Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm

Dual-Source Photon-Counting Computed Tomography-Part III: Clinical Overview of Vascular Applications beyond Cardiac and Neuro Imaging

Photon-counting computed tomography (PCCT) is an emerging technology that is expected to radically change clinical CT imaging. PCCT offers several advantages over conventional CT, which can be combined to improve and expand the diagnostic possibilities of CT angiography. After a brief description of the PCCT technology and its main advantages we will discuss the new opportunities brought about by PCCT in the field of vascular imaging, while addressing promising future clinical scenarios.

Hepatic vessels segmentation using deep learning and preprocessing enhancement

PURPOSE

Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment.

METHODS

Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied.

RESULTS

The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%.

CONCLUSIONS

The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.

Treatment of hepatic venous system hemorrhage and carbon dioxide gas embolization during laparoscopic hepatectomy via hepatic vein approach

With the improvement of laparoscopic surgery, the feasibility and safety of laparoscopic hepatectomy have been affirmed, but intraoperative hepatic venous system hemorrhage and carbon dioxide gas embolism are the difficulties in laparoscopic hepatectomy. The incidence of preoperative hemorrhage and carbon dioxide gas embolism could be reduced through preoperative imaging evaluation, reasonable liver blood flow blocking method, appropriate liver-breaking device, controlled low-center venous pressure technology, and fine-precision precision operation. In the case of blood vessel rupture bleeding in the liver vein system, after controlling and reducing bleeding, confirm the type and severity of vascular damage in the liver and venous system, take appropriate measures to stop the bleeding quickly and effectively, and, if necessary, transfer the abdominal treatment in time. In addition, to strengthen the understanding, prevention and emergency treatment of severe CO2 gas embolism in laparoscopic hepatectomy is also the key to the success of surgery. This study aims to investigate the methods to deal with hepatic venous system hemorrhage and carbon dioxide gas embolization based on author's institutional experience and relevant literature. We retrospectively analyzed the data of 60 patients who received laparoscopic anatomical hepatectomy of hepatic vein approach for HCC. For patients with intraoperative complications, corresponding treatments were given to cope with different complications. After the operation, combined with clinical experience and literature, we summarized and discussed the good treatment methods in the face of such situations so that minimize the harm to patients as much as possible.

Techniques and Algorithms for Hepatic Vessel Skeletonization in Medical Images: A Survey

Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These methods are classified into five categories according to their methodological characteristics. The overview and brief assessment of each category are provided in the corresponding chapters, respectively. We provide a comprehensive summary among the cited publications, image modalities and datasets from various aspects, which hope to reveal the pros and cons of every method, summarize its achievements and discuss the challenges and future trends.

Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

Application of three-dimensional reconstruction and virtual reality technology in liver surgery

After three-dimensional (3D) reconstruction of the two-dimensional information obtained from routine computed tomography or magnetic resonance imaging examinations of the liver using software, surgeons can examine the volume of the liver, anatomical variation, the course of intrahepatic vessels, the location of the tumor, and its relationship with the surrounding vessels more intuitively, vividly, and from multiple angles. Preoperative 3D reconstruction and virtual reality technology can realize the measurement of liver volume and the implementation of simulated hepatectomy, which can further clarify the scope of surgical resection and ensure the residual liver volume and function to meet the needs of patients after operation. The virtual operation and image navigation before and during the operation can also prevent the injury to important blood vessels and bile ducts in the liver during the operation, significantly shorten the operation time, reduce the bleeding during the operation, and reduce the occurrence of complications such as liver dysfunction, bile leakage, and bleeding after the operation.

Probability density function based modeling of spatial feature variation in capsule endoscopy data for automatic bleeding detection

