Using Ultrasound Imaging Research Articles

Comprehensive Assessment of Ultrasound's Role in Carpal Tunnel Syndrome in Reference to Electromyography.

Carpal tunnel syndrome (CTS) is the most common peripheral mononeuropathy related to entrapment syndrome. An increase in the median nerve cross-sectional area (CSA) is visualized using ultrasound (US) imaging for CTS patients. This study aims to correlate, for the first time in the state of Qatar, the findings of US imaging and electromyography (EMG) in patients with CTS for diagnostic and classification purposes. First, US CSA was numerically obtained from patients' records. Second, EMG findings were gathered as normal, mild, moderate, and severe CTS. After that, we performed a different statistical approach than those used in the literature, using one-way analysis of variance (ANOVA) and post hoc tests for the final analysis. In summary, we found that the US seems unable to differentiate some normal from mild CTS cases; however, it appears to be excellent at differentiating moderate from severe CTS cases.

FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images

Recent studies have illustrated that the peritumoral regions of medical images have value for clinical diagnosis. However, the existing approaches using peritumoral regions mainly focus on the diagnostic capability of the single region and ignore the advantages of effectively fusing the intratumoral and peritumoral regions. In addition, these methods need accurate segmentation masks in the testing stage, which are tedious and inconvenient in clinical applications. To address these issues, we construct a deep convolutional neural network that can adaptively fuse the information of multiple tumoral-regions (FMRNet) for breast tumor classification using ultrasound (US) images without segmentation masks in the testing stage. To sufficiently excavate the potential relationship, we design a fused network and two independent modules to extract and fuse features of multiple regions simultaneously. First, we introduce two enhanced combined-tumoral (EC) region modules, aiming to enhance the combined-tumoral features gradually. Then, we further design a three-branch module for extracting and fusing the features of intratumoral, peritumoral, and combined-tumoral regions, denoted as the intratumoral, peritumoral, and combined-tumoral module. Especially, we design a novel fusion module by introducing a channel attention module to adaptively fuse the features of three regions. The model is evaluated on two public datasets including UDIAT and BUSI with breast tumor ultrasound images. Two independent groups of experiments are performed on two respective datasets using the fivefold stratified cross-validation strategy. Finally, we conduct ablation experiments on two datasets, in which BUSI is used as the training set and UDIAT is used as the testing set. We conduct detailed ablation experiments about the proposed two modules and comparative experiments with other existing representative methods. The experimental results show that the proposed method yields state-of-the-art performance on both two datasets. Especially, in the UDIAT dataset, the proposed FMRNet achieves a high accuracy of 0.945 and a specificity of 0.945, respectively. Moreover, the precision (PRE=0.909) even dramatically improves by 21.6% on the BUSI dataset compared with the existing method of the best result. The proposed FMRNet shows good performance in breast tumor classification with US images, and proves its capability of exploiting and fusing the information of multiple tumoral-regions. Furthermore, the FMRNet has potential value in classifying other types of cancers using multiple tumoral-regions of other kinds of medical images.

Volumetric Flow Estimation in a Coronary Artery Phantom Using High-Frame-Rate Contrast-Enhanced Ultrasound, Speckle Decorrelation, and Doppler Flow Direction Detection.

The coronary flow reserve (CFR), relating to the volumetric flow rate, is an effective functional parameter to assess the stenosis in the left anterior descending (LAD) coronary artery. We have recently proposed to use high-frame-rate (HFR) contrast-enhanced ultrasound (CEUS) to estimate the volumetric flow rate using ultrasound (US) speckle decorrelation (SDC) without any assumptions about the velocity profile. However, this method still has challenges in imaging deep and small vessels, such as LAD. In this study, we proposed to address the challenges and demonstrate the feasibility of volumetric flow rate measurement in a coronary mimicking phantom with pulsatile flow using a 1-D array cardiac probe, vector Doppler, and an optimal probe rotation/tilting for flow direction detection. Both simulations and in vitro experiments were conducted to validate the proposed method. It is shown that in-plane velocities estimated by vector Doppler under a 10° probe tilting resulted in smaller percentage error (+5.2%) in flow rate estimates than that in US imaging velocimetry (-20.2%) although their relative standard deviations were very close, being 2.6 and 2.8 ml/min, respectively. The flow rate estimated by SDC without direction detection had an error higher than 70%. A 10° tilting of the probe had the best results in flow rate estimation compared to the 5° or 15° tilting. Realistic global motions in the LAD increased the flow rate estimation error from 5.2% to 14.2%. It is concluded that it is feasible to measure the volumetric flow rate in a coronary artery flow phantom with a conventional cardiac probe, using HFR acquisition, Doppler, and SDC analysis. Potentially, this technique could also be applied to investigate the volumetric flow rate in other small vessels similar to the LAD.

