Needle Segmentation Research Articles

Design and evaluation of a pneumatic actuation unit for a wasp-inspired self-propelled needle.

Transperineal laser ablation is a minimally invasive thermo-ablative treatment for prostate cancer that requires the insertion of a needle for accurate optical fiber positioning. Needle insertion in soft tissues may cause tissue motion and deformation, resulting in tissue damage and needle positioning errors. In this study, we present a wasp-inspired self-propelled needle that uses pneumatic actuation to move forward with zero external push force, thus avoiding large tissue motion and deformation. The needle consists of six parallel 0.25-mm diameter Nitinol rods driven by a pneumatic actuation system. The pneumatic actuation system consists of Magnetic Resonance (MR) safe 3D-printed parts and off-the-shelf plastic screws. A self-propelled motion is achieved by advancing the needle segments one by one, followed by retracting them simultaneously. The advancing needle segment has to overcome a cutting and friction force, while the stationary needle segments experience a friction force in the opposite direction. The needle self-propels through the tissue when the friction force of the five stationary needle segments overcomes the sum of the friction and cutting forces of the advancing needle segment. We evaluated the prototype's performance in 10-wt% gelatin phantoms and ex vivo porcine liver tissue inside a preclinical Magnetic Resonance Imaging (MRI) scanner in terms of the slip ratio of the needle with respect to the phantom or liver tissue. Our results demonstrated that the needle was able to self-propel through the phantom and liver tissue with slip ratios of 0.912-0.955 and 0.88, respectively. The prototype is a promising step toward the development of self-propelled needles for MRI-guided transperineal laser ablation as a method to treat prostate cancer.

A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images

Objective. Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning networks as convolutional neural network and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption. Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume. Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% versus 76.5%), smaller needle tip position (1.28 mm versus 1.87 mm) and length (1.78 mm versus 2.19 mm) errors on the kidney dataset as well as DSC (78.5% versus 76.9%), needle tip position (0.86 mm versus 1.12 mm) and length (1.01 mm versus 1.26 mm) errors on the prostate dataset. Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.

Deep learning-based automatic pipeline for 3D needle localization on intra-procedural 3D MRI

PurposeAccurate and rapid needle localization on 3D magnetic resonance imaging (MRI) is critical for MRI-guided percutaneous interventions. The current workflow requires manual needle localization on 3D MRI, which is time-consuming and cumbersome. Automatic methods using 2D deep learning networks for needle segmentation require manual image plane localization, while 3D networks are challenged by the need for sufficient training datasets. This work aimed to develop an automatic deep learning-based pipeline for accurate and rapid 3D needle localization on in vivo intra-procedural 3D MRI using a limited training dataset.MethodsThe proposed automatic pipeline adopted Shifted Window (Swin) Transformers and employed a coarse-to-fine segmentation strategy: (1) initial 3D needle feature segmentation with 3D Swin UNEt TRansfomer (UNETR); (2) generation of a 2D reformatted image containing the needle feature; (3) fine 2D needle feature segmentation with 2D Swin Transformer and calculation of 3D needle tip position and axis orientation. Pre-training and data augmentation were performed to improve network training. The pipeline was evaluated via cross-validation with 49 in vivo intra-procedural 3D MR images from preclinical pig experiments. The needle tip and axis localization errors were compared with human intra-reader variation using the Wilcoxon signed rank test, with p < 0.05 considered significant.ResultsThe average end-to-end computational time for the pipeline was 6 s per 3D volume. The median Dice scores of the 3D Swin UNETR and 2D Swin Transformer in the pipeline were 0.80 and 0.93, respectively. The median 3D needle tip and axis localization errors were 1.48 mm (1.09 pixels) and 0.98°, respectively. Needle tip localization errors were significantly smaller than human intra-reader variation (median 1.70 mm; p < 0.01).ConclusionThe proposed automatic pipeline achieved rapid pixel-level 3D needle localization on intra-procedural 3D MRI without requiring a large 3D training dataset and has the potential to assist MRI-guided percutaneous interventions.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.