Wireless capsule endoscopy (WCE) is a video technology to inspect abnormalities, like bleeding in the gastrointestinal tract. In order to avoid a complex and long duration manual review process, automatic bleeding detection schemes are developed that mainly utilize features extracted from WCE images. In feature-based bleeding detection schemes, either global features are used which produce averaged characteristics ignoring the effect of smaller bleeding regions or local features are utilized that cause large feature dimension. In this paper, pixels of interest (POI) in a given WCE image are determined using a linear separation scheme, local spatial features are then extracted from the POI and finally, a suitable characteristic probability density function (PDF) is fitted over the resulting feature space. The proposed PDF model fitting based approach not only reduces the computational complexity but also offers more consistent representation of a class. Details analysis are carried out to find the best suitable PDF and it is found that fitting of Rayleigh PDF model to the local spatial features is best suited for bleeding detection. For the purpose of classification, the fitted PDF parameters are used as features in the supervised support vector machine classifier. Pixels residing in the close vicinity of the POI are further classified with the help of an unsupervised clustering-based scheme to extract more precise bleeding regions. A large number of WCE images obtained from 30 publicly available WCE videos are used for performance evaluation of the proposed scheme and the effects on classification performance due to the changes in PDF models, block statistics, color spaces, and classifiers are experimentally analyzed. The proposed scheme shows satisfactory performance in terms of sensitivity (97.55%), specificity (96.59%) and accuracy (96.77%) and the results obtained by the proposed method outperforms the results reported for some state-of-the-art methods.

An overview on 3D printing for abdominal surgery

BACKGROUND

Three-dimensional (3D) printing is a disruptive technology that is quickly spreading to many fields, including healthcare. In this context, it allows the creation of graspable, patient-specific, anatomical models generated from medical images. The ability to hold and show a physical object speeds up and facilitates the understanding of anatomical details, eases patient counseling and contributes to the education and training of students and residents. Several medical specialties are currently exploring the potential of this technology, including general surgery.

METHODS

In this review, we provide an overview on the available 3D printing technologies, together with a systematic analysis of the medical literature dedicated to its application for abdominal surgery. Our experience with the first clinical laboratory for 3D printing in Italy is also reported.

RESULTS

There was a tenfold increase in the number of publications per year over the last decade. About 70% of these papers focused on kidney and liver models, produced primarily for pre-interventional planning, as well as for educational and training purposes. The most used printing technologies are material jetting and material extrusion. Seventy-three percent of publications reported on fewer than ten clinical cases.

CONCLUSION

The increasing application of 3D printing in abdominal surgery reflects the dawn of a new technology, although it is still in its infancy. The potential benefit of this technology is clear, however, and it may soon lead to the development of new hospital facilities to improve surgical training, research, and patient care.

Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme

BACKGROUND

Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task.

METHODS

We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations.

RESULTS

We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results.

CONCLUSIONS

We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space

Wireless capsule endoscopy (WCE) is an effective video technology to diagnose gastrointestinal (GI) disease, such as bleeding. In order to avoid conventional tedious and risky manual review process of long duration WCE videos, automatic bleeding detection schemes are getting importance. In this paper, to investigate bleeding, the analysis of WCE images is carried out in normalized RGB color space as human perception of bleeding is associated with different shades of red. In the proposed method, at first, from the WCE image frame, an efficient region of interest (ROI) is extracted based on interplane intensity variation profile in normalized RGB space. Next, from the extracted ROI, the variation in the normalized green plane is presented with the help of histogram. Features are extracted from the proposed normalized green plane histograms. For classification purpose, the K-nearest neighbors classifier is employed. Moreover, bleeding zones in a bleeding image are extracted utilizing some morphological operations. For performance evaluation, 2300 WCE images obtained from 30 publicly available WCE videos are used in a tenfold cross-validation scheme and the proposed method outperforms the reported four existing methods having an accuracy of 97.86%, a sensitivity of 95.20%, and a specificity of 98.32%.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

Do 3D Printing Models Improve Anatomical Teaching About Hepatic Segments to Medical Students? A Randomized Controlled Study

BACKGROUND

It is a difficult and frustrating task for young surgeons and medical students to understand the anatomy of hepatic segments. We tried to develop an optimal 3D printing model of hepatic segments as a teaching aid to improve the teaching of hepatic segments.

METHODS

A fresh human cadaveric liver without hepatic disease was CT scanned. After 3D reconstruction, three types of 3D computer models of hepatic structures were designed and 3D printed as models of hepatic segments without parenchyma (type 1) and with transparent parenchyma (type 2), and hepatic ducts with segmental partitions (type 3). These models were evaluated by six experts using a five-point Likert scale. Ninety two medical freshmen were randomized into four groups to learn hepatic segments with the aid of the three types of models and traditional anatomic atlas (TAA). Their results of two quizzes were compared to evaluate the teaching effects of the four methods.