Application of Ultrasound Images Texture Analysis for the Estimation of Intramuscular Fat Content in the Longissimus Thoracis Muscle of Beef Cattle after Slaughter: A Methodological Study.

Simple SummaryFat content in the muscle mass (IMF) is one of the most important characteristics influencing the aroma, tenderness, and juiciness of the meat and therefore has high importance for both commercialization purposes and consumers. However, IMF determination currently relies on visual inspection, which is a subjective and inconsistent technique. The aim of the present study is the elaboration of a procedure capable of predicting IMF% in beef carcasses using ultrasound imaging texture analysis. Ultrasound images taken on meat samples were compared to meat composition measured by chemical extraction. Determination coefficient between the two techniques was R2 = 0.76, while Receiver Operating Characteristic analysis showed a sensitivity of 88% and a specificity of 90%. The results therefore suggest that the described procedure is expected to determine IMF% in muscle with good accuracy. Ultrasound imaging could be applied in routine beef grading practices. This may help to solve the issues related to subjectivity and leave to the operator only imaging acquisition. Better consistency in beef products could enhance consumers’ satisfaction and commercial standardization programs.Intramuscular fat (IMF) is a major trait in the evaluation of beef meat, but its determination is subjective and inconsistent and still relies on visual inspection. This research objective was a method to predict IMF% from beef meat using ultrasound (US) imaging texture analysis. US images were performed on the longissimus thoracis muscle of 27 Charolaise heifers. Cuts from the 12th to 13th ribs were scanned. The lipid content of the muscle samples was determined with the petrol ether (Randall) extraction method. A stepwise linear discriminant analysis was used to screen US texture parameters. IMF% measured by chemical extraction (IMFqa) was the dependent variable and the results of the texture analysis were the explanatory variables. The model highlighted seven parameters, as a predictive and a multiple regression equation was created. Prediction of IMF content (IMFpred) was then validated using IMFqa as ground truth. Determination coefficient between IMFqa and IMFpred was R2 = 0.76, while the ROC analysis showing a sensitivity of 88% and a specificity of 90%. Bland-Altman plot upper and lower limit were +1.34 and −1.42, respectively (±1.96 SD), with a mean of −0.04. The results from the present study therefore suggest that prediction of IMF content in muscle mass by US texture analysis is possible.

Efektivitas Penggunaan Seleksi Ciri CFS pada Klasifikasi Ciri Bentuk Nodul Kanker Payudara dengan Citra Ultrasonografi

The implementation of nodule shape characteristics is one of the parameters used in determining breast cancer malignancy. Mathematical calculations are used as a second decision to strengthen radiologists in determining breast cancer malignancy using ultrasound images (USG). The method used in this research is to filter ultrasound images that contain speckle noise, then continue the segmentation process, extracting shape features, selecting shape features, and classifying them. The feature selection process using Correlated based Feature Selection (CFS) is used to select the dominant shape features in the image. The classification results obtained show that the results of feature selection using CFS can improve the results of image accuracy, sensitivity and specificity, so as to be able to better distinguish the characteristic shape of the cancer nodule.

Prediction of the Presence of Fluid Accumulation in the Subcutaneous Tissue in BCRL Using Texture Analysis of Ultrasound Images.