Combined Method for Segmentation of Multiple Needles in 3D Liver CT

Abstract Radiofrequency ablation is a minimally invasive technique for treating liver or kidney tumors, where precisely inserted ablation probes destroy the tumor tissue with induced heat around their tips. Automatic segmentation of needles from the abdominal computed tomography (CT) scans enables large-scale procedure evaluation and creates a baseline ground truth for automated planning approaches. Our method for needle segmentation enables the detection of multiple needles from unlabeled CT scans by sequential combination of two approaches, a classical method and a neural network. The former method detects intensity-prominent needles in 3D. The latter focuses on the detection of needles with lower intensity, that remained undetected by the classical method. A well-known encoder-decoder approach on axial slices of the CT scan gives a segmentation mask for the needles prominent in slices. To train the network, we create a synthetic dataset, adding needles to clean abdominal CT slices. Our network trained on a small synthetic dataset is able to generalize to clinical CT scans. The performance of the combined method is evaluated on ten anonymous patients from the Medical University of Innsbruck each with up to 18 needles inserted in the liver without any template.

Design and evaluation of an MRI-ready, self-propelled needle for prostate interventions.

Prostate cancer diagnosis and focal laser ablation treatment both require the insertion of a needle for biopsy and optical fibre positioning. Needle insertion in soft tissues may cause tissue motion and deformation, which can, in turn, result in tissue damage and needle positioning errors. In this study, we present a prototype system making use of a wasp-inspired (bioinspired) self-propelled needle, which is able to move forward with zero external push force, thereby avoiding large tissue motion and deformation. Additionally, the actuation system solely consists of 3D printed parts and is therefore safe to use inside a magnetic resonance imaging (MRI) system. The needle consists of six parallel 0.25-mm diameter Nitinol rods driven by the actuation system. In the prototype, the self-propelled motion is achieved by advancing one needle segment while retracting the others. The advancing needle segment has to overcome a cutting and friction force while the retracting needle segments experience a friction force in the opposite direction. The needle self-propels through the tissue when the friction force of the five retracting needle segments overcomes the sum of the friction and cutting forces of the advancing needle segment. We tested the performance of the prototype in ex vivo human prostate tissue inside a preclinical MRI system in terms of the slip ratio of the needle with respect to the prostate tissue. The results showed that the needle was visible in MR images and that the needle was able to self-propel through the tissue with a slip ratio in the range of 0.78-0.95. The prototype is a step toward self-propelled needles for MRI-guided transperineal laser ablation as a method to treat prostate cancer.

Automatic and accurate needle detection in 2D ultrasound during robot-assisted needle insertion process.

Robot-assisted needle insertion guided by 2D ultrasound (US) can effectively improve the accuracy and success rate of clinical puncture. To this end, automatic and accurate needle-tracking methods are important for monitoring puncture processes, avoiding the needle deviating from the intended path, and reducing the risk of injury to surrounding tissues. This work aims to develop a framework for automatic and accurate detection of an inserted needle in 2D US image during the insertion process. We propose a novel convolutional neural network architecture comprising of a two-channel encoder and single-channel decoder for needle segmentation using needle motion information extracted from two adjacent US image frames. Based on the novel network, we further propose an automatic needle detection framework. According to the prediction result of the previous frame, a region of interest of the needle in the US image was extracted and fed into the proposed network to achieve finer and faster continuous needle localization. The performance of our method was evaluated based on 1000 pairs of US images extracted from robot-assisted needle insertions on freshly excised bovine and porcine tissues. The needle segmentation network achieved 99.7% accuracy, 86.2% precision, 89.1% recall, and an F1-score of 0.87. The needle detection framework successfully localized the needle with a mean tip error of 0.45 ± 0.33mm and a mean orientation error of 0.42° ± 0.34° and achieved a total processing time of 50ms per image. The proposed framework demonstrated the capability to realize robust, accurate, and real-time needle localization during robot-assisted needle insertion processes. It has a promising application in tracking the needle and ensuring the safety of robotic-assisted automatic puncture during challenging US-guided minimally invasive procedures.

