Open access segmentations of intraoperative brain tumor ultrasound images.

PURPOSE: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery. ACQUISITION AND VALIDATION METHODS: The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations. DATA FORMAT AND USAGE NOTES: Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk. POTENTIAL APPLICATIONS: The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.

On the soft tissue ultrasound elastography using FEM based inversion approach.

Elastography is a medical imaging modality that enables visualization of tissue stiffness. It involves quasi-static or harmonic mechanical stimulation of the tissue to generate a displacement field which is used as input in an inversion algorithm to reconstruct tissue elastic modulus. This paper considers quasi-static stimulation and presents a novel inversion technique for elastic modulus reconstruction. The technique follows an inverse finite element framework. Reconstructed elastic modulus maps produced in this technique do not depend on the initial guess, while it is computationally less involved than iterative reconstruction approaches. The method was first evaluated using simulated data (in-silico) where modulus reconstruction's sensitivity to displacement noise and elastic modulus was assessed. To demonstrate the method's performance, displacement fields of two tissue mimicking phantoms determined using three different motion tracking techniques were used as input to the developed elastography method to reconstruct the distribution of relative elastic modulus of the inclusion to background tissue. In the next stage, the relative elastic modulus of three clinical cases pertaining to liver cancer patient were determined. The obtained results demonstrate reasonably high elastic modulus reconstruction accuracy in comparison with similar direct methods. Also it is associated with reduced computational cost in comparison with iterative techniques, which suffer from convergence and uniqueness issues, following the same formulation concept. Moreover, in comparison with other methods which need initial guess, the presented method does not require initial guess while it is easy to understand and implement.

Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography.

BACKGROUND: Ultrasound strain imaging, which delineates mechanical properties to detect tissue abnormalities, involves estimating the time delay between two radio-frequency (RF) frames collected before and after tissue deformation. The existing regularized optimization-based time-delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first-order displacement derivative into account; (2) The L 2 $L2$ -norm of the displacement derivatives, which oversmooths the estimated time-delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of L 1 $L1$ -norm. PURPOSE: Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms. METHODS: Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of L 2 $L2$ -norm data fidelity term and L 1 $L1$ -norm first- and second-order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high-contrast strain images with smooth backgrounds and sharp boundaries. We name our technique ADMM for totaL variaTion RegUlarIzation in ultrasound STrain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and L 1 $L1$ -SOUL, three recently published energy-based techniques, and UMEN-Net, a state-of-the-art deep learning-based algorithm. Analysis of variance (ANOVA)-led multiple comparison tests and paired t $t$ -tests at 5 % $5\%$ overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard-inclusion simulated phantom, one multi-inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at http://code.sonography.ai. RESULTS: ALTRUIST substantially outperforms the four state-of-the-art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to 573 % ∗ ${573\%}^{*}$ , 41 % ∗ ${41\%}^{*}$ , and 51 % ∗ ${51\%}^{*}$ SNR improvements and 443 % ∗ ${443\%}^{*}$ , 53 % ∗ ${53\%}^{*}$ , and 15 % ∗ ${15\%}^{*}$ CNR improvements over L 1 $L1$ -SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA-led multiple comparison tests and paired t $t$ -tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN-Net, OVERWIND, and L 1 $L1$ -SOUL in terms of AE, SSIM map, SNR, and CNR. CONCLUSIONS: A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an L 1 $L1$ -norm regularization-based cost function. ALTRUIST exhibits promising performance in simulation, phantom, and in vivo experiments.

The Big Bang of Deep Learning in Ultrasound-Guided Surgery: A Review.

Ultrasound (US) imaging is a paramount modality in many image-guided surgeries and percutaneous interventions, thanks to its high portability, temporal resolution, and cost-efficiency. However, due to its imaging principles, the US is often noisy and difficult to interpret. Appropriate image processing can greatly enhance the applicability of the imaging modality in clinical practice. Compared with the classic iterative optimization and machine learning (ML) approach, deep learning (DL) algorithms have shown great performance in terms of accuracy and efficiency for US processing. In this work, we conduct a comprehensive review on deep-learning algorithms in the applications of US-guided interventions, summarize the current trends, and suggest future directions on the topic.