RESULTS

Three types of models were successful produced which displayed the structures of hepatic segments. By experts' evaluation, type 3 model was better than type 1 and 2 models in anatomical condition, type 2 and 3 models were better than type 1 model in tactility, and type 3 model was better than type 1 model in overall satisfaction (P < 0.05). The first quiz revealed that type 1 model was better than type 2 model and TAA, while type 3 model was better than type 2 and TAA in teaching effects (P < 0.05). The second quiz found that type 1 model was better than TAA, while type 3 model was better than type 2 model and TAA regarding teaching effects (P < 0.05). Only TAA group had significant declines between two quizzes (P < 0.05).

CONCLUSIONS

The model with segmental partitions proves to be optimal, because it can best improve anatomical teaching about hepatic segments.

Strategies for controlling hemorrhage in laparoscopic hepatectomy

With the development of laparoscopic surgery techniques and the better understanding of liver anatomical and physiological characteristics in recent years, laparoscopic hepatectomy has developed rapidly and the feasibility and safety of laparoscopic liver resection surgery has been also getting better and better. However, due to the special physiological function and anatomical structure of the liver, hemorrhage in laparoscopic hepatectomy is serious, and the control of intraoperative bleeding is especially important. In this paper, we will discuss three important aspects of the strategies for controlling hemorrhage in laparoscopic hepatectomy, including preoperative evaluation of patients, intraoperative rational use of hepatic blood flow blocking techniques, and choosing the appropriate instrument.

The error analysis of Lobular and segmental division of right liver by volume measurement

The aim of this study is to explore the inconsistencies between right liver volume as measured by imaging and the actual anatomical appearance of the right lobe. Five healthy donated livers were studied. The liver slices were obtained with hepatic segments multicolor-infused through the portal vein. In the slices, the lobes were divided by two methods: radiological landmarks and real anatomical boundaries. The areas of the right anterior lobe (RAL) and right posterior lobe (RPL) on each slice were measured using Photoshop CS5 and AutoCAD, and the volumes of the two lobes were calculated. There was no statistically significant difference between the volumes of the RAL or RPL as measured by the radiological landmarks (RL) and anatomical boundaries (AB) methods. However, the curves of the square error value of the RAL and RPL measured using CT showed that the three lowest points were at the cranial, intermediate, and caudal levels. The U- or V-shaped curves of the square error rate of the RAL and RPL revealed that the lowest value is at the intermediate level and the highest at the cranial and caudal levels. On CT images, less accurate landmarks were used to divide the RAL and RPL at the cranial and caudal layers. The measured volumes of hepatic segments VIII and VI would be less than their true values, and the measured volumes of hepatic segments VII and V would be greater than their true values, according to radiological landmarks. Clin. Anat. 30:585-590, 2017. © 2017 Wiley Periodicals, Inc.

Nerve Fascicles and Epineurium Volume Segmentation of Peripheral Nerve Using Magnetic Resonance Micro-neurography

RATIONALE AND OBJECTIVES

The aims of this study were to propose a semiautomated technique to segment and measure the volume of different nerve components of the tibial nerve, such as the nerve fascicles and the epineurium, based on magnetic resonance microneurography and a segmentation tool derived from brain imaging; and to assess the reliability of this method by measuring interobserver and intraobserver agreement.

MATERIALS AND METHODS

The tibial nerve of 20 healthy volunteers (age range = 23-69; mean = 47; standard deviation = 15) was investigated at the ankle level. High-resolution images were obtained through tailored microneurographic sequences, covering 28 mm of nerve length. Two operators manually segmented the nerve using the in-phase image. This region of interest was used to mask the nerve in the water image, and two-class segmentation was performed to measure the fascicular volume, epineurial volume, nerve volume, and fascicular to nerve volume ratio (FNR). Interobserver and intraobserver agreements were calculated.

RESULTS

The nerve structure was clearly visualized with distinction of the fascicles and the epineurium. Segmentation provided absolute volumes for nerve volume, fascicular volume, and epineurial volume. The mean FNR resulted in 0.69 with a standard deviation of 0.04 and appeared to be not correlated with age and sex. Interobserver and intraobserver agreements were excellent with alpha values >0.9 for each parameter investigated, with measurements free of systematic errors at the Bland-Altman analysis.