Automated Needle Digitization in Ultrasound-based Prostate High Dose-rate Brachytherapy Using a Deep Learning Algorithm

Prostate high dose-rate brachytherapy (HDR) delivers the radiation using a radioactive Ir-192 seed through interstitial needles and the needle implants are guided by a real-time ultrasound (US) imaging system. The success of this therapy relies highly on accurate digitization of the needles in the treatment planning US images. In current clinical procedures, the needles' trajectories are manually labeled by physicists, which is time-consuming and prone to errors. In recent years, deep learning techniques have brought many breakthroughs, especially in medical imaging processing. We thus propose a deep learning-based method to segment the needles accurately in the US images. The training data including the US images, needle positions and treatment plans were collected from the clinical database with IRB approval. Before training, images in the Cartesian coordinate were transferred into the polar plane and then split into smaller batches so that each contains one needle. Data augmentation techniques were used to avoid overfitting. The needle segmented neural network was based on U-Net with an encoder-decoder structure and shortcuts connecting directly the encoder layers and the corresponding decoder ones. The network structure and parameters were optimized for the needle segmentation problem. To estimate the positions of the needle tips, a separate convolutional neural network with the architecture of Vgg16 was developed and the 3D position accuracy was obtained. Data from 441 patients and 13200 needles was utilized. The needle trajectories were successfully segmented with mean errors of 0.67 mm and 0.32 mm in left-right (X) and anterior-posterior (Y) directions of the original US images and the estimation errors within 2 mm in X and Y directions are 95.4% and 99.2%, respectively. As for the needle tip localization, the positioning accuracies in X, Y, and Z (superior-inferior) are 0.72 mm, 0.37 mm and 1.88 mm, with 94.2%, 98.3% and 67.5% of the data have error of less than 2 mm, respectively. A novel method based on deep learning and neural networks was successfully developed to digitize HDR brachytherapy needles automatically. This method can be served as a valuable tool during brachytherapy procedures and has the potential to improve the efficiency and quality of treatment delivery, contributing overall to the patient care.

Deep learning applications in automatic needle segmentation in ultrasound-guided prostate brachytherapy.

High-Dose-Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time consuming and error prone. In this study, we propose a set of deep learning-based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images. Two deep neural networks are developed to address this problem. First, a modified deep U-Net is used to segment the pixels belonging to the brachytherapy needles from the US images. Second, an additional VGG-16-based deep convolutional network is combined with the segmentation network to predict the locations of the needle tips. The networks are trained and evaluated on a clinical US images dataset with labeled needle trajectories collected in our hospital (Institutional Review Board approval (IRB 41755)). The evaluation results show that our method can accurately extract the trajectories of the needles with a resolution of 0.668mm and 0.319mm in x and y direction, respectively. 95.4% of the x direction and 99.2% of the y direction have error ≤ 2mm. Moreover, the position resolutions of the tips are 0.721, 0.369, and 1.877mm in x, y, and z directions, respectively, while 94.2%, 98.3%, and 67.5% of the data have error ≤ 2mm. This paper proposed a neural network-based algorithm to segment the brachytherapy needles from the US images and locate the needle tip. It can be used in the HDR brachytherapy to help improve the efficiency and quality of the treatments.