Segmentation of Arm Ultrasound Images in Breast Cancer-Related Lymphedema: A Database and Deep Learning Algorithm.

OBJECTIVE: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms look similar in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of tissue layers. METHODS: For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects and manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of 0.94±0.08 and 0.92±0.06, respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. RESULTS: We got an average DSC of 0.87±0.11 on the test set, which confirms the high performance of the method. CONCLUSION: Automatic segmentation can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. SIGNIFICANCE: Timely diagnosis and treatment of BCRL have crucial importance in preventing irreversible damage.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

PURPOSE: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods in treatment decision-making. METHODS: We adapted a pre-trained EfficientNet B0 network through transfer learning to improve collateral evaluation using slice-based and subject-level classification. Our method uses stacking and overlapping of 2D slices from a patient's 4D computed tomography angiography (CTA) and a majority voting scheme to determine a patient's final collateral grade based on all classified 2D MIPs. Class imbalance is handled in the evaluation process by using the focal loss with class weight to penalize the majority class. RESULTS: We evaluated our method using a nine-fold cross-validation performed with 83 subjects. Mean sensitivity of 0.71, specificity of 0.84, and a weighted F1 score of 0.71 in multi-class (good, intermediate, and poor) classification were obtained. Considering treatment effect, a dichotomized decision is also made for collateral scoring of a subject based on two classes (good/intermediate and poor) which achieves a sensitivity of 0.89 and specificity of 0.96 with a weighted F1 score of 0.95. CONCLUSION: An automatic and robust collateral assessment method that mitigates the issues with the small imbalanced dataset was developed. Computer-aided evaluation of collaterals can help decision-making of ischemic stroke treatment strategy in clinical settings.

Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection.

PURPOSE: In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety. METHODS: We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages. An efficient 2.5D approach and an iterative re-weighted least squares algorithm are utilized to perform landmark-based registration for brain shift correction. The proposed method is validated and compared against the state-of-the-art methods using the public BITE and RESECT datasets. RESULTS: Registration of pre-resection iUS scans to during- and post-resection iUS images were executed. The results with the proposed method shows a significant improvement from the initial misalignment ([Formula: see text]) and the method is comparable to the state-of-the-art methods validated on the same datasets. CONCLUSIONS: We have proposed a robust technique to efficiently detect matching landmarks in iUS and perform brain shift correction with excellent performance. It has the potential to improve the accuracy and safety of neurosurgery.

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR.

PURPOSE: Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task. METHODS: We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration. RESULTS: The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN). CONCLUSIONS: Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor .

Vision Transformers for Classification of Breast Ultrasound Images.

Medical ultrasound (US) imaging has become a prominent modality for breast cancer imaging due to its ease of use, low cost, and safety. In the past decade, convolutional neural networks (CNNs) have emerged as the method of choice in vision applications and have shown excellent potential in the automatic classification of US images. Despite their success, their restricted local receptive field limits their ability to learn global context information. Recently, Vision Transformer (ViT) designs, based on self-attention between image patches, have shown great potential to be an alternative to CNNs. In this study, for the first time, we utilize ViT to classify breast US images using different augmentation strategies. We also adopted a weighted cross-entropy loss function since breast ultrasound datasets are often imbalanced. The results are provided as classification accuracy and Area Under the Curve (AUC) metrics, and the performance is compared with the SOTA CNNs. The results indicate that the ViT models have comparable efficiency with or even better than the CNNs in the classification of US breast images. Clinical relevance- This work shows the potential of Vision Transformers in the automatic classification of masses in breast ultrasound, which helps clinicians diagnose and make treatment decisions more precisely.