CONCLUSIONS

We concluded that the method is reproducible and the parameter FNR is a novel feature that may help in the diagnosis of neuropathies detecting changes in volume of the fascicles or the epineurium.

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.

Automatic Echographic Detection of Halloysite Clay Nanotubes in a Low Concentration Range

Aim of this work was to investigate the automatic echographic detection of an experimental drug delivery agent, halloysite clay nanotubes (HNTs), by employing an innovative method based on advanced spectral analysis of the corresponding "raw" radiofrequency backscatter signals. Different HNT concentrations in a low range (5.5-66 × 1010 part/mL, equivalent to 0.25-3.00 mg/mL) were dispersed in custom-designed tissue-mimicking phantoms and imaged through a clinically-available echographic device at a conventional ultrasound diagnostic frequency (10 MHz). The most effective response (sensitivity = 60%, specificity = 95%), was found at a concentration of 33 × 1010 part/mL (1.5 mg/mL), representing a kind of best compromise between the need of enough particles to introduce detectable spectral modifications in the backscattered signal and the necessity to avoid the losses of spectral peculiarity associated to higher HNT concentrations. Based on theoretical considerations and quantitative comparisons with literature-available results, this concentration could also represent an optimal concentration level for the automatic echographic detection of different solid nanoparticles when employing a similar ultrasound frequency. Future dedicated studies will assess the actual clinical usefulness of the proposed approach and the potential of HNTs for effective theranostic applications.

Usefulness of two-point Dixon fat-water separation technique in gadoxetic acid-enhanced liver magnetic resonance imaging

AIM: To compare differences between volumetric interpolated breath-hold examination (VIBE) using two-point Dixon fat-water separation (Dixon-VIBE) and chemically selective fat saturation (FS-VIBE) with magnetic resonance imaging examination.

METHODS: Forty-nine patients were included, who were scanned with two VIBE sequences (Dixon-VIBE and FS-VIBE) in hepatobiliary phase after gadoxetic acid administration. Subjective evaluations including sharpness of tumor, sharpness of vessels, strength and homogeneity of fat suppression, and artifacts that were scored using a 4-point scale. The liver-to-lesion contrast was also calculated and compared.

RESULTS: Dixon-VIBE with water reconstruction had significantly higher subjective scores than FS-VIBE in strength and homogeneity of fat suppression (< 0.0001) but lower scores in sharpness of tumor (P < 0.0001), sharpness of vessels (P = 0.0001), and artifacts (P = 0.034). The liver-to-lesion contrast on Dixon-VIBE images was significantly lower than that on FS-VIBE (16.6% ± 9.4% vs 23.9% ± 12.1%, P = 0.0001).

CONCLUSION: Dixon-VIBE provides stronger and more homogenous fat suppression than FS-VIBE, while has lower clarity of focal liver lesions in hepatobiliary phase after gadoxetic acid administration.

Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video

Wireless capsule endoscopy (WCE) enables noninvasive and painless direct visual inspection of a patient's whole digestive tract, but at the price of long time reviewing large amount of images by clinicians. Thus, an automatic computer-aided technique to reduce the burden of physicians is highly demanded. In this paper, we propose a novel color feature extraction method to discriminate the bleeding frames from the normal ones, with further localization of the bleeding regions. Our proposal is based on a twofold system. First, we make full use of the color information of WCE images and utilize K-means clustering method on the pixel represented images to obtain the cluster centers, with which we characterize WCE images as words-based color histograms. Then, we judge the status of a WCE frame by applying the support vector machine (SVM) and K-nearest neighbor methods. Comprehensive experimental results reveal that the best classification performance is obtained with YCbCr color space, cluster number 80 and the SVM. The achieved classification performance reaches 95.75% in accuracy, 0.9771 for AUC, validating that the proposed scheme provides an exciting performance for bleeding classification. Second, we propose a two-stage saliency map extraction method to highlight bleeding regions, where the first-stage saliency map is created by means of different color channels mixer and the second-stage saliency map is obtained from the visual contrast. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas. Quantitative as well as qualitative results show that our methods could differentiate the bleeding areas from neighborhoods correctly.

Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

INTRODUCTION

Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input.

MATERIALS AND METHODS

We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data.

RESULTS

Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm.

