent CT of the chest and abdomen: 1575 patients were positioned laser-guided by a radiographer, 1561 patients with 3D camera guidance. Although the radiographers were highly trained, isocenter positioning was significantly improved with the 3D camera.…”

Section: Camera-based Patient Positioningmentioning

confidence: 99%

Computed Tomography 2.0

Lell

Kachelrieß

2023

Invest Radiol

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Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

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“…[20][21][22] The second group comprises camera-based systems that use artificial intelligence to guide patient positioning before exams. [23][24][25] Philips, GE, and Siemens have developed software such as Philips Precise Position AI-enabled camera and Siemens YSIO X.pree for this purpose.…”

Section: Introductionmentioning

confidence: 99%

“…These software can identify suboptimal breath‐holding, outline areas of the chest anatomy that may have been collimated off the image, and suggest adjustments for proper rotation 20–22 . The second group comprises camera‐based systems that use artificial intelligence to guide patient positioning before exams 23–25 . Philips, GE, and Siemens have developed software such as Philips Precise Position AI‐enabled camera and Siemens YSIO X.pree for this purpose.…”

Section: Introductionmentioning

confidence: 99%

A semi‐supervised learning‐based quality evaluation system for digital chest radiographs

Wei,

Qiu,

et al. 2023

Medical Physics

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BackgroundDigital radiography is the most commonly utilized medical imaging technique worldwide, and the quality of radiographs plays a crucial role in accurate disease diagnosis. Therefore, evaluating the quality of radiographs is an essential step in medical examinations. However, manual evaluation can be time‐consuming, labor‐intensive, and prone to interobserver differences, making it less reliable.PurposeTo alleviate the workload of radiographic technologists and enhance the efficiency of radiograph quality evaluation, it is crucial to develop rapid and reliable quality evaluation methods and establish a set of quantitative evaluation standards. To address this, we have proposed a quality evaluation system for digital radiographs that utilizes deep learning techniques to achieve fast and precise evaluation.MethodsThe evaluation of frontal chest radiograph quality involves assessing patient positioning through semantic segmentation and foreign body detection. For lung, scapula, and clavicle segmentation in digital chest radiographs, a residual connection‐based convolutional neural network π‐ResUNet, was proposed. Criteria for patient positioning evaluation were established based on the segmentation and manual evaluation results. A convolutional neural network, FasterRCNN, was utilized to detect and localize foreign bodies in digital chest radiographs. To enhance the performance of both neural networks, a semi‐supervised learning (SSL) strategy was implemented by incorporating a consistency loss that leverages a large number of unlabeled digital radiographs. We also trained the network using the fully supervised learning (FSL) strategy and compared their performance on the test set. The ChestXRay‐14 and object‐CXR datasets were used throughout the process.ResultsBy comparing with the manual annotation, the proposed network, trained using the SSL method, achieved a high Dice similarity coefficient (DSC) of 0.96, 0.88, and 0.88 for lung, scapula, and clavicle segmentation, respectively, outperforming the network trained with the FSL method. In addition, for foreign body detection, the proposed SSL method was superior to the FSL method, achieving an AUC (Area under receiver operating characteristic curve, Area under ROC curve) of 0.90 and an FROC (Free‐response ROC) of 0.77 on the test dataset.ConclusionsThe experimental results show that our proposed system is well‐suited for radiograph quality evaluation, with the semi‐supervised learning method further improving the network's performance. The proposed method can evaluate the quality of a chest radiograph from two aspects—patient positioning and foreign body detection—within 1 s, offering a promising tool in radiograph quality evaluation.

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Optimized Camera-Based Patient Positioning in CT

Cited by 9 publications

References 29 publications

Influence of tube and patient positioning in thoracoabdominal CT examinations on radiation exposure–towards a better patient positioning

Influence of tube and patient positioning in thoracoabdominal CT examinations on radiation exposure–towards a better patient positioning

Computed Tomography 2.0

A semi‐supervised learning‐based quality evaluation system for digital chest radiographs

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