Fast and robust localization of surgical array using Kalman filter.

PROBLEM: Intraoperative tracking of surgical instruments is an inevitable task of computer-assisted surgery. An optical tracking system often fails to precisely reconstruct the dynamic location and pose of a surgical tool due to the acquisition noise and measurement variance. Embedding a Kalman filter (KF) or any of its extensions such as extended and unscented Kalman filters (EKF and UKF) with the optical tracker resolves this issue by reducing the estimation variance and regularizing the temporal behavior. However, the current KF implementations are computationally burdensome and hence takes long execution time which hinders real-time surgical tracking. AIM: This paper introduces a fast and computationally efficient implementation of linear KF to improve the measurement accuracy of an optical tracking system with high temporal resolution. METHODS: Instead of the surgical tool as a whole, our KF framework tracks each individual fiducial mounted on it using a Newtonian model. In addition to simulated dataset, we validate our technique against real data obtained from a high frame-rate commercial optical tracking system. We also perform experiments wherein a diffusive material (such as a drop of blood) blocks one of the fiducials and show that KF can substantially reduce the tracking error. RESULTS: The proposed KF framework substantially stabilizes the tracking behavior in all of our experiments and reduces the mean-squared error (MSE) by a factor of 26.84, from the order of [Formula: see text] to [Formula: see text] mm[Formula: see text]. In addition, it exhibits a similar performance to UKF, but with a much smaller computational complexity.

Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms.

PURPOSE: Accurate multimodal registration of intraoperative ultrasound (US) and preoperative computed tomography (CT) is a challenging problem. Construction of public datasets of US and CT images can accelerate the development of such image registration techniques. This can help ensure the accuracy and safety of spinal surgeries using image-guided surgery systems where an image registration is employed. In addition, we present two algorithms to register US and CT images. METHODS: We present three different datasets of vertebrae with corresponding CT, US, and simulated US images. For each of the two latter datasets, we also provide 16 landmark pairs of matching structures between the CT and US images and performed fiducial registration to acquire a silver standard for assessing image registration. Besides, we proposed two patch-based rigid image registration algorithms, one based on normalized cross-correlation (NCC) and the other based on correlation ratio (CR) to register misaligned CT and US images. RESULTS: The CT and corresponding US images of the proposed database were pre-processed and misaligned with different error intervals, resulting in 6000 registration problems solved using both NCC and CR methods. Our results show that the methods were successful in aligning the pre-processed CT and US images by decreasing the warping index. CONCLUSIONS: The database provides a resource for evaluating image registration techniques. The simulated data have two applications. First, they provide the gold standard ground-truth which is difficult to obtain with ex vivo and in vivo data for validating US-CT registration methods. Second, the simulated US images can be used to validate real-time US simulation methods. Besides, the proposed image registration techniques can be useful for developing methods in clinical application.

A Tissue Mechanics Based Method to Improve Tissue Displacement Estimation in Ultrasound Elastography.

Cancer is known to induce significant structural changes to tissue. In most cancers, including breast cancer, such changes yield tissue stiffening. As such, imaging tissue stiffness can be used effectively for cancer diagnosis. One such imaging technique, ultrasound elastography, has emerged with the aim of providing a low-cost imaging modality for effective breast cancer diagnosis. In quasi-static breast ultrasound elastography, the breast is stimulated by ultrasound probe, leading to tissue deformation. The tissue displacement data can be estimated using a pair of acquired ultrasound radiofrequency (RF) data pertaining to pre- and post-deformation states. The data can then be used within a mathematical framework to construct an image of the tissue stiffness distribution. Ultrasound RF data is known to include significant noise which lead to corruption of estimated displacement fields, especially the lateral displacements. In this study, we propose a tissue mechanics-based method aiming at improving the quality of estimated displacement data. We applied the method to RF data acquired from a tissue-mimicking phantom. The results indicated that the method is effective in improving the quality of the displacement data.

Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition.

PURPOSE: Sufficient collateral blood supply is crucial for favorable outcomes with endovascular treatment. The current practice of collateral scoring relies on visual inspection and thus can suffer from inter and intra-rater inconsistency. We present a robust and automatic method to score cerebral collateral blood supply to aid ischemic stroke treatment decision making. The developed method is based on 4D dynamic CT angiography (CTA) and the ASPECTS scoring protocol. METHODS: The proposed method, ACCESS (Automatic Collateral Circulation Evaluation in iSchemic Stroke), estimates a target patient's unfilled cerebrovasculature in contrast-enhanced CTA using the lack of contrast agent due to clotting. To do so, the fast robust matrix completion algorithm with in-face extended Frank-Wolfe optimization is applied on a cohort of healthy subjects and a target patient, to model the patient's unfilled vessels and the estimated full vasculature as sparse and low-rank components, respectively. The collateral score is computed as the ratio of the unfilled vessels to the full vasculature, mimicking existing clinical protocols. RESULTS: ACCESS was tested with 46 stroke patients and obtained an overall accuracy of 84.78%. The optimal threshold selection was evaluated using a receiver operating characteristics curve with the leave-one-out approach, and a mean area under the curve of 85.39% was obtained. CONCLUSION: ACCESS automates collateral scoring to mitigate the shortcomings of the standard clinical practice. It is a robust approach, which resembles how radiologists score clinical scans, and can be used to help radiologists in clinical decisions of stroke treatment.

Two-stage ultrasound image segmentation using U-Net and test time augmentation.

PURPOSE: Detecting breast lesions using ultrasound imaging is an important application of computer-aided diagnosis systems. Several automatic methods have been proposed for breast lesion detection and segmentation; however, due to the ultrasound artefacts, and to the complexity of lesion shapes and locations, lesion or tumor segmentation from ultrasound breast images is still an open problem. In this paper, we propose using a lesion detection stage prior to the segmentation stage in order to improve the accuracy of the segmentation. METHODS: We used a breast ultrasound imaging dataset which contained 163 images of the breast with either benign lesions or malignant tumors. First, we used a U-Net to detect the lesions and then used another U-Net to segment the detected region. We could show when the lesion is precisely detected, the segmentation performance substantially improves; however, if the detection stage is not precise enough, the segmentation stage also fails. Therefore, we developed a test-time augmentation technique to assess the detection stage performance. RESULTS: By using the proposed two-stage approach, we could improve the average Dice score by 1.8% overall. The improvement was substantially more for images wherein the original Dice score was less than 70%, where average Dice score was improved by 14.5%. CONCLUSIONS: The proposed two-stage technique shows promising results for segmentation of breast US images and has a much smaller chance of failure.

Improving 3D ultrasound prostate localisation in radiotherapy through increased automation of interfraction matching.

BACKGROUND AND PURPOSE: Daily image guidance is standard care for prostate radiotherapy. Innovations which improve the accuracy and efficiency of ultrasound guidance are needed, particularly with respect to reducing interobserver variation. This study explores automation tools for this purpose, demonstrated on the Elekta Clarity Autoscan®. The study was conducted as part of the Clarity-Pro trial (NCT02388308). MATERIALS AND METHODS: Ultrasound scan volumes were collected from 32 patients. Prostate matches were performed using two proposed workflows and the results compared with Clarity's proprietary software. Gold standard matches derived from manually localised landmarks provided a reference. The two workflows incorporated a custom 3D image registration algorithm, which was benchmarked against a third-party application (Elastix). RESULTS: Significant reductions in match errors were reported from both workflows compared to standard protocol. Median (IQR) absolute errors in the left-right, anteroposterior and craniocaudal axes were lowest for the Manually Initiated workflow: 0.7(1.0) mm, 0.7(0.9) mm, 0.6(0.9) mm compared to 1.0(1.7) mm, 0.9(1.4) mm, 0.9(1.2) mm for Clarity. Median interobserver variation was ≪0.01 mm in all axes for both workflows compared to 2.2 mm, 1.7 mm, 1.5 mm for Clarity in left-right, anteroposterior and craniocaudal axes. Mean matching times was also reduced to 43 s from 152 s for Clarity. Inexperienced users of the proposed workflows attained better match precision than experienced users on Clarity. CONCLUSION: Automated image registration with effective input and verification steps should increase the efficacy of interfraction ultrasound guidance compared to the current commercially available tools.

Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge.

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

18F-FACBC PET/MRI in Diagnostic Assessment and Neurosurgery of Gliomas.

PURPOSE: This pilot study aimed to evaluate the amino acid tracer F-FACBC with simultaneous PET/MRI in diagnostic assessment and neurosurgery of gliomas. MATERIALS AND METHODS: Eleven patients with suspected primary or recurrent low- or high-grade glioma received an F-FACBC PET/MRI examination before surgery. PET and MRI were used for diagnostic assessment, and for guiding tumor resection and histopathological tissue sampling. PET uptake, tumor-to-background ratios (TBRs), time-activity curves, as well as PET and MRI tumor volumes were evaluated. The sensitivities of lesion detection and to detect glioma tissue were calculated for PET, MRI, and combined PET/MRI with histopathology (biopsies for final diagnosis and additional image-localized biopsies) as reference. RESULTS: Overall sensitivity for lesion detection was 54.5% (95% confidence interval [CI], 23.4-83.3) for PET, 45.5% (95% CI, 16.7-76.6) for contrast-enhanced MRI (MRICE), and 100% (95% CI, 71.5-100.0) for combined PET/MRI, with a significant difference between MRICE and combined PET/MRI (P = 0.031). TBRs increased with tumor grade (P = 0.004) and were stable from 10 minutes post injection. PET tumor volumes enclosed most of the MRICE volumes (>98%) and were generally larger (1.5-2.8 times) than the MRICE volumes. Based on image-localized biopsies, combined PET/MRI demonstrated higher concurrence with malignant findings at histopathology (89.5%) than MRICE (26.3%). CONCLUSIONS: Low- versus high-grade glioma differentiation may be possible with F-FACBC using TBR. F-FACBC PET/MRI outperformed MRICE in lesion detection and in detection of glioma tissue. More research is required to evaluate F-FACBC properties, especially in grade II and III tumors, and for different subtypes of gliomas.

Combining Total Variation Regularization with Window-Based Time Delay Estimation in Ultrasound Elastography.

A major challenge of free-hand palpation ultrasound elastography (USE) is estimating the displacement of RF samples between pre- and post-compressed RF data. The problem of displacement estimation is ill-posed since the displacement of one sample by itself cannot be uniquely calculated. To resolve this problem, two categories of methods have emerged. The first category assumes that the displacement of samples within a small window surrounding the reference sample is constant. The second class imposes smoothness regularization and optimizes an energy function. Herein, we propose a novel method that combines both approaches, and as such, is more robust to noise. The second contribution of this work is the introduction of the L1 norm as the regularization term in our cost function, which is often referred to as the total variation (TV) regularization. Compared to previous work that used the L2 norm regularization, optimization of the new cost function is more challenging. However, the advantages of using the L1 norm are twofold. First, it leads to substantial improvement in the sharpness of displacement estimates. Second, to optimize the cost function with the L1 norm regularization, we use an iterative method that further increases the robustness. We name our proposed method tOtal Variation Regularization and WINDow-based time delay estimation (OVERWIND) and show that it is robust to signal decorrelation and generates sharp displacement and strain maps for simulated, experimental phantom and in-vivo data. In particular, OVERWIND improves strain contrast-to-noise ratio (CNR) by 27.26%, 144.05%, and 49.90% on average in simulation, phantom, and in-vivo data, respectively, compared to our recent Global Ultrasound Elastography (GLUE) method.