CONCLUSIONS

The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation-based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

The assistant function of three-dimensional information for I125 particle implantation

The purpose of this study was to explore the assistant function of 3-D information for I125 particle implantation of multineedle intervention under the guidance of ultrasound. The assistant function of 3-D information was verified by a simulation experiment system which consists of an ultrasound probe, an abdominal phantom, the preoperative computed tomography image of a patient, the electromagnetic tracking device, and the self-developed 3-D image navigation software with a practical and friendly graphical user interface. The simulation particle implantation experiments were divided into the two groups. The first group of experiments was performed with the aid of 3-D information. Seven days later, the second group of experiments was carried out with the aid of 2-D information. We made the statistical analysis of the experimental results obtained by nine medical students, nine interventional radiologists, and nine attending physicians. With the assistance of 3-D information, the percentage of tumor coverage increased (p < 0.01), the operation time shortened (p < 0.01), and the number of insertions reduced (p < 0.01). The assistant function of 3-D information for particle implantation of multineedle intervention under the guidance of ultrasound was technically feasible and effective.

Comparative study of hepatic venography using non-linear-blending images, monochromatic images and low-voltage images of dual-energy CT

OBJECTIVE

To investigate the use of non-linear-blending and monochromatic dual-energy CT (DECT) images to improve the image quality of hepatic venography.

METHODS

82 patients undergoing abdominal DECT in the portal venous phase were enrolled. For each patient, 31 data sets of monochromatic images and 7 data sets of non-linear-blending images were generated. The data sets of the non-linear-blending and monochromatic images with the best contrast-to-noise ratios (CNRs) for hepatic veins were selected and compared with the images obtained at 80 kVp and a simulated 120 kVp. The subjective image quality of the hepatic veins was evaluated using a four-point scale. The image quality of the hepatic veins was analysed using signal-to-noise ratio (SNR) and CNR values.

RESULTS

The optimal CNR between hepatic veins and the liver was obtained with the non-linear-blending images. Compared with the other three groups, there were significant differences in the maximum CNR, the SNR, the subjective ratings and the minimum background noise (p < 0.001). A comparison of the monochromatic and 80-kVp images revealed that the CNR and subjective ratings were both improved (p < 0.001). There was no significant difference in the CNR or subjective ratings between the simulated 120-kVp group and the control group (p = 0.090 and 0.053, respectively).

CONCLUSION

The non-linear-blending technique for acquiring DECT provided the best image quality for hepatic venography.

ADVANCES IN KNOWLEDGE

DECT can enhance the contrast of hepatic veins and the liver, potentially allowing the wider use of low-dose contrast agents for CT examination of the liver.

Serial changes of clinical parameters in a patient with advanced hepatocellular carcinoma with portal vein thrombosis achieving complete response after treatment with sorafenib

The prognosis is usually poor in advanced hepatocellular carcinoma (HCC). Sorafenib is approved for Child-Pugh class A patients with unresectable and advanced HCC. We report here a rare case of a patient with advanced HCC with right portal vein thrombosis (PVT) who achieved a complete response after treatment with sorafenib. This 74-year-old man was a case of non-hepatitis B and C virus-related cirrhosis. Multiphase liver computed tomography showed an 8 cm tumor with early enhance, early wash out, and right PVT at segment 8 of the right lobe. A liver tumor biopsy confirmed the diagnosis of poorly differentiated HCC. Blood tests showed Child-Pugh class A cirrhosis and an alpha-fetoprotein level of 33,058 ng/mL. Sorafenib was initiated at 800 mg/day but was eventually reduced to 400 mg every other day because of a grade 3 hand-foot skin reaction. The alpha fetoprotein (AFP) level decreased rapidly with a linear trend after treatment. After log transformation, the calculated half-life of AFP was 6.84 days. There was no more tumor arterial enhancement, and tumor size was decreased to 3.7 cm on day 42. PVT shrank gradually and localized to the right anterior branch at month 9. There was no recurrence of tumor at the end of follow-up in month 19. Typical serial changes of clinical parameters were demonstrated in this patient.

A new segmentation framework based on sparse shape composition in liver surgery planning system

PURPOSE

To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system.

METHODS

The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer.

RESULTS

Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively.

CONCLUSIONS

The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.

