An accurate percutaneous puncture is essential for disintegration and removal of renal stones. Although this procedure has proven to be safe, some organs surrounding the renal target might be accidentally perforated. This work describes a new intraoperative framework where tracked surgical tools are superimposed within 4D ultrasound imaging for security assessment of the percutaneous puncture trajectory (PPT). A PPT is first generated from the skin puncture site towards an anatomical target, using the information retrieved by electromagnetic motion tracking sensors coupled to surgical tools. Then, 2D ultrasound images acquired with a tracked probe are used to reconstruct a 4D ultrasound around the PPT under GPU processing. Volume hole-filling was performed in different processing time intervals by a tri-linear interpolation method. At spaced time intervals, the volume of the anatomical structures was segmented to ascertain if any vital structure is in between PPT and might compromise the surgical success. To enhance the volume visualization of the reconstructed structures, different render transfer functions were used. Results: Real-time US volume reconstruction and rendering with more than 25 frames/s was only possible when rendering only three orthogonal slice views. When using the whole reconstructed volume one achieved 8-15 frames/s. 3 frames/s were reached when one introduce the segmentation and detection if some structure intersected the PPT. The proposed framework creates a virtual and intuitive platform that can be used to identify and validate a PPT to safely and accurately perform the puncture in percutaneous nephrolithotomy.
Background: Kidney stone is a major universal health problem, affecting 10% of the population worldwide. Percutaneous nephrolithotomy is a first-line and established procedure for disintegration and removal of renal stones. Its surgical success depends on the precise needle puncture of renal calyces, which remains the most challenging task for surgeons. This work describes and tests a new ultrasound based system to alert the surgeon when undesirable anatomical structures are in between the puncture path defined through a tracked needle.
Methods: Two circular ultrasound transducers were built with a single 3.3-MHz piezoelectric ceramic PZT SN8, 25.4 mm of radius and resin-epoxy matching and backing layers. One matching layer was designed with a concave curvature to work as an acoustic lens with long focusing. The A-scan signals were filtered and processed to automatically detect reflected echoes.
Results: The transducers were mapped in water tank and tested in a study involving 45 phantoms. Each phantom mimics different needle insertion trajectories with a percutaneous path length between 80 and 150 mm. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm.
Conclusions: This new solution may alert the surgeon about anatomical tissues changes during needle insertion, which may decrease the need of X-Ray radiation exposure and ultrasound image evaluation during percutaneous puncture.
Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.
Background: Precise needle puncture of renal calyces is a challenging and essential step for successful percutaneous nephrolithotomy. This work tests and evaluates, through a clinical trial, a real-time navigation system to plan and guide percutaneous kidney puncture. Methods: A novel system, entitled i3DPuncture, was developed to aid surgeons in establishing the desired puncture site and the best virtual puncture trajectory, by gathering and processing data from a tracked needle with optical passive markers. In order to navigate and superimpose the needle to a preoperative volume, the patient, 3D image data and tracker system were previously registered intraoperatively using seven points that were strategically chosen based on rigid bone structures and nearby kidney area. In addition, relevant anatomical structures for surgical navigation were automatically segmented using a multi-organ segmentation algorithm that clusters volumes based on statistical properties and minimum description length criterion. For each cluster, a rendering transfer function enhanced the visualization of different organs and surrounding tissues. Results: One puncture attempt was sufficient to achieve a successful kidney puncture. The puncture took 265 seconds, and 32 seconds were necessary to plan the puncture trajectory. The virtual puncture path was followed correctively until the needle tip reached the desired kidney calyceal. Conclusions: This new solution provided spatial information regarding the needle inside the body and the possibility to visualize surrounding organs. It may offer a promising and innovative solution for percutaneous punctures.
In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a
process often used as disease progression readout and to develop therapeutic strategies. This work presents an image
processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode
Caenorhabditis Elegans.
A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals
of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering
their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in
order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm
that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to
dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm
and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial
animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations
were subsequently segmented based on an iso-value and blended with the resulting volume mesh.
The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and
high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative
diseases treatment planning and interventions prevention.
Pectus excavatum is the most common congenital deformity of the anterior thoracic wall. The surgical correction of such
deformity, using Nuss procedure, consists in the placement of a personalized convex prosthesis into sub-sternal position
to correct the deformity. The aim of this work is the CT-scan substitution by ultrasound imaging for the pre-operative
diagnosis and pre-modeling of the prosthesis, in order to avoid patient radiation exposure. To accomplish this, ultrasound
images are acquired along an axial plane, followed by a rigid registration method to obtain the spatial transformation
between subsequent images. These images are overlapped to reconstruct an axial plane equivalent to a CT-slice. A
phantom was used to conduct preliminary experiments and the achieved results were compared with the corresponding
CT-data, showing that the proposed methodology can be capable to create a valid approximation of the anterior thoracic
wall, which can be used to model/bend the prosthesis.
Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal
formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this
deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar
under the sternum and over the ribs. Although this technique has been shown to be safe and reliable, not all patients have
achieved adequate cosmetic outcome. This often leads to psychological problems and social stress, before and after the
surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical
outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses
the patient pre-surgical thoracic CT scan and anatomical-surgical references to perform a 3D segmentation of the left
ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the
ribs and the sternum, trough b-spline interpolation; b) a volumetric mass spring model that connects two layers - inner
skin layer based on the outer pleura contour and the outer surface skin; and c) displacement of the sternum according to
the prosthetic bar position.
A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the
movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical
skin surface acquired with Polhemus FastSCAN system.
Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the
sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic.
This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and
social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical
correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery
correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this
goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography
(before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was
reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between
the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA
was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum
correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a
satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical
model capable of estimating, with satisfactory results, the postsurgical outcome.
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