ARENA: Inter-modality affine registration using evolutionary strategy.

PURPOSE: Image fusion of different imaging modalities renders valuable information to clinicians. In this paper, we proposed an automatic multimodal registration method to register intra-operative ultrasound images (US) to preoperative magnetic resonance images (MRI) in the context of image-guided neurosurgery. METHODS: We employed refined correlation ratio as a similarity metric for our intensity-based image registration method. We deem MRI as the fixed image ([Formula: see text]) and US as the moving image ([Formula: see text]) and then transform [Formula: see text] to align with [Formula: see text]. We utilized the covariance matrix adaptation evolutionary strategy to find the optimal affine transformation in registration of [Formula: see text] to [Formula: see text]. RESULTS: We applied our method on the publicly available retrospective evaluation of cerebral tumors (RESECT) database and Montreal Neurological Institute's brain images of tumors for evaluation (BITE) database. We validated the results qualitatively and quantitatively. Qualitative validation is conducted (by the three authors) through overlaying pre- and post-registration US and MRI to allow visual assessment of the alignment. Quantitative validation is performed by utilizing the corresponding landmarks in the databases for the preoperative MRI and the intra-operative US. Average mean target registration error (mTRE) has been reduced from [Formula: see text] to [Formula: see text] in 22 patients in the RESECT database and from [Formula: see text] to [Formula: see text] in the BITE database. A nonparametric statistical analysis performed using the Wilcoxon rank sum test shows that there is a significant difference between pre- and post-registration mTREs with a p value of [Formula: see text] for the RESECT database and [Formula: see text] for the BITE database. CONCLUSIONS: The proposed fully automatic registration method significantly improved the alignment of MRI and US images and can therefore be used to reduce the misalignment of US and MRI caused by brain shift, calibration errors, and patient to MRI transformation matrix.

Assessment of Mechanical Properties of Tissue in Breast Cancer-Related Lymphedema Using Ultrasound Elastography.

Breast cancer-related lymphedema is a consequence of a malfunctioning lymphatic drainage system resulting from surgery or some other form of treatment. In the initial stages, minor and reversible increases in the fluid volume of the arm are evident. As the stages progress over time, the underlying pathophysiology dramatically changes with an irreversible increase in arm volume most likely due to a chronic local inflammation leading to adipose tissue hypertrophy and fibrosis. Clinicians have subjective ways to stage the degree and severity such as the pitting test which entails manually comparing the elasticity of the affected and unaffected arms. Several imaging modalities can be used but ultrasound appears to be the most preferred because it is affordable, safe, and portable. Unfortunately, ultrasonography is not typically used for staging lymphedema, because the appearance of the affected and unaffected arms is similar in B-mode ultrasound images. However, novel ultrasound techniques have emerged, such as elastography, which may be able to identify changes in mechanical properties of the tissue related to detection and staging of lymphedema. This paper presents a novel technique to compare the mechanical properties of the affected and unaffected arms using quasi-static ultrasound elastography to provide an objective alternative to the current subjective assessment. Elastography is based on time delay estimation (TDE) from ultrasound images to infer displacement and mechanical properties of the tissue. We further introduce a novel method for TDE by incorporating higher order derivatives of the ultrasound data into a cost function and propose a novel optimization approach to efficiently minimize the cost function. This method works reliably with our challenging patient data. We collected radio frequency ultrasound data from both arms of seven patients with stage 2 lymphedema, at six different locations in each arm. The ratio of strain in skin, subcutaneous fat, and skeletal muscle divided by strain in the standoff gel pad was calculated in the unaffected and affected arms. The p -values using a Wilcoxon sign-rank test for the skin, subcutaneous fat, and skeletal muscle were 1.24×10-5 , 1.77×10-8 , and 8.11×10-7 respectively, showing differences between the unaffected and affected arms with a very high level of significance.