A critical inventory of preoperative skull replicas

INTRODUCTION

Physical replicas of organs are used increasingly for preoperative planning. The quality of these models is generally accepted by surgeons. In view of the strong trend towards minimally invasive and personalised surgery, however, the aim of this investigation was to assess qualitatively the accuracy of such replicas, using skull models as an example.

METHODS

Skull imaging was acquired for three cadavers by computed tomography using clinical routine parameters. After digital three-dimensional (3D) reconstruction, physical replicas were produced by 3D printing. The facsimilia were analysed systematically and compared with the best gold standard possible: the macerated skull itself.

RESULTS

The skull models were far from anatomically accurate. Non-conforming rendering was observed in particular for foramina, sutures, notches, fissures, grooves, channels, tuberosities, thin-walled structures, sharp peaks and crests, and teeth.

CONCLUSIONS

Surgeons should be aware that preoperative models may not yet render the exact anatomy of the patient under consideration and are advised to continue relying, in specific conditions, on their own analysis of the native computed tomography or magnetic resonance imaging.

Automated registration, segmentation, and measurement of metastatic melanoma tumors in serial CT scans

OBJECTIVES

Our goal was to evaluate a new software capability that integrates registration, segmentation and tumor measurement across serial exams within a picture archiving communication system (PACS) to expedite tumor measurement.

MATERIALS AND METHODS

Patients treated under institutional review board-approved protocols for metastatic melanoma were retrospectively reviewed. Of the 19 included patients, five were male, the median age was 43.2, and all received treatment using an adoptive cell therapy. Seventy-one lung, liver, and subcutaneous tumors were manually measured using RECIST (Response Evaluation Criteria In Solid Tumors) criteria before therapy (baseline computed tomography [CT]) and within 3 months after therapy (follow-up CT). We performed semiautomated registration, segmentation, and RECIST measurements at both time points within PACS (Carestream Health, Rochester, NY). We compared manual and software-generated RECIST measurements using Bland-Altman plots.

RESULTS

The median manually measured RECIST diameter for all baseline tumors was 2.1 (1.0-6.2) cm. The refined registration function identified 70/71 (98.6%) tumors on the follow-up CT. On the baseline CT, all 21 liver, 27/32 (84%) lung, and 10/18 (55%) subcutaneous tumors completed segmentation. On the follow-up CT, 19/21 (90%) liver, 21/27 (78%) lung, and 8/10 (80%) subcutaneous tumors completed segmentation. The Bland-Altman plot demonstrated a 95% confidence interval of ±0.7 cm when comparing the software-generated and manual RECIST measurements.

CONCLUSIONS

The PACS software performed semiautomated baseline tumor measurements and fully automated follow-up tumor measurements in a majority of lung, liver, and subcutaneous tumors. In our patients, semiautomated metastatic tumor measurement did not obviate the need for physician oversight due to disease and treatment-related factors.

Computer-aided analysis of 64-slice coronary computed tomography angiography: a comparison with manual interpretation

Coronary computed tomography angiography (CCTA) is increasingly used for the assessment of coronary heart disease (CHD) in symptomatic patients. Software applications have recently been developed to facilitate efficient and accurate analysis of CCTA. This study aims to evaluate the clinical application of computer-aided diagnosis (CAD) software for the detection of significant coronary stenosis on CCTA in populations with low (8%), moderate (13%), and high (27%) CHD prevalence. A total of 341 consecutive patients underwent 64-slice CCTA at 3 clinical sites in the United States. CAD software performed automatic detection of significant coronary lesions (>50% stenosis). CAD results were then compared to the consensus manual interpretation of 2 imaging experts. Data analysis was conducted for each patient and segment. The CAD had 100% sensitivity per patient across all 3 clinical sites. Specificity in the low, moderate, and high CHD prevalence populations was 64%, 41%, and 38%, respectively. The negative predictive value at the 3 clinical sites was 100%. The positive predictive value was 22%, 21%, and 38% for the low, moderate, and high CHD prevalence populations, respectively. This study demonstrates the utility of CAD software in 3 distinct clinical settings. In a low-prevalence population, such as seen in the emergency department, CAD can be used as a Computer-Aided Simple Triage tool to assist in diagnostic delineation of acute chest pain. In a higher prevalence population, CAD software is useful as an adjunct for both the experienced and inexperienced reader.

