Accuracy of percutaneous soft-tissue interventions using a multi-axis, C-arm CT system and 3D laser guidance

Kostrzewa, Michael; Rathmann, Nils; Kara, Kerim; Schoenberg, Stefan O.; Diehl, Steffen J.

doi:10.1016/j.ejrad.2015.06.028

Cited by 13 publications

(14 citation statements)

References 19 publications

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“…These markers were used for a rigid point based registration performed with a localized pointing device 61 . CT scanner was employed to acquire preregistered images for clinical use 61 . AlZu'bi et al proposed 62 a 3D segmentation system that used this phantom data set to validate their proposed system.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Von Berg et al 59 compared between reformatted CT slice and ultrasound image in real time by overlaying both kinds of information directly (Figure 11). 61 Some preprocessing steps were applied here to visualize the output in a good manner. The root mean square of all Cartesian distances between corresponding positions was taken as measure to compare two registration approaches (eg, manual and automatic).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

AlZu’bi

Al-Qatawneh

Elbes

et al. 2019

Concurrency and Computation

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Acceptable error rate, low quality assessment, and time complexity are the major problems in image segmentation, which needed to be discovered. A variety of acceleration techniques have been applied and achieve real time results, but still limited in 3D. HMM is one of the best statistical techniques that played a significant rule recently. The problem associated with HMM is time complexity, which has been resolved using different accelerator. In this research, we propose a methodology for transferring HMM matrices from image to another skipping the training time for the rest of the 3D volume. One HMM train is generated and generalized to the whole volume. The concepts behind multi-orientation geometrical segmentation has been employed here to improve the quality of HMM segmentation. Axial, saggital, and coronal orientations have been considered individually and together to achieve accurate segmentation results in less processing time and superior quality in the detection accuracy. KEYWORDS hidden Markov model, image segmentation, medical imaging, transfer learning, 3D volumes 1 INTRODUCTION Nowadays, segmentation is one of the key problems in the field of computer vision. The understanding of a scene is considered to be the main problem of different computer-based applications that nourish from inferring knowledge from imagery, such as self-driving vehicles, human-computer interaction, virtual reality, medical applications, and many others. The growing interest of deep learning has added an enormous boost to the already rapidly developing field of computer vision where many semantic segmentation problems are being tackled using deep architectures. In the early stages of this century, civilization became more picture based. The employment of image processing and analysis is widely broadcast in many activities more than ever been. The expansion of social networks and due to globalization mechanisms helps in employing image processing analysis in this era. 1 Computer-aided diagnosis (CAD) systems are computer software, which have been mainly implemented for helping specialists doing their jobs; many applications have employed computer systems for better quality such as those in other works. 2-5 These software techniques improve diagnosing medical data. Big data and high-speed processing of this data helps the physician reach an impressive and high-quality diagnosis in real time with the mentioned software techniques or systems from possibly different sources. 6 Image segmentation plays an important role in both natural and medical analysis. 7 It is the process of segmenting or partitioning a digital image into a set of separated regions according to some connected parts. 8,9 Volume segmentation can be defined as declaring the voxels, which comprise the contour edges and the volume of objects of interest. Many techniques can be employed into segmentation. Those techniques could not be a portion of image processing, such as mathematical morphology and watershed 10,11 hard or fuzzy clustering, 12-14 fuzzy connectedness...

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

AlZu’bi

Al-Qatawneh

Elbes

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

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“…In recent years, robotic C-arm CT-guided interventions have been used more frequently because of the improving image quality of flat-panel CT and the availability of threedimensional mapping and navigation applications. A robotic C-arm CT interventional suite can provide real-time visualization for transthoracic needle biopsy (TNB) and more flexibility of the detector system around the patient compared with conventional gantry-based, multidetector CT (MDCT) (21)(22)(23)(24). Furthermore, the patient has not to be repositioned and the system complies even with orthopedic hygienic standards.…”

Section: Discussionmentioning

confidence: 99%

“…Rotolo et al (21) evaluated 324 TNBs using either C-arm CT or MDCT and demonstrated that C-arm CTguided TNB provided high and comparable diagnostic accuracy (96% vs. 94%) with a similar radiation dose. However, MDCT guidance is only possible in an axial plane, thus making off-plane interventions more difficult and time consuming, whereas, the C-arm CT system is not restricted to a single plane (22). Thus, the C-arm CT guidance enables better angle flexibility for nodule localization, especially in decubitus position.…”

Section: Discussionmentioning

confidence: 99%

Image-guided thoracoscopic surgery with dye localization in a hybrid operating room

Yang

Lin

et al. 2016

J. Thorac. Dis.

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“…However, it can be technically challenging because of the deep subphrenic location and the presence of vital structures in the needle path, especially in the case of small masses (7). A new real-time stereotactic needle guidance technique, three-dimensional cone-beam CT (3D CBCT), uses a combination of CBCT and real-time fluoroscopy in the angiography suite (8,9). Compared to conventional CT-guided biopsy, CBCT guidance offers a more sterile workspace and better angulation/rotation, making it easier to biopsy hard-to-reach AGMs.…”

Section: Introductionmentioning

confidence: 99%

C-arm cone-beam computed tomography with stereotactic needle guidance for percutaneous adrenal biopsy: initial experience

Jiao

Xie

et al. 2016

Acta Radiol

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Background Metastasis to the adrenal glands is frequent in patients with various cancers and adrenal gland biopsy is routinely performed using ultrasound or computed tomographic (CT) guidance. However, this method is technically challenging, especially in the case of small masses. Purpose To determine whether the new real-time stereotactic needle guidance technique C-arm cone-beam CT (CBCT) allows safe and accurate biopsy of adrenal gland masses, especially those in hard-to-reach anatomical locations. Material and Methods CBCT guidance was used to perform 60 stereotactic biopsy procedures of lesions that were inaccessible with ultrasound or CT guidance. The needle path was carefully planned and calculated on the CBCT virtual navigation guidance system, which acquired 3D CT-like cross-sectional images. The adrenal biopsy procedures were performed with fluoroscopic feedback. Technical success rate, sensitivity, specificity, accuracy, and complications were investigated. Results The technical success rate of adrenal biopsy under CBCT virtual navigation was 100%, with a mean total procedure time of 14.6 ± 3.6 min. Of the 60 lesions, 46 were malignant, 11 were benign, and three were non-diagnostic. The three non-diagnostic lesions proved to be malignant. Thus, the sensitivity, specificity, and accuracy were 93.8%, 100%, and 95.0%, respectively. Minor bleeding occurred in two (3.3%) cases. Conclusion CBCT guidance allows safe and accurate biopsy of adrenal gland masses and may be especially useful for hard-to-reach anatomical locations.

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Accuracy of percutaneous soft-tissue interventions using a multi-axis, C-arm CT system and 3D laser guidance

Cited by 13 publications

References 19 publications

Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

Transferable HMM probability matrices in multi‐orientation geometric medical volumes segmentation

Image-guided thoracoscopic surgery with dye localization in a hybrid operating room

C-arm cone-beam computed tomography with stereotactic needle guidance for percutaneous adrenal biopsy: initial experience

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