Ultrasound Imaging Information Research Articles

NSBR-Net: A Novel Noise Suppression and Boundary Refinement Network for Breast Tumor Segmentation in Ultrasound Images

Breast tumor segmentation of ultrasound images provides valuable tumor information for early detection and diagnosis. However, speckle noise and blurred boundaries in breast ultrasound images present challenges for tumor segmentation, especially for malignant tumors with irregular shapes. Recent vision transformers have shown promising performance in handling the variation through global context modeling. Nevertheless, they are often dominated by features of large patterns and lack the ability to recognize negative information in ultrasound images, which leads to the loss of breast tumor details (e.g., boundaries and small objects). In this paper, we propose a novel noise suppression and boundary refinement network, NSBR-Net, to simultaneously alleviate speckle noise interference and blurred boundary problems of breast tumor segmentation. Specifically, we propose two innovative designs, namely, the Noise Suppression Module (NSM) and the Boundary Refinement Module (BRM). The NSM filters noise information from the coarse-grained feature maps, while the BRM progressively refines the boundaries of significant lesion objects. Our method demonstrates superior accuracy over state-of-the-art deep learning models, achieving significant improvements of 3.67% on Dataset B and 2.30% on the BUSI dataset in mDice for testing malignant tumors.

A bi-directional segmentation method for prostate ultrasound images under semantic constraints.

Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solution for TRUS image segmentation using an end-to-end bidirectional semantic constraint method, namely the BiSeC model. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the Dice Similarity Coefficient (DSC) of 96.74% and the Intersection over Union (IoU) of 93.71%. Our model achieves a good balance between actual boundaries and noise areas, reducing costs while ensuring the accuracy and speed of segmentation.

RL-TEE: Autonomous Probe Guidance for Transesophageal Echocardiography Based on Attention-Augmented Deep Reinforcement Learning

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

RMAU-Net: Breast Tumor Segmentation Network Based on Residual Depthwise Separable Convolution and Multiscale Channel Attention Gates

Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization.

Self-supervised dual-head attentional bootstrap learning network for prostate cancer screening in transrectal ultrasound images

Current convolutional neural network-based ultrasound automatic classification models for prostate cancer often rely on extensive manual labeling. Although Self-supervised Learning (SSL) have shown promise in addressing this problem, those data that from medical scenarios contains intra-class similarity conflicts, so using loss calculations directly that include positive and negative sample pairs can mislead training. SSL method tends to focus on global consistency at the image level and does not consider the internal informative relationships of the feature map. To improve the efficiency of prostate cancer diagnosis, using SSL method to learn key diagnostic information in ultrasound images, we proposed a self-supervised dual-head attentional bootstrap learning network (SDABL), including Online-Net and Target-Net. Self-Position Attention Module (SPAM) and adaptive maximum channel attention module (CAAM) are inserted in both paths simultaneously. They captures position and inter-channel attention and of the original feature map with a small number of parameters, solve the information optimization problem of feature maps in SSL. In loss calculations, we discard the construction of negative sample pairs, and instead guide the network to learn the consistency of the location space and channel space by drawing closer to the embedding representation of positive samples continuously. We conducted numerous experiments on the prostate Transrectal ultrasound (TRUS) dataset, experiments show that our SDABL pre-training method has significant advantages over both mainstream contrast learning methods and other attention-based methods. Specifically, the SDABL pre-trained backbone achieves 80.46% accuracy on our TRUS dataset after fine-tuning.

An Intelligent Breast Ultrasound System for Diagnosis and 3D Visualization

Background: Ultrasonography is the main examination method for breast diseases. Ultrasound imaging is currently relied upon by doctors to form statements of characteristics and locations of lesions, which severely limits the completeness and effectiveness of ultrasound image information. Moreover, analyzing ultrasonography requires experienced ultrasound doctors, which are not common in hospitals. Thus, this work proposes a 3D-based breast ultrasound system, which can automatically diagnose ultrasound images of the breasts and generate a representative 3D breast lesion model through typical ultrasonography. Methods: In this system, we use a weighted ensemble method to combine three different neural networks and explore different combinations of the neural networks. On this basis, a breast locator was designed to measure and transform the spatial position of lesions. The breast ultrasound software generates a 3D visualization report through the selection and geometric transformation of the nodular model. Results: The ensemble neural network improved in all metrics compared with the classical neural network (DenseNet, AlexNet, GoogLeNet, etc.). It proved that the ensemble neural network proposed in this work can be used for intelligent diagnosis of breast ultrasound images. For 3D visualization, magnetic resonance imaging (MRI) scans were performed to achieve their 3D reconstructions. By comparing two types of visualized results (MRI and our 3D model), we determined that models generated by the 3D-based breast ultrasound system have similar nodule characteristics and spatial relationships with the MRI. Conclusions: In summary, this system implements automatic diagnosis of ultrasound images and presents lesions through 3D models, which can obtain complete and accurate ultrasound image information. Thus, it has clinical potential.

