CrOssing fiber Modeling in the Peritumoral Area using dMRI (COMPARI).

Visualization of fiber tracts around the tumor is critical for neurosurgical planning and preservation of crucial structural connectivity during tumor resection. Biophysical modeling approaches estimate fiber tract orientations from differential water diffusivity information of diffusion MRI. However, the presence of edema and tumor infiltration presents a challenge to visualize crossing fiber tracts in the peritumoral region. Previous approaches proposed free water modeling to compensate for the effect of water diffusivity in edema, but those methods were limited in estimating complex crossing fiber tracts. We propose a new cascaded multi-compartment model to estimate tissue microstructure in the presence of edema and pathological contaminants in the area surrounding brain tumors. In our model (COMPARI), the isotropic components of diffusion signal, including free water and hindered water, were eliminated, and the fiber orientation distribution (FOD) of the remaining signal was estimated. In simulated data, COMPARI accurately recovered fiber orientations in the presence of extracellular water. In a dataset of 23 patients with highly edematous brain tumors, the amplitudes of FOD and anisotropic index distribution within the peritumoral region were higher with COMPARI than with a recently proposed multi-compartment constrained deconvolution model. In a selected patient with metastatic brain tumor, we demonstrated COMPARI's ability to effectively model and eliminate water from the peritumoral region. The white matter bundles reconstructed with our model were qualitatively improved compared to those of other models, and allowed the identification of crossing fibers. In conclusion, the removal of isotropic components as proposed with COMPARI improved the bio-physical modeling of dMRI in edema, thus providing information on crossing fibers, thereby enabling improved tractography in a highly edematous brain tumor. This model may improve surgical planning tools to help achieve maximal safe resection of brain tumors.

Application of Fractal Analysis in Detecting Trabecular Bone Changes in Periapical Radiograph of Patients with Periodontitis.

INTRODUCTION: Evaluation of detailed features of the supporting bone is an important step in diagnosis and treatment planning for teeth with clinical attachment loss. Fractal analysis can be used as a method for evaluating the complexity of trabecular bone structures. The aim of this study was to evaluate the trabecular bone changes in periapical radiographs of patients with different stages of periodontitis using fractal analysis. METHODS: This comparative cross-sectional study was performed on patients with and without clinical attachment loss in mandibular first molars. Teeth with clinical attachment loss were divided into mild, moderate, and severe periodontitis groups. Digital periapical radiographs were obtained from the mandibular first molars using the same exposure parameters. DICOM file of the radiographs was exported to ImageJ software for fractal analysis. Three regions of interest (ROIs) were considered in each radiograph: two proximal ROIs mesial and distal to the mandibular first molar and one apical ROI. Fractal dimension (FD) values were calculated using the fractal box counting approach. Statistical analysis was performed using the chi-square test, Mann-Whitney test, intraclass correlation coefficient, and ANOVA (α = 0.05). RESULTS: FD values were significantly different between moderate and severe periodontitis and healthy periodontal bone (P < 0.05), except for the distal ROI for moderate periodontitis cases (P=0.280). However, FD values of the supporting bone in periodontally healthy teeth and teeth with mild periodontitis did not show a statistically significant difference (P > 0.05). CONCLUSION: Fractal analysis is a useful tool for evaluation of bone alterations in moderate and severe periodontitis, but was not able to detect the most initial radiographic bone signs of mild periodontitis.

Hybrid registration of retinal fluorescein angiography and optical coherence tomography images of patients with diabetic retinopathy.

Diabetic retinopathy (DR) is a common ophthalmic disease among diabetic patients. It is essential to diagnose DR in the early stages of treatment. Various imaging systems have been proposed to detect and visualize retina diseases. The fluorescein angiography (FA) imaging technique is now widely used as a gold standard technique to evaluate the clinical manifestations of DR. Optical coherence tomography (OCT) imaging is another technique that provides 3D information of the retinal structure. The FA and OCT images are captured in two different phases and field of views and image fusion of these modalities are of interest to clinicians. This paper proposes a hybrid registration framework based on the extraction and refinement of segmented major blood vessels of retinal images. The newly extracted features significantly improve the success rate of global registration results in the complex blood vessel network of retinal images. Afterward, intensity-based and deformable transformations are utilized to further compensate the motion magnitude between the FA and OCT images. Experimental results of 26 images of the various stages of DR patients indicate that this algorithm yields promising registration and fusion results for clinical routine.

GPU-based 3D iceball modeling for fast cryoablation simulation and planning.

PURPOSE: The elimination of abdominal tumors by percutaneous cryoablation has been shown to be an effective and less invasive alternative to open surgery. Cryoablation destroys malignant cells by freezing them with one or more cryoprobes inserted into the tumor through the skin. Alternating cycles of freezing and thawing produce an enveloping iceball that causes the tumor necrosis. Planning such a procedure is difficult and time-consuming, as it is necessary to plan the number and cryoprobe locations and predict the iceball shape which is also influenced by the presence of heating sources, e.g., major blood vessels and warm saline solution, injected to protect surrounding structures from the cold. METHODS: This paper describes a method for fast GPU-based iceball modeling based on the simulation of thermal propagation in the tissue. Our algorithm solves the heat equation within a cube around the cryoprobes tips and accounts for the presence of heating sources around the iceball. RESULTS: Experimental results of two studies have been obtained: an ex vivo warm gel setup and simulation on five retrospective patient cases of kidney tumors cryoablation with various levels of complexity of the vascular structure and warm saline solution around the tumor tissue. The experiments have been conducted in various conditions of cube size and algorithm implementations. Results show that it is possible to obtain an accurate result within seconds. CONCLUSION: The promising results indicate that our method yields accurate iceball shape predictions in a short time and is suitable for surgical planning.

An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning.

PURPOSE: Segmentation of liver tumours is an important part of the 3D visualisation of the liver anatomy for surgical planning. The spatial relationship between tumours and other structures inside the liver forms the basis of preoperative surgical risk assessment. However, the automatic segmentation of liver tumours from abdominal CT scans is riddled with challenges. Tumours located at the border of the liver impose a big challenge as the surrounding tissues could have similar intensities. METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries. RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets. CONCLUSIONS: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.

Automatic planning of needle placement for robot-assisted percutaneous procedures.

PURPOSE: Percutaneous procedures allow interventional radiologists to perform diagnoses or treatments guided by an imaging device, typically a computed tomography (CT) scanner with a high spatial resolution. To reduce exposure to radiations and improve accuracy, robotic assistance to needle insertion is considered in the case of X-ray guided procedures. We introduce a planning algorithm that computes a needle placement compatible with both the patient's anatomy and the accessibility of the robot within the scanner gantry. METHODS: Our preoperative planning approach is based on inverse kinematics, fast collision detection, and bidirectional rapidly exploring random trees coupled with an efficient strategy of node addition. The algorithm computes the allowed needle entry zones over the patient's skin (accessibility map) from 3D models of the patient's anatomy, the environment (CT, bed), and the robot. The result includes the admissible robot joint path to target the prescribed internal point, through the entry point. A retrospective study was performed on 16 patients datasets in different conditions: without robot (WR) and with the robot on the left or the right side of the bed (RL/RR). RESULTS: We provide an accessibility map ensuring a collision-free path of the robot and allowing for a needle placement compatible with the patient's anatomy. The result is obtained in an average time of about 1 min, even in difficult cases. The accessibility maps of RL and RR covered about a half of the surface of WR map in average, which offers a variety of options to insert the needle with the robot. We also measured the average distance between the needle and major obstacles such as the vessels and found that RL and RR produced needle placements almost as safe as WR. CONCLUSION: The introduced planning method helped us prove that it is possible to use such a "general purpose" redundant manipulator equipped with a dedicated tool to perform percutaneous interventions in cluttered spaces like a CT gantry.

Real-time curvature defect detection on outer surfaces using best-fit polynomial interpolation.

This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously.