Enhancement of needle visualization and localization in ultrasound.

PURPOSE: This scoping review covers needle visualization and localization techniques in ultrasound, where localization-based approaches mostly aim to compute the needle shaft (and tip) location while potentially enhancing its visibility too. METHODS: A literature review is conducted on the state-of-the-art techniques, which could be divided into five categories: (1) signal and image processing-based techniques to augment the needle, (2) modifications to the needle and insertion to help with needle-transducer alignment and visibility, (3) changes to ultrasound image formation, (4) motion-based analysis and (5) machine learning. RESULTS: Advantages, limitations and challenges of representative examples in each of the categories are discussed. Evaluation techniques performed in ex vivo, phantom and in vivo studies are discussed and summarized. CONCLUSION: Greatest limitation of the majority of the literature is that they rely on original visibility of the needle in the static image. Need for additional/improved apparatus is the greatest limitation toward clinical utility in practice. SIGNIFICANCE: Ultrasound-guided needle placement is performed in many clinical applications, including biopsies, treatment injections and anesthesia. Despite the wide range and long history of this technique, an ongoing challenge is needle visibility in ultrasound. A robust technique to enhance ultrasonic needle visibility, especially for steeply inserted hand-held needles, and while maintaining clinical utility requirements is needed.

CASPER: computer-aided segmentation of imperceptible motion-a learning-based tracking of an invisible needle in ultrasound.

PURPOSE: This paper presents a new micro-motion-based approach to track a needle in ultrasound images captured by a handheld transducer. METHODS: We propose a novel learning-based framework to track a handheld needle by detecting microscale variations of motion dynamics over time. The current state of the art on using motion analysis for needle detection uses absolute motion and hence work well only when the transducer is static. We have introduced and evaluated novel spatiotemporal and spectral features, obtained from the phase image, in a self-supervised tracking framework to improve the detection accuracy in the subsequent frames using incremental training. Our proposed tracking method involves volumetric feature selection and differential flow analysis to incorporate the neighboring pixels and mitigate the effects of the subtle tremor motion of a handheld transducer. To evaluate the detection accuracy, the method is tested on porcine tissue in-vivo, during the needle insertion in the biceps femoris muscle. RESULTS: Experimental results show the mean, standard deviation and root-mean-square errors of [Formula: see text], [Formula: see text] and [Formula: see text] in the insertion angle, and 0.82, 1.21, 1.47 mm, in the needle tip, respectively. CONCLUSIONS: Compared to the appearance-based detection approaches, the proposed method is especially suitable for needles with ultrasonic characteristics that are imperceptible in the static image and to the naked eye.

Detection of an invisible needle in ultrasound using a probabilistic SVM and time-domain features.

We propose a novel learning-based approach to detect an imperceptible hand-held needle in ultrasound images using the natural tremor motion. The minute tremor induced on the needle however is also transferred to the tissue in contact with the needle, making the accurate needle detection a challenging task. The proposed learning-based framework is based on temporal analysis of the phase variations of pixels to classify them according to the motion characteristics. In addition to the classification, we also obtain a probability map of the segmented pixels by cross-validation. A Hough transform is then used on the probability map to localize the needle using the segmented needle and posterior probability estimate. The two-step probability-weighted localization on the segmented needle in a learning framework is the key innovation which results in localization improvement and adaptability to specific clinical applications. The method was tested in vivo for a standard 17 gauge needle inserted at 50-80° insertion angles and 40-60mm depths. The results showed an average accuracy of (2.12°, 1.69mm) and 81%±4% for localization and classification, respectively.

Three-Dimensional Ultrasound-Guided Real-Time Midline Epidural Needle Placement with Epiguide: A Prospective Feasibility Study.

Current 2-D ultrasound technology is unable to perform a midline neuraxial needle insertion under real-time ultrasound guidance using a standard needle and without an assistant. The aim of the work described here was to determine the feasibility of a new technology providing such capability, starting with a study evaluating the selected puncture site. A novel 3-D ultrasound imaging technique was designed using thick-slice rendering in conjunction with a custom needle guide (3DUS + Epiguide). A clinical feasibility study evaluated the ability of 3DUS + Epiguide to identify the epidural needle puncture site for a midline insertion in the lumbar spine. We hypothesized that (i) the puncture site identified by 3DUS + Epiguide was within a 5-mm radius from the site chosen by standard palpation, and (ii) the difference between the two puncture sites was not correlated to the patient characteristics age, weight, height, body mass index and gestational age. The mean (±standard deviation) distances between puncture sites determined by 3DUS + Epiguide and palpation were 3.1 (±1.7) mm and 2.8 (±1.3) mm, for the L2-3 and L3-4 interspaces of 20 patients, respectively. Distances were comparable to intra-observer variability, indicating the potential for a thick-slice rendering of 3-D ultrasound along the Epiguide trajectory to select the puncture site of a midline neuraxial needle insertion. The long-term potential benefits of this system include increased efficiency and use of anesthesia, and a reduction in the frequency and severity of the complications from incorrect needle insertions. Epidural success in the most difficult cases (e.g., the obese) will be the focus of future work.

Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling.

PURPOSE: This paper presents a new approach to detect a standard handheld needle in ultrasound-guided interventions. METHODS: Our proposal is to use natural hand tremor, which causes minute displacement of the needle, to detect the needle in ultrasound B-mode images. Subtle displacements arising from tremor motion have a periodic pattern which is usually imperceptible to the naked eye in the B-mode image. We use these displacement measurements in a spatiotemporal framework to detect linear structures with periodic pattern among a sequence of frames. The needle trajectory is estimated as a linear path in the image having maximum spectral correlation with the time trace of displacement due to tremor. A coarse estimation process is followed by a fine estimation step, where the motion pattern is analyzed along spatiotemporal linear paths with various angles originating from the estimated puncture site, within the trajectory channel. Spectral coherency is derived for each sample path versus the reference path, and the needle trajectory is identified as the mean of the sample paths with the maximum coherence within the tremor frequency range. RESULTS: To evaluate the detection accuracy, we tested the method in vivo on porcine tissue, where the needle was inserted into the biceps femoris muscle. To understand whether tremor itself affects needle position, the maximum angular change due to tremor was calculated: mean, standard deviation (SD) and root-mean-square (RMS) measurement of [Formula: see text] and [Formula: see text]. The accuracy of the needle trajectory was calculated by comparing to an expert manual segmentation, averaged over the captured data and presented in mean, SD and RMS error of [Formula: see text] and [Formula: see text], respectively. CONCLUSION: Results demonstrate that natural tremor motion creates minute coherent motion along the needle, which could be used to localize the needle trajectory within the acceptable accuracy. This method is suitable for standard needles used clinically.

Needle Trajectory and Tip Localization in Real-Time 3-D Ultrasound Using a Moving Stylus.

Described here is a novel approach to needle localization in 3-D ultrasound based on automatic detection of small changes in appearance on movement of the needle stylus. By stylus oscillation, including its full insertion into the cannula to the tip, the image processing techniques can localize the needle trajectory and the tip in the 3-D ultrasound volume. The 3-D needle localization task is reduced to two 2-D localizations using orthogonal projections. To evaluate our method, we tested it on three different ex vivo tissue types, and the preliminary results indicated that the method accuracy lies within clinical acceptance, with average error ranges of 0.9°-1.4° in needle trajectory and 0.8-1.1 mm in needle tip. Results also indicate that method performance is independent of the echogenicity of the tissue. This technique is a safe way of producing ultrasonic intensity changes and appears to introduce negligible risk to the patient, as the outer cannula remains fixed.

Sex and Smoking Status Effects on the Early Detection of Early Lung Cancer in High-Risk Smokers Using an Electronic Nose.

OBJECTIVE: Volatile organic compounds (VOCs) in exhaled breath as measured by electronic nose (e-nose) have utility as biomarkers to detect subjects at risk of having lung cancer in a screening setting. We hypothesize that breath analysis using an e-nose chemo-resistive sensor array could be used as a screening tool to discriminate patients diagnosed with lung cancer from high-risk smokers. METHODS: Breath samples from 191 subjects-25 lung cancer patients and 166 high-risk smoker control subjects without cancer-were analyzed. For clinical relevancy, subjects in both groups were matched for age, sex, and smoking histories. Classification and regression trees and discriminant functions classifiers were used to recognize VOC patterns in e-nose data. Cross-validated results were used to assess classification accuracy. Repeatability and reproducibility of e-nose data were assessed by measuring subject-exhaled breath in parallel across two e-nose devices. RESULTS: e-Nose measurements could distinguish lung cancer patients from high-risk control subjects, with a better than 80% classification accuracy. Subject sex and smoking status impacted classification as area under the curve results (ex-smoker males 0.846, ex-smoker female 0.816, current smoker male 0.745, and current smoker female 0.725) demonstrated. Two e-nose systems could be calibrated to give equivalent readings across subject-exhaled breath measured in parallel. CONCLUSIONS: e-Nose technology may have significant utility as a noninvasive screening tool for detecting individuals at increased risk for lung cancer. SIGNIFICANCE: The results presented further the case that VOC patterns could have real clinical utility to screen for lung cancer in the important growing ex-smoker population.