Automatic image fusion of real-time ultrasound with computed tomography images: a prospective comparison between two auto-registration methods

2017 ◽  
Vol 58 (11) ◽  
pp. 1349-1357 ◽  
Author(s):  
Dong Ik Cha ◽  
Min Woo Lee ◽  
Ah Yeong Kim ◽  
Tae Wook Kang ◽  
Young-Taek Oh ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Fusion ◽  
Real Time ◽  
Ct Images ◽  
Rank Test ◽  
Initial Image ◽  
Major Drawback ◽  
Registration Error ◽  
Computed Tomography Images ◽  
Prospective Comparison

Background A major drawback of conventional manual image fusion is that the process may be complex, especially for less-experienced operators. Recently, two automatic image fusion techniques called Positioning and Sweeping auto-registration have been developed. Purpose To compare the accuracy and required time for image fusion of real-time ultrasonography (US) and computed tomography (CT) images between Positioning and Sweeping auto-registration. Material and Methods Eighteen consecutive patients referred for planning US for radiofrequency ablation or biopsy for focal hepatic lesions were enrolled. Image fusion using both auto-registration methods was performed for each patient. Registration error, time required for image fusion, and number of point locks used were compared using the Wilcoxon signed rank test. Results Image fusion was successful in all patients. Positioning auto-registration was significantly faster than Sweeping auto-registration for both initial (median, 11 s [range, 3–16 s] vs. 32 s [range, 21–38 s]; P < 0.001] and complete (median, 34.0 s [range, 26–66 s] vs. 47.5 s [range, 32–90]; P = 0.001] image fusion. Registration error of Positioning auto-registration was significantly higher for initial image fusion (median, 38.8 mm [range, 16.0–84.6 mm] vs. 18.2 mm [6.7–73.4 mm]; P = 0.029), but not for complete image fusion (median, 4.75 mm [range, 1.7–9.9 mm] vs. 5.8 mm [range, 2.0–13.0 mm]; P = 0.338]. Number of point locks required to refine the initially fused images was significantly higher with Positioning auto-registration (median, 2 [range, 2–3] vs. 1 [range, 1–2]; P = 0.012]. Conclusion Positioning auto-registration offers faster image fusion between real-time US and pre-procedural CT images than Sweeping auto-registration. The final registration error is similar between the two methods.

scholarly journals Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-16
Author(s):  
Xiaowe Xu ◽  
Jiawei Zhang ◽  
Jinglan Liu ◽  
Yukun Ding ◽  
Tianchen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Denoising ◽  
Ct Images ◽  
High Dose ◽  
Ct Image ◽  
Paired Data ◽  
High Quality ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Global Cycle

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

scholarly journals CT and SPECT image fusion using external fiducial markers for detection of the sentinel lymph nodes in breast cancer

2017 ◽  
Vol 4 (2) ◽  
pp. 23
Author(s):  
Kei Haramiishi ◽  
Shinya Nakamura ◽  
Tomoaki Tsuchiya ◽  
Atsushi Fukui ◽  
Midori Matsuyama ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Lymph Nodes ◽  
Image Fusion ◽  
Single Photon ◽  
Photon Emission ◽  
Ct Images ◽  
Fiducial Markers ◽  
Single Photon Emission ◽  
Spect Image

Object: Hybrid single-photon emission computed tomography/computed tomography, which is recently developed, is useful for the sentinel node (SN) mapping in patients with breast cancer. However, this expensive new technology is only available at limited hospitals. The purpose of this study was to assess the feasibility of software-based computed tomography (CT) and single-photon emission tomography (SPECT) image fusion using external fiducial markers for visualization of SNs in breast cancer.Methods: Preoperative lymphoscintigraphy using 99mTc-phytate colloid was performed in 70 consecutive patients (mean age, 55.3 ± 11.8). Continually, SPECT and low-dose chest CT were performed using an 241Am-containing button as an external fiducial marker attached to the skin surface of the patient’s chest wall. The acquired SPECT and CT images were rescaled, interpolated, reformatted, and registered point-by-point on a workstation.Results: SPECT detected SN sites, including axillar (n = 96) and internal mammary lesions (n = 7). On fused images, precise overlap of hot spots shown at the corresponding lymph nodes on CT images was achieved in all but 2 cases. In cases with axillar lesions, rendering the fused images into 3D volumes with accentuation of the pectoralis minor muscle was helpful for diagnosis of SN locations in level II (n = 10). After surgery, all nodes were depicted as “hot nodes” on fused images, and 14 metastatic nodes were confirmed by histological examination.Conclusions: External fiducial-based coregistration of SPECT lymphoscintigraphic and CT images depicted the precise location of SN drainage and may provide useful information for preoperative planning, without the need for hybrid SPECT/CT.

scholarly journals A deep learning approach for dental implant planning in cone-beam computed tomography images

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sevda Kurt Bayrakdar ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Elif Bilgir ◽  
Matvey Ezhov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
San Francisco ◽  
Cone Beam Computed Tomography ◽  
Assessment Method ◽  
Cone Beam ◽  
Rank Test ◽  
Human Observer ◽  
Computed Tomography Images ◽  
Bone Height ◽  
Missing Tooth

Abstract Background The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. Methods Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland–Altman analysis and Wilcoxon signed rank test. Results In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. Conclusions Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

scholarly journals Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Taka-aki Hirose ◽  
Hidetaka Arimura ◽  
Kenta Ninomiya ◽  
Tadamasa Yoshitake ◽  
Jun-ichi Fukunaga ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Radiation Therapy ◽  
Stereotactic Body Radiation Therapy ◽  
Prediction Models ◽  
Ct Images ◽  
Imaging Biomarkers ◽  
Stereotactic Body Radiation ◽  
Computed Tomography Images ◽  
Radiation Induced

AbstractThis study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. For the RP prediction models, 275 non-small-cell lung cancer patients consisted of 245 training (22 with grade ≥ 2 RP) and 30 test cases (8 with grade ≥ 2 RP) were selected. A total of 486 radiomic features were calculated to quantify the RP texture patterns reflecting radiation-induced tissue reaction within lung volumes irradiated with more than x Gy, which were defined as LVx. Ten subsets consisting of all 22 RP cases and 22 or 23 randomly selected non-RP cases were created from the imbalanced dataset of 245 training patients. For each subset, signatures were constructed, and predictive models were built using the least absolute shrinkage and selection operator logistic regression. An ensemble averaging model was built by averaging the RP probabilities of the 10 models. The best model areas under the receiver operating characteristic curves (AUCs) calculated on the training and test cohort for LV5 were 0.871 and 0.756, respectively. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT.

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