Ultrasonic Image Optimization based on Double Constraint Algorithm

The study of ultrasonic image deblurring is of high research value and practical significance, which can provide significantly useful medical pathological information for doctors and patients. Ultrasonic images are prone to speckle noise or blur. Real-time deblurring of ultrasonic images is a common problem in medical circles in China and globally. In this paper, based on the causes of ultrasound image degradation and its degradation model, the problem of ultrasound image deblurring is studied in depth. A new regularization constraint model is given, and the algorithm of ultrasound image deblurring with double constraints is effective. Through the experiment, the image quality evaluation index is used to evaluate the experimental results. The improved algorithm proposed in this paper improves the peak signal-to-noise ratio by 7%, the structural similarity value by 6%, the visual information fidelity by 14%, the feature similarity value by 4%, the resolution gain by 4%, and the new blind image quality evaluation value by 22%. The key information of the image can be kept faithfully. Compared with the traditional methods, the proposed method enhances the identification of the focus area in ultrasound images, provides a fast, efficient and complete deblurring method for medical image analysis, and faithfully retains and restores the required information in ultrasound images.

Deep Learning Algorithm-Based Ultrasound Image Information in Diagnosis and Treatment of Pernicious Placenta Previa.

This study was to explore the value of the deep dictionary learning algorithm in constructing a B ultrasound scoring system and exploring its application in the clinical diagnosis and treatment of pernicious placenta previa (PPP). 60 patients with PPP were divided into a low-risk group (severe, implantable) and high-risk group (adhesive, penetrating) according to their clinical characteristics, B ultrasound imaging characteristics, and postpartum pathological examination results. Under PPP ultrasonic image information using the deep learning algorithm, the B ultrasound image diagnostic scoring system was established to predict the depth of various types of placenta accreta. The results showed that the cut-off values of severe, implantable, adhesive, and penetrating types were <2.3, 2.3-6.5, 6.5-9, and ≥9 points, respectively; there were significant differences in the termination of pregnancy and neonatal birth weight between the two groups (P < 0.05); the positive predictive value, negative predictive value, and false positive rate of ultrasound images based on the deep dictionary learning algorithm for PPP were 95.33%, 94.89%, and 3.56%, respectively. Thus, the ultrasound image diagnostic scoring system based on the deep learning algorithm has an important predictive role for PPP, which can provide a more targeted diagnosis and treatment plan for patients in clinical practice and improve the prediction and treatment efficiency.

Diagnosis of Thyroid Nodules Based on Image Enhancement and Deep Neural Networks

The diagnosis of thyroid nodules at an early stage is a challenging task. Manual diagnosis of thyroid nodules is labor-intensive and time-consuming. Meanwhile, due to the difference of instruments and technical personnel, the original thyroid nodule ultrasound images collected are very different. In order to make better use of ultrasound image information of thyroid nodules, some image processing methods are indispensable. In this paper, we developed a method for automatic thyroid nodule classification based on image enhancement and deep neural networks. The selected image enhancement method is histogram equalization, and the neural networks have four-layer network nodes in our experiments. The dataset in this paper consists of thyroid nodule images of 508 patients. The data are divided into 80% training and 20% validation sets. A comparison result demonstrates that our method can achieve a better performance than other normal machine learning methods. The experimental results show that our method has achieved 0.901961 accuracy, 0.894737 precision, 1 recall, and 0.944444 F1-score. At the same time, we also considered the influence of network structure, activation function of network nodes, number of training iterations, and other factors on the classification results. The experimental results show that the optimal network structure is 2500-40-2-1, the optimal activation function is logistic function, and the best number of training iterations is 500.

Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image.

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p=0.000 < 0.05. Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p=0.128 > 0.05). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 (p < 0.05). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation.