Background: Breast cancer-related lymphedema (BCRL) is a chronic swelling of the arm due to breast cancer treatment. Lymphedema is diagnosed and staged on the basis of limb circumference measurements and the patient's subjective symptoms, which have poor reproducibility and objectivity: these cannot detect any fluid accumulation in the tissue. Ultrasonography is a feasible noninvasive technique that can be used to evaluate tissue structure in real time. This study aimed to assess the ability of texture features for discriminating the presence of accumulated fluid within the subcutaneous tissue of BCRL using ultrasound (US) imaging. Methods and Results: This study included 20 women who were treated for unilateral breast cancer and who subsequently developed BCRL (International Society of Lymphology stage II). Subcutaneous tissue was scanned through an US system (Sonosite Edge II; Sonosite, Inc., FUJIFILM) using a 6- to 15-MHz linear transducer to assess the ability of texture features for discriminating the presence of accumulated fluid within the subcutaneous tissue of BCRL. Fluid accumulation was observed using a 3-Tesla MR system under double-echo steady-state conditions. There was a significant difference among the three groups (with hyperintense area, without hyperintense area, and unaffected side) in 11 of 14 textural features (p < 0.05). Post hoc analysis (Mann-Whitney U test; Bonferroni correction p < 0.0167) revealed significant differences in seven textural features within the hyperintense area. Conclusions: This study revealed that seven texture features quantified by US imaging data can provide information regarding fluid accumulation in the subcutaneous tissue of lymphedema.

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study.

BackgroundThe current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.MethodsTaking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.ResultsThe accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).ConclusionsThe CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registrationChictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.

Proposed ultrasound algorithm to differentiate thyroglossal duct and dermoid cysts

Objective1) To assess reproducibility of the previously established SIST score. 2) To determine inter-observer agreement in using ultrasound (US) characteristics to differentiate thyroglossal duct cyst (TGDC) from dermoid cysts (DC) 3) Improve the method used to pre-operatively differentiate TGDC from DC. Study designRetrospective chart review. SettingTertiary care pediatric hospital. Subjects and MethodsAn electronic medical record was queried to identify children with midline neck masses who underwent pre-operative neck US and had a histopathologic diagnosis of either TGDC or DC. Two pediatric radiologists, blinded to the pathologic diagnosis, evaluated the US images and documented the presence of pre-determined characteristics of each mass. Potential differentiating factors were analyzed for their predictive power. The SIST (septations, irregular walls, solid components = TGDC) score was determined as well as inter-observer agreement. Using the characteristics that had significant predictive power, we used the data to develop an algorithm to improve predicting cyst type. ResultsPathologically, there were 47 TGDC and 25 DC. The inter-observer agreement about the pathologic diagnosis between the two radiologists was substantial, K = 0.66. Overall, the SIST score predicted the correct diagnosis 67% of the time. Radiologist 1 and radiologist 2 were more accurate than the SIST score alone, making the correct diagnosis 96% and 86% of the time, respectively. In our study, we found that the most important US characteristics in differentiating TGDC and DC are: internal Septations, depth relative to Strap muscles, Shape and Solid parts (4 S algorithm). The SIST score criteria were individually shown to be significant and sensitive in recognizing DC, however, they were not specific and often misclassified TGDC as DC. We developed a new sequential filtering algorithm that more accurately differentiates cysts. This new algorithm uses step-wise filtering of characteristics, first for Septations, then for depth to Straps, then Shape of the cyst and lastly Solid parts (4 S algorithm). This algorithm correctly categorized cyst type in 100% of patients in our study. ConclusionsPre-operatively differentiating TGDC and DC continues to be a challenge. Using our 4 S algorithm, we can more definitively differentiate TGDC from DC compared to the SIST score. All SIST score characteristics were significant and sensitive in detecting dermoid cysts, however, not very specific. The radiologists’ judgment and accuracy was better than the SIST score. The 4 S algorithm uses sequential filtering of important characteristics: Septations, depth to Straps, Shape of cyst and lastly Solid parts to improve diagnostic accuracy.