Theranostic applications: Non-ionizing cellular and molecular imaging through innovative nanosystems for early diagnosis and therapy

Modern medicine is expanding the possibilities of receiving "personalized" diagnosis and therapies, providing minimal invasiveness, technological solutions based on non-ionizing radiation, early detection of pathologies with the main objectives of being operator independent and with low cost to society. Our research activities aim to strongly contribute to these trends by improving the capabilities of current diagnostic imaging systems, which are of key importance in possibly providing both optimal diagnosis and therapies to patients. In medical diagnostics, cellular imaging aims to develop new methods and technologies for the detection of specific metabolic processes in living organisms, in order to accurately identify and discriminate normal from pathological tissues. In fact, most diseases have a "molecular basis" that detected through these new diagnostic methodologies can provide enormous benefits to medicine. Nowadays, this possibility is mainly related to the use of Positron Emission Tomography, with an exposure to ionizing radiation for patients and operators and with extremely high medical diagnostics costs. The future possible development of non-ionizing cellular imaging based on techniques such as Nuclear Magnetic Resonance or Ultrasound, would represent an important step towards modern and personalized therapies. During the last decade, the field of nanotechnology has made important progress and a wide range of organic and inorganic nanomaterials are now available with an incredible number of further combinations with other compounds for cellular targeting. The availability of these new advanced nanosystems allows new scenarios in diagnostic methodologies which are potentially capable of providing morphological and functional information together with metabolic and cellular indications.

Echographic detectability of optoacoustic signals from low-concentration PEG-coated gold nanorods

Purpose:

To evaluate the diagnostic performance of gold nanorod (GNR)-enhanced optoacoustic imaging employing a conventional echographic device and to determine the most effective operative configuration in order to assure optoacoustic effectiveness, nanoparticle stability, and imaging procedure safety.

Methods:

The most suitable laser parameters were experimentally determined in order to assure nanoparticle stability during the optoacoustic imaging procedures. The selected configuration was then applied to a novel tissue-mimicking phantom, in which GNR solutions covering a wide range of low concentrations (25–200 pM) and different sample volumes (50–200 μL) were exposed to pulsed laser irradiation. GNR-emitted optoacoustic signals were acquired either by a couple of single-element ultrasound probes or by an echographic transducer. Off-line analysis included: (a) quantitative evaluation of the relationships between GNR concentration, sample volume, phantom geometry, and amplitude of optoacoustic signals propagating along different directions; (b) echographic detection of “optoacoustic spots,” analyzing their intensity, spatial distribution, and clinical exploitability. MTT measurements performed on two different cell lines were also used to quantify biocompatibility of the synthesized GNRs in the adopted doses.

Results:

Laser irradiation at 30 mJ/cm² for 20 seconds resulted in the best compromise among the requirements of effectiveness, safety, and nanoparticle stability. Amplitude of GNR-emitted optoacoustic pulses was proportional to both sample volume and concentration along each considered propagation direction for all the tested boundary conditions, providing an experimental confirmation of isotropic optoacoustic emission. Average intensity of echographically detected spots showed similar behavior, emphasizing the presence of an “ideal” GNR concentration (100 pM) that optimized optoacoustic effectiveness. The tested GNRs also exhibited high biocompatibility over the entire considered concentration range.

Conclusion:

An optimal configuration for GNR-enhanced optoacoustic imaging was experimentally determined, demonstrating in particular its feasibility with a conventional echographic device. The proposed approach can be easily extended to quantitative performance evaluation of different contrast agents for optoacoustic imaging.

Vascular decomposition using weighted approximate convex decomposition

OBJECTIVE

Stroke treatment often requires analysis of vascular pathology evaluated using computed tomography (CT) angiography. Due to vascular variability and complexity, finding precise relationships between vessel geometries and arterial pathology is difficult. A new convex shape decomposition strategy was developed to understand complex vascular structures and synthesize a weighted approximate convex decomposition (WACD) method for vascular decomposition in computer-aided diagnosis.

MATERIALS AND METHODS

The vascular tree is decomposed into optimal number of components (determined by an expert). The decomposition is based on two primary features of vascular structures: (i) the branching factor that allows structural decomposition and (ii) the concavity over the vessel surface seen primarily at the site of an aneurysm. Such surfaces are decomposed into subcomponents. Vascular sections are reconstructed using CT angiograms. Next the dual graph is constructed, and edge weights for the graph are computed from shape indices. Graph vertices are iteratively clustered by a mesh decimation operator, while minimizing a cost function related to concavity.