A semiautomatic segmentation method for interstitial needles in intraoperative 3D transvaginal ultrasound images for high-dose-rate gynecologic brachytherapy of vaginal tumors

PurposeThe purpose of this study was to evaluate the use of a semiautomatic algorithm to simultaneously segment multiple high-dose-rate (HDR) gynecologic interstitial brachytherapy (ISBT) needles in three-dimensional (3D) transvaginal ultrasound (TVUS) images, with the aim of providing a clinically useful tool for intraoperative implant assessment. Methods and MaterialsA needle segmentation algorithm previously developed for HDR prostate brachytherapy was adapted and extended to 3D TVUS images from gynecologic ISBT patients with vaginal tumors. Two patients were used for refining/validating the modified algorithm and five patients (8–12 needles/patient) were reserved as an unseen test data set. The images were filtered to enhance needle edges, using intensity peaks to generate feature points, and leveraged the randomized 3D Hough transform to identify candidate needle trajectories. Algorithmic segmentations were compared against manual segmentations and calculated dwell positions were evaluated. ResultsAll 50 test data set needles were successfully segmented with 96% of algorithmically segmented needles having angular differences <3° compared with manually segmented needles and the maximum Euclidean distance was <2.1 mm. The median distance between corresponding dwell positions was 0.77 mm with 86% of needles having maximum differences <3 mm. The mean segmentation time using the algorithm was <30 s/patient. ConclusionsWe successfully segmented multiple needles simultaneously in intraoperative 3D TVUS images from gynecologic HDR-ISBT patients with vaginal tumors and demonstrated the robustness of the algorithmic approach to image artifacts. This method provided accurate segmentations within a clinically efficient timeframe, providing the potential to be translated into intraoperative clinical use for implant assessment.

An adaptive finite element model for steerable needles

Penetration of a flexible and steerable needle into a soft target material is a complex problem to be modelled, involving several mechanical challenges. In the present paper, an adaptive finite element algorithm is developed to simulate the penetration of a steerable needle in brain-like gelatine material, where the penetration path is not predetermined. The geometry of the needle tip induces asymmetric tractions along the tool–substrate frictional interfaces, generating a bending action on the needle in addition to combined normal and shear loading in the region where fracture takes place during penetration. The fracture process is described by a cohesive zone model, and the direction of crack propagation is determined by the distribution of strain energy density in the tissue surrounding the tip. Simulation results of deep needle penetration for a programmable bevel-tip needle design, where steering can be controlled by changing the offset between interlocked needle segments, are mainly discussed in terms of penetration force versus displacement along with a detailed description of the needle tip trajectories. It is shown that such results are strongly dependent on the relative stiffness of needle and tissue and on the tip offset. The simulated relationship between programmable bevel offset and needle curvature is found to be approximately linear, confirming empirical results derived experimentally in a previous work. The proposed model enables a detailed analysis of the tool–tissue interactions during needle penetration, providing a reliable means to optimise the design of surgical catheters and aid pre-operative planning.

Ultrasound needle segmentation and trajectory prediction using excitation network.

Ultrasound (US)-guided percutaneous kidney biopsy is a challenge for interventionists as US artefacts prevent accurate viewing of the biopsy needle tip. Automatic needle tracking and trajectory prediction can increase operator confidence in performing biopsies, reduce procedure time, minimize the risk of inadvertent biopsy bleedings, and enable future image-guided robotic procedures. In this paper, we propose a tracking-by-segmentation model with spatial and channel "Squeeze and Excitation" (scSE) for US needle detection and trajectory prediction. We adopt a light deep learning architecture (e.g. LinkNet) as our segmentation baseline network and integrate the scSE module to learn spatial information for better prediction. The proposed model is trained with the US images of anonymized kidney biopsy clips from 8 patients. The contour is obtained using the border-following algorithm and area calculated using Green formula. Trajectory prediction is made by extrapolating from the smallest bounding box that can capture the contour. We train and test our model on a total of 996 images extracted from 102 short videos at a rate of 3 frames per second from each video. A set of 794 images is used for training and 202 images for testing. Our model has achieved IOU of 41.01%, dice accuracy of 56.65%, F1-score of 36.61%, and root-mean-square angle error of 13.3[Formula: see text]. We are thus able to predict and extrapolate the trajectory of the biopsy needle with decent accuracy for interventionists to better perform biopsies. Our novel model combining LinkNet and scSE shows a promising result for kidney biopsy application, which implies potential to other similar ultrasound-guided biopsies that require needle tracking and trajectory prediction.

Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy.

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.

Creation of a multi-segmented optical needle with prescribed length and spacing using the radiation pattern from a sectional-uniform line source

This paper presents a method to generate a multi-segmented optical needle with a strong longitudinally polarized field, uniform intensity along the optical axis, and a transverse size (~0.36λ). The length of each segment in the optical needle and the spacing between adjacent segments are controllable by reversing and focusing the radiation pattern from a sectional-uniform line source antenna to the focal volume of a 4Pi focusing system. By solving the inverse problem, we can obtain the required incident field distribution at the pupil plane to create the multi-segmented optical needle. Numerical examples demonstrate that a multi-segmented optical needle with variable focal depth, adjustable interval, narrow lateral width, homogeneous intensity, and high longitudinal polarization purity can be formed using the proposed approach. The length of each needle segment is approximately equal to the length of the corresponding sectional uniform line source. The multi-segmented optical needle may be employed in applications such as multi-particle acceleration, multi-particle trapping and manipulation, laser machining, and laser material processing.

Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

Needle segmentation is a fundamental step for needle reconstruction and image-guided surgery. Although there has been success stories in needle segmentation for non-microsurgeries, the methods cannot be directly extended to ophthalmic surgery due to the challenges bounded to required spatial resolution. As the ophthalmic surgery is performed by finer and smaller surgical instruments in micro-structural anatomies, specifically in retinal domains, difficulties are raised for delicate operation and sensitive perception. To address these challenges, in this paper we investigate needle segmentation in ophthalmic operation on 60 Optical Coherence Tomography (OCT) cubes captured during needle injection surgeries on ex-vivo pig eyes. Furthermore, we developed two different approaches, a conventional method based on morphological features (MF) and a specifically designed full convolution neural networks (FCN) method, moreover, we evaluate them on the benchmark for needle segmentation in the volumetric OCT images. The experimental results show that FCN method has a better segmentation performance based on four evaluation metrics while MF method has a short inference time, which provides valuable reference for future works.

Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery

Simultaneous automatic segmentation of multiple needles using 3D ultrasound for high-dose-rate prostate brachytherapy.

Sagittally reconstructed 3D (SR3D) ultrasound imaging shows promise for improved needle localization for high-dose-rate prostate brachytherapy (HDR-BT); however, needles must be manually segmented intraoperatively while the patient is anesthetized to create a treatment plan. The purpose of this article was to describe and validate an automatic needle segmentation algorithm designed for HDR-BT, specifically capable of simultaneously segmenting all needles in an HDR-BT implant using a single SR3D image with ~5 mm interneedle spacing. The segmentation algorithm involves regularized feature point classification and line trajectory identification based on the randomized 3D Hough transform modified to handle multiple straight needles in a single image simultaneously. Needle tips are identified based on peaks in the derivative of the signal intensity profile along the needle trajectory. For algorithm validation, 12 prostate cancer patients underwent HDR-BT during which SR3D images were acquired with all needles in place. Needles present in each of the 12 images were segmented manually, providing a gold standard for comparison, and using the algorithm. Tip errors were assessed in terms of the 3D Euclidean distance between needle tips, and trajectory error was assessed in terms of 2D distance in the axial plane and angular deviation between trajectories. In total, 190 needles were investigated. Mean execution time of the algorithm was 11.0 s per patient, or 0.7 s per needle. The algorithm identified 82% and 85% of needle tips with 3D errors ≤3 mm and ≤5 mm, respectively, 91% of needle trajectories with 2D errors in the axial plane ≤3 mm, and 83% of needle trajectories with angular errors ≤3°. The largest tip error component was in the needle insertion direction. Previous work has indicated HDR-BT needles may be manually segmented using SR3D images with insertion depth errors ≤3 mm and ≤5 mm for 83% and 92% of needles, respectively. The algorithm shows promise for reducing the time required for the segmentation of straight HDR-BT needles, and future work involves improving needle tip localization performance through improved image quality and modeling curvilinear trajectories.

Sci‐Thur AM: YIS – 03: Combining sagittally‐reconstructed 3D and live‐2D ultrasound for high‐dose‐rate prostate brachytherapy needle segmentation

Ultrasound‐guided high‐dose‐rate prostate brachytherapy (HDR‐BT) needle segmentation is performed clinically using live‐2D sagittal images. Organ segmentation is then performed using axial images, introducing a source of geometric uncertainty. Sagittally‐reconstructed 3D (SR3D) ultrasound enables both needle and organ segmentation, but suffers from shadow artifacts. We present a needle segmentation technique augmenting SR3D with live‐2D sagittal images using mechanical probe tracking to mitigate image artifacts and compare it to the clinical standard.Seven prostate cancer patients underwent TRUS‐guided HDR‐BT during which the clinical and proposed segmentation techniques were completed in parallel using dual ultrasound video outputs. Calibrated needle end‐length measurements were used to calculate insertion depth errors (IDEs), and the dosimetric impact of IDEs was evaluated by perturbing clinical treatment plan source positions.The proposed technique provided smaller IDEs than the clinical approach, with mean±SD of −0.3±2.2 mm and −0.5±3.7mm respectively. The proposed and clinical techniques resulted in 84% and 43% of needles with IDEs within ±3mm, and IDE ranges across all needles of [−7.7mm, 5.9mm] and [−9.3mm, 7.7mm] respectively. The proposed and clinical IDEs lead to mean±SD changes in the volume of the prostate receiving the prescription dose of −0.6±0.9% and −2.0±5.3% respectively.The proposed technique provides improved HDR‐BT needle segmentation accuracy over the clinical technique leading to decreased dosimetric uncertainty by eliminating the axial‐to‐sagittal registration, and mitigates the effect of shadow artifacts by incorporating mechanically registered live‐2D sagittal images.

SU‐F‐T‐41: 3D MTP‐TRUS for Prostate Implant

Purpose:Prostate brachytherapy is an effective treatment for early prostate cancer. The current prostate implant is limited to using 2D transrectal ultrassound (TRUS) or machenical motor driven 2D array either in the end or on the side. Real‐time 3D images can improve the accuracy of the guidance of prostate implant. The concept of our system is to allow realtime full visualization of the entire prostate with the multiple transverse scan.Methods:The prototype of 3D Multiple‐Transverse‐Plane Transrectal Ultrasound probe (MTP‐TRUS) has been designed by us and manufactured by Blatek inc. It has 7 convex linear arrays and each array has 96 elements. It is connected to cQuest Fire bird research system (Cephasonics inc.) which is a flexible and configurable ultrasound‐development platform. The size of cQuest Firebird system is compact and supports the real‐time wireless image transferring. A relay based mux board is designed for the cQuest Firebird system to be able to connect 672 elements.Results:The center frequency of probe is 6MHz±10%. The diameter of probe is 3cm and the length is 20cm. The element pitch is 0.205 mm. Array focus is 30mm and spacing 1.6cm. The beam data for each array was measured and met our expectation. The interface board of MTP‐TURS is made and able to connect to cQuest Firebird system. The image display interface is still under the development. Our real‐time needle tracking algorithm will be implemented too.Conclusion:Our MTP‐TRUS system for prostate implant will be able to acquire real‐time 3D images of prostate and do the real‐time needle segmentation and tracking. The system is compact and have wireless function.

Ultrasound Guided High-Dose-Rate Prostate Brachytherapy: Live Needle Segmentation and 3D Image Reconstruction Using the Sagittal Transducer

Ultrasound Guided High-Dose-Rate Prostate Brachytherapy: Live Needle Segmentation and 3D Image Reconstruction Using the Sagittal Transducer