Evaluation of left ventricular diastolic function of patients with coronary heart disease by ultrasound images on bilateral filtering image noise reduction algorithm combined with electrocardiogram

Objective:To explore the evaluation of left ventricular diastolic function (LVDF) in patients with coronary heart disease (CHD) using ultrasound images (UI) combined with electrocardiogram (ECG) on bilateral filtering image noise reduction algorithm (BFINRA).Methods:A BFINRA was constructed, and 60 subjects who were investigated were divided into a control group (CG) from June 2019 to November 2019 in Taizhou People’s Hospital, a myocardial infarction group (MIG), and an angina pectoris group (APG). The patient’s LVDF was examined by two-dimensional electrophoresis (2DE) and real-time three-dimensional echocardiography (RT-3DE) combined with ECG. The results showed BFINRA could improve UI quality.Results:Clinical data indicated there were no substantial differences in age, gender, and fasting blood glucose of all subjects. 2DE examination results showed the left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and early diastolic mitral blood flow velocity / early diastolic mitral annulus velocity (E/E’) of MIG were much higher than CG (P<0.05), while the left ventricular ejection fraction (LVEF), E / late diastolic mitral blood flow velocity (E/A) and E’ peak value were sharply decreased (P<0.05);LVESV and E/E’ of APG were increased dramatically (P<0.05), while E peak, E/A and E’ peak were decreased greatly. RT-3DE examination results indicated LVEDV and LVESV of MIG were considerably higher than CG (P<0.05), while LVEF and macrophage resistance factor (MRF) were enormously decreased (P<0.05);LVEDV and LVESV of APG were greatly increased (P<0.05). However, LVEF and MRF were not changed significantly (P>0.05). LVEDV had a remarkable difference (P<0.05), but LVESV and LVEF had no obvious differences (P>0.05). The electrocardiogram results illustrated the increase in QT dispersion (QTd) of MIG and APG was statistically significant (P<0.05) compared with CG, while the negative increase of P-wave terminal force in lead V1 (PTFV1) also had a statistical significance (P<0.05). Correlation analysis revealed that MRF and PTFV1 had positive correlation, while MRF and QTd showed a negative correlation.Conclusion:The combination of UI and ECG could better assess LVDF in CHD patients.

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features.

The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

Structural remodelling of the lumbar multifidus, thoracolumbar fascia and lateral abdominal wall perimuscular connective tissues: A cross-sectional and comparative ultrasound study

IntroductionWith low back pain (LBP), remodelling of the lumbar soft tissues involves both trunk muscles and neighbouring passive connective tissues. The aim of the present study was to compare three quantitative measures of these tissues, using ultrasound imaging (USI), among healthy controls and individuals with LBP. MethodsUSI measures from 30 healthy subjects and 34 patients with non-acute LBP were compared between groups and sexes. The measures employed were (1) lumbar multifidus echogenicity (fatty/fibrosis infiltration) at three vertebral levels; (2) posterior layer thickness of the thoracolumbar fascia, and (3) thickness of the perimuscular tissues surrounding the external oblique, internal oblique and transversus abdominis (TrA). ResultsUSI measures of (1) multifidus echogenicity showed statistically significant changes between vertebral levels and sexes (females > males; p = 0.02); (2) differences in thoracolumbar fascia thickness approached statistical significance between groups (LBP > controls; p = 0.09) and sexes (females < males; p = 0.07); and (3) perimuscular tissue surrounding the TrA was significantly thinner (p ≤ 0.001) in patients with LBP compared to controls. DiscussionThe thinner perimuscular tissues surrounding the TrA in patients with LBP is a new finding, concurring with previous findings with regard to the lower activation of this deep muscle as well as more recent findings on other perimuscular tissue. ConclusionOverall, USI measures were sensitive to different potential changes (pain status, sex, vertebral level), and this is useful in studying the remodelling of various soft tissues of the trunk.

Ultrasound Assessment of Pedal Soft Tissue Thickness in Diabetic Patients With and Without Peripheral Neuropathy