RESULTS

The method was validated by first comparing results with an approximate convex decomposition (ACD) method and next on vessel sections (n = 177) whose number of clusters (ground truth) was predetermined by an expert. In both cases, WACD produced promising results with 84.7 % of the vessel sections correctly clustered and when compared with ACD produced a more effective decomposition. Next, the algorithm was validated in a longitudinal study data of 4 subjects where volumetric and surface area comparisons were made between expert segmented sections and WACD decomposed sections that contained aneurysms. The results showed a mean error rate of 7.8 % for volumetric comparisons and 10.4 % for surface area comparisons.

CONCLUSION

Decomposition of the cerebral vasculature from CT angiograms into a geometrically optimal set of convex regions may be useful for computer-assisted diagnosis. A new WACD method capable of decomposing complex vessel structures, including bifurcations and aneurysms, was developed and tested with promising results.

Quantitative measurement of contrast enhancement of esophageal squamous cell carcinoma on clinical MDCT

AIM

To investigate contrast-enhanced computed tomography (CECT) for discriminating esophageal squamous cell carcinoma (ESCC) from normal esophagus and evaluating outcomes within tumors after chemoradiotherapy (CRT).

METHODS

Sixty-four patients with surgical ESCC served as group A, and underwent thoracic contrast-enhanced scan with 16-section multidetector row CT 1 wk before surgery. Thirty-five patients with advanced ESCC receiving 4-wk CRT and showing response to CRT served as group B, and underwent CT scans similar with group A 4 wk after completion of CRT. In group A, differences in CT attenuation values (in HU) between the preoperative ESCC and background normal esophageal wall (delta CT(1)), or between different background normal esophageal walls (delta CT(2)) were compared. Furthermore, delta CT(1) between group A and B was also compared.

RESULTS

In group A, mean delta CT(1) was higher than delta CT(2) (23.86 ± 10.59 HU vs 6.24 ± 3.06 HU, P < 0.05). When a delta CT(1) of 10.025 HU was employed at a cut-off value to discriminate ESCC from normal esophagus, a sensitivity of 89.1% and specificity of 90.6% were achieved. Mean delta CT(1) was lower in group B than in group A (9.25 ± 10.86 vs 23.86 ± 10.59, P < 0.05), and a delta CT(1) of 15.45 HU was obtained at a cut-off value to assess the CRT changes with a sensitivity of 76.6% and specificity of 77.1%.

CONCLUSION

CECT might be a clinical technique for discriminating ESCC from normal esophagus, and evaluating outcome in the tumors treated with CRT.

Theranostic applications: Non-ionizing cellular and molecular imaging through innovative nanosystems for early diagnosis and therapy

Modern medicine is expanding the possibilities of receiving “personalized” diagnosis and therapies, providing minimal invasiveness, technological solutions based on non-ionizing radiation, early detection of pathologies with the main objectives of being operator independent and with low cost to society. Our research activities aim to strongly contribute to these trends by improving the capabilities of current diagnostic imaging systems, which are of key importance in possibly providing both optimal diagnosis and therapies to patients. In medical diagnostics, cellular imaging aims to develop new methods and technologies for the detection of specific metabolic processes in living organisms, in order to accurately identify and discriminate normal from pathological tissues. In fact, most diseases have a “molecular basis” that detected through these new diagnostic methodologies can provide enormous benefits to medicine. Nowadays, this possibility is mainly related to the use of Positron Emission Tomography, with an exposure to ionizing radiation for patients and operators and with extremely high medical diagnostics costs. The future possible development of non-ionizing cellular imaging based on techniques such as Nuclear Magnetic Resonance or Ultrasound, would represent an important step towards modern and personalized therapies. During the last decade, the field of nanotechnology has made important progress and a wide range of organic and inorganic nanomaterials are now available with an incredible number of further combinations with other compounds for cellular targeting. The availability of these new advanced nanosystems allows new scenarios in diagnostic methodologies which are potentially capable of providing morphological and functional information together with metabolic and cellular indications.