Background:Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM.Methods:A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves.Results:A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949.Conclusions:The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

Predictive Diagnosis of Neurological Function in Patients with Atherosclerotic Cerebral Arterial Stenosis by Transcranial Doppler Ultrasound Imaging Information Technology

Objective: The purpose of this experiment is to observe the clinical effect of transcranial ultrasound thrombolytic adjuvant therapy, and to provide clinical results for transcranial ultrasound thrombolytic therapy for atherosclerotic cerebral arterial stenosis. in accordance with. Methods: Collected in the Department of Neurology, Brain Hospital of our hospital within 24 hours after the onset from October 2012 to April 2014, DSA and/or MRA, CTA examination confirmed the existence of middle cerebral artery stenosis and evidence of cerebral atherosclerosis, 100 patients with narrow middle cerebral artery as the diagnostic criteria for cerebral infarction. Results: The positive rates of microemboli in the ultrasound thrombolysis group at 7 days and 14 days were 17.00% and 6.00% significantly lower than those in the drug treatment group at 67.00% and 8.30% (P &lt; 0.05); the 7 days and 14 days of ultrasound thrombolysis treatment group Serum high-sensitivity (P &lt; 0.05), and serum C-reactive protein levels in the ultrasound thrombolytic treatment group were significantly lower than those in the drug treatment group (P &lt; 0.05). Conclusion: Transcranial ultrasound thrombolytic adjuvant drug treatment can significantly promote the recovery of neurological function.

WITHDRAWN: Efficacy of High-frequency Ultrasound Image Information Diagnosis on Neurological-abnormality in Patients with Type-2-diabetes Combined with Peripheral- neuropathy

This article has been withdrawn at the request of the Editor-in-Chief. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

A risk model to predict severe postpartum hemorrhage in patients with placenta previa: a single-center retrospective study.

The study aimed to establish a predictive risk model for severe postpartum hemorrhage in placenta previa using clinical and placental ultrasound imaging performed prior to delivery. Postpartum hemorrhage patients were retrospectively enrolled. Severe postpartum hemorrhage was defined as exceeding 1,500 mL. Data collected included clinical and placental ultrasound images. Age of pregnancy, time of delivery, time of miscarriage, history of vaginal delivery, gestational weeks at pregnancy termination, depth of placenta invading the uterine muscle wall were independent risk factors for severe postpartum hemorrhage in placenta previa. A model to predict severe postpartum hemorrhage in placenta previa was established: P=Log(Y/1-Y), where Y =-6.942 + 0.075 X1 (age) +1.531 X2 (times of delivery) + 0.223 X3 (time of miscarriage) - 3.557X4 (vaginal delivery: 1 for yes, 0 for no) + 1.753 X5 (0 for <37 weeks, 1 for ≥37 weeks) + 1.574 X6 (Depth of placenta invading uterine muscle wall: 0 for normal, 1 for placenta adhesion, 2 for placenta implantation, 3 for placenta penetration); discriminant boundary value of the prediction model (probability: P) was 0.268. Predicting sensitivity (Se) =0.765 (negative predicting accuracy rate), specificity (Sp) =0.900 (positive predicting accuracy rate), total accuracy rate =0.8000, and AUC of ROC curve =0.840. The risk prediction model which had clinical and ultrasound imaging information prior to delivery had a high decision accuracy. However, before it can be used in the clinic, multicenter large-sample clinical studies should be performed to verify its accuracy and reliability.

EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER

Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contrast. If global scheme is adopted for segmentation the high frequency edges may appear as artifacts. Hence to overcome this the wavelet transform of co-occurrence matrix is obtained and each decomposed band is subjected to an averaging filter. The process bands are thresholded using Otsu’s method and a binary image is obtained. The isolated pixels are removed by using suitable morphological operations. Then inverse wavelet transform is performed to obtain the image skeleton. The resultant image is subjected to watershed segmentation using gradients. Using the above mentioned approach we can see that the regions of interest are clearly segmented and is producing reproducible results.

Deep learning for ultrasound image caption generation based on object detection

Deep learning for image caption generation makes great progress in the field of natural images. However, there are still lack of effective methods for detailed analysis and automatic description of diseases content information in ultrasound image understanding. In order to find the location of focus areas, and understand the content of focus areas conveniently, we propose a novel method of ultrasound image captioning generation based on region detection. The method simultaneously detects and encodes the focus areas in ultrasound images, then utilizes the LSTM to decode the encoding vectors and generate annotation text information to describe the diseases content information in ultrasound images. The experimental results show that the method can accurately detect the location of the focus area, and also improves 1% the scores of BLEU-1, BLEU-2 with less parameters and running time, which compared with the full-size-image captioning model for ultrasound images.