Abstract Background: Foot ulcer is a serious complication of diabetes which can cause patient disability. High plantar pressure is one of the risk factors for ulcer formation. Pedal Soft Tissue (PST) thickness is essential in keeping its cush-ioning effect. Atrophy of PST leads to increase in the plantar pressure, which subsequently leads to ischemia and ulceration. Aim of Study: To compare the PST thickness opposite the Metatarsal Heads (MTHs) in diabetic patients with and without peripheral neuropathy using Ultrasound (US) as an easy, available, and non-invasive tool. Material and Methods: We examined the feet (n=96) of 48 diabetic patients using high-frequency US imaging (10- 13MHz). We divided the patients into 2 groups. The first group included 24 diabetic patients with peripheral neuropathy (mean age 56±6 years), and the second group included 24 diabetic patients without peripheral neuropathy (mean age 57±5 years). We measured and compared the vertical thickness of PST superficial to flexor tendons opposite all MTHs in between the 2 groups using independent-sample-t-test. Results: There was statistically significant difference in mean PST thickness under all left MTHs (first 5 vs. 6.4; second 6.1 vs. 7.8; third 6.2 vs. 7.4; fourth 5.6 vs. 6.9; fifth 4 vs. 5.7mm; p < 0.05), and the first, second, third and fifth right MTHs (first 5.1 vs. 6.3; second 6.1 vs. 7.5; third 5.8 vs. 7.3; fifth 4.4 vs. 5.9mm; p < 0.05) between the first and second groups, respectively. Age, Body Mass Index (BMI) and sex had no significant effect on PST thickness in both groups. Conclusion: Our study confirmed that US is effective, easy, available, harmless, low cost, non-invasive imaging tool that detects more PST atrophy in diabetic patients with pe-ripheral neuropathy than that occurs in diabetic patients without peripheral neuropathy.

Accuracy of Ultrasound-Guided Pudendal Nerve Block in the Ischial Spine and Alcock’s Canal Levels: A Cadaveric Study

Blockade of the pudendal nerve (PN) using ultrasound (US) guidance has been described at the levels of the ischial spine and Alcock's canal. However, no study has been conducted to compare anatomical accuracy between different approaches in targeting the PN. To investigate the accuracy of US-guided injection of the PN at the ischial spine and Alcock's canal levels. This study also compared the accuracy of the infiltrations by three sonographers with different levels of experience. Eight Thiel-embalmed cadavers (16 hemipelvises). Three physiatrists trained in musculoskeletal US imaging with 12 years, five years, and one year of experience performed the injections. Each injected a 0.1-mL bolus of colored dye in both hemipelvises of each cadaver at the ischial spine and Alcock's canal levels under US guidance. Each cadaver received three injections per hemipelvis. The accuracy of the injection was determined following hemipelvis dissection by an anatomist. The injections were accurate 33 times out of the total 42 attempts, resulting in 78% accuracy. Sixteen out of 21 injections at the ischial spine level were on target (76% accuracy), while the approach at Alcock's canal level yielded 17 successful injections (81% accuracy). The difference between the approaches was not statistically significant. There was also no significant difference in accuracy between the operators. US-guided injection of the PN can be performed accurately at both the ischial spine and Alcock's canal levels. The difference between the approaches was not statistically significant.

Iliopsoas muscle thickness and pelvic alignment in pronated and normal foot postures

It is hypothesized that the subtalar hyperpronation may provoke the development of a biomechanical chain of events in lower extremity alignment. Several studies have shown that pelvic alignment may alter in the presence of immediate foot hyperpronation induced by external forces. It is unknown whether these alterations are presented in chronic foot hyperpronation or not. It is also unknown if these potential postural changes could affect iliopsoas muscle size. Therefore, it appears necessary to carry out thorough research in this study. Twenty nine females with pronated foot posture and twenty seven females with normal foot posture participated in this study. The iliopsoas muscle was measured using ultrasound imaging (USI). Pelvic angle (PA) was measured using reflective markers and digital photography. Intrarater reliability of USI for the iliopsoas muscle thickness was also measured. The results indicated that the iliopsoas muscle thickness and the PA were not different in individuals with pronated foot compared to the normal foot group. However, a good intrarater reliability of USI was found for measuring the iliopsoas muscle thickness.

Ultrasound-guided sciatic nerve block at the midthigh level in a porcine model: A descriptive study.

Background and ObjectiveThere are a growing number of porcine models being used for orthopaedic experiments for human beings. Therefore, pain management of those research pigs using ultrasound (US)‐guided nerve block can be usefully performed. The aim of this study is to determine optimal US approaches for accessing and localizing the sciatic nerve at the midthigh level, a relevant block site for hindlimb surgery in female Yorkshire pigs.MethodsAs a first step, we dissected the intubated, blood‐washed out pigs (n = 3) and confirmed the anatomical position of the sciatic nerve at midthigh level. After dissection, we found the sciatic nerve, connected with nerve stimulator, and checked the dorsiflexion or plantar flexion of the hindlimb. We matched the sciatic nerve location with the US image. After the pigs were euthanized, the neural structures of the sciatic nerve were confirmed by histological examination with H&E staining. In second step, a main US‐guided sciatic nerve block study was done in the intubated, live pigs (n = 8) based on the above study.ResultsIn lateral position, the effective US‐guided nerve block site was about 6 cm from the patella crease level; immediately proximal to the bifurcation of the sciatic nerve into the tibial nerve and common peroneal nerve. The distal femur was selected as the landmark. There were no vessels or other nerves surrounding the sciatic nerve. The needle‐tip was positioned less than 1 cm lateral from the distal femur and about 2 cm deep to skin. ‘Donut sign’ in US images was confirmed in all 16 nerves.ConclusionsMidthigh level sciatic nerve is located superficially, which enables nerve block to be easily performed using US. This is the first study to describe midthigh sciatic nerve block in the lateral position under US guidance in a porcine model from a clinical perspective.

Ultrasound imaging features of the Achilles tendon in dancers. Is there a correlation between the imaging and clinical findings? A cross-sectional study

ObjectivesTo analyze the prevalence of tendon pathology and performance associated in pre-professional classic dancers with no history of Achilles tendon (AT) pain. Design: A cross-sectional study was conducted. ParticipantsTwenty-nine classic ballet dancers without AT pain (58 tendons) were recruited. The tendons were classified (normal, abnormal) using ultrasound imaging (USI). Cross-sectional area (CSA) and thickness of flexor hallucis longus (FHL) muscle were measured using USI. Range of movement (ROM) of the first metatarsophalangeal join, balance test, endurance test and vertical jump were measured (bilaterally) as performance variables. ResultsMore than a half (62%) of the participants had at least one abnormal tendon, with 45% of the dancers in the abnormal group having bilateral abnormalities. Dancers with AT pathology had significantly more years of dancing and number of weekly pointe hours than those with no pathology on US imaging. No significant differences between the groups were identified for the rest of the variables. Dancing years were moderately correlated with AT thickness. ConclusionsAsymptomatic classic dancers had a high incidence of tendon pathology. Increased dancing years was associated with pathology in the AT. Nevertheless, these abnormalities may not affect dance performance.

Ultrasound Assessment of the Abdominal Wall Muscles in Women with and without Primary Dysmenorrhea: A Cross-Sectional Study.

Background: Primary dysmenorrhea (PD) is one of the most common gynecological disorders in women of reproductive age that may affect quality of life. It is believed that the underlying cause of PD may be the excessive production of prostaglandins (PGs), however, between 20%–25% of women with PD did not respond to pharmacological treatments, showing that nociceptive mechanisms underlying PD are still not understood. The purpose of this study was to measure and compare, through the use of ultrasound imaging, the thickness at rest of the abdominal wall, as well as the interrecti distance (IRD), in women with and without PD. Methods: A cross-sectional study has been performed using ultrasound imaging (USI) to measure the resting thickness of the external oblique (EO), internal oblique (IO), transversus abdominis (TrAb), rectus abdominis (RA), as well as the IRD in a sample of 39 women, 19 with PD and 20 without PD (median ± IR age: 20 ± 4 and 22.5 ± 7 years, respectively). Results: Findings of muscular thickness did not reveal statically significant differences (p < 0.05) in EO, IO, TrAb, RA, and the IRD between the PD group and control group. Conclusions: These findings suggest that the thickness of the abdominal wall is not associated with PD.

Assessing the Performance of Morphologic and Echogenic Features in Median Nerve Ultrasound for Carpal Tunnel Syndrome Diagnosis.

To assess the feasibility of using ultrasound (US) image features related to the median nerve echogenicity and shape for carpal tunnel syndrome (CTS) diagnosis. In 31 participants (21 healthy participants and 10 patients with CTS), US images were collected with a 30-MHz transducer from median nerves at the wrist crease in 2 configurations: a neutral position and with wrist extension. Various morphologic features, including the cross-sectional area (CSA), were calculated to assess the nerve shape. Carpal tunnel syndrome commonly results in loss of visualization of the nerve fascicular pattern on US images. To assess this phenomenon, we