Wavelet and Total Variation Based Method Using Adaptive Regularization for Speckle Noise Reduction in Ultrasound Images

Ultrasound (US) images are useful in medical diagnosis. US is preferred over other medical diagnosis technique because it is non-invasive in nature and has low cost. The presence of speckle noise in US images degrades its usefulness. A method that reduces the speckle noise in US images can help in correct diagnosis. This method also should preserve the important structural information in US images while removing the speckle noise. In this paper, a method for removing speckle noise using a combination of wavelet, total variation (TV) and morphological operations has been proposed. The proposed method achieves denoising by combining the advantages of the wavelet, TV and morphological operations along with the utilization of adaptive regularization parameter which controls the amount of smoothing during denoising. The work in this paper has the capability of reducing speckle noise while preserving the structural information in the denoised image. The proposed method demonstrates strong denoising for synthetic and real ultrasound images, which is also supported by the results of various quantitative measures and visual inspection.

Technology steps towards portable 3D ultrasound

Significant strides have been made in the evolution of portable ultrasound with system becoming increasing smaller and power requirements decreasing. Commercial systems have concentrated largely on reaching a compact size and portability that has arguably come at some performance cost. Initially systems were lower channel and element count and restricted to b-mode imaging of reduced quality. However, significant advances have been made in these systems such that one can now readily envision the development of portable 3D ultrasound. This “Stethoscope of the Future” would combining the ultrasound imaging information and its presentation in a manner that would be more readily interpretable, allowing for more consistent diagnoses and assistance in obtaining appropriate images of high quality. There exist some challenges in reaching the next level of performance and doing so in the context of 3D/4D ultrasound imaging. Power consumption affects not only requisite battery capacity but depending on where power is consumed and efficiency can result in heating complications. As more of the operational components more to the scanhead, thermal regulation becomes an increasingly important consideration. Management of data sufficient for 3D/4D requires more intelligent beamforming (e.g. synthetic aperture, plane wave imaging, separable beamforming) to maintain image quality. This is all at the same time as there is an expanding demand on performance for new imaging modes such as elastography requiring motion (speckle) tracking. In this talk, some of the current methods being pursued to address these considerations will be presented and discussed along with data processing and hardware approaches that might be applied in the future.

Liver tracking system utilizing template matching and energy function in high intensity focused ultrasound/radio frequency ablation therapy

Monitoring and evaluating the therapeutic effects, in ultrasound images during HIFU (High Intensity Focused Ultrasound) and RFA (Radio Frequency Ablation) therapies, are important. However, the common problem is the difficulty to monitor the progress and identify the positions of the focal lesion precisely in accordance with the progress of the treatment. This problem is caused by the movement of organs and the change of the textures information in ultrasound images. To overcome those problems, we have developed a novel method to track, follow, and monitor the focal lesion by the combination of the energy function method and the template matching method. The template matching method uses the characteristic texture information of the organ near the focal lesion. The energy function is implemented in order to reinforce the robustness of the tracking performance of the template matching method. Particularly, we evaluate the existing probability of the focal lesion considering the continuity of the movement and the relative distance from the surface of the liver. The estimated position of the focal lesion is obtained by minimizing the above mentioned energy function. The effectiveness of the proposed method is confirmed by comparing the novel proposed method and the conventional template matching method in terms of the tracking performance for the liver tumor during the real RFA treatments.

Automatic 3D ultrasound calibration for image guided therapy using intramodality image registration

Many real time ultrasound (US) guided therapies can benefit from management of motion-induced anatomical changes with respect to a previously acquired computerized anatomy model. Spatial calibration is a prerequisite to transforming US image information to the reference frame of the anatomy model. We present a new method for calibrating 3D US volumes using intramodality image registration, derived from the ‘hand-eye’ calibration technique. The method is fully automated by implementing data rejection based on sensor displacements, automatic registration over overlapping image regions, and a self-consistency error metric evaluated continuously during calibration. We also present a novel method for validating US calibrations based on measurement of physical phantom displacements within US images. Both calibration and validation can be performed on arbitrary phantoms. Results indicate that normalized mutual information and localized cross correlation produce the most accurate 3D US registrations for calibration. Volumetric image alignment is more accurate and reproducible than point selection for validating the calibrations, yielding <1.5 mm root mean square error, a significant improvement relative to previously reported hand-eye US calibration results. Comparison of two different phantoms for calibration and for validation revealed significant differences for validation (p = 0.003) but not for calibration (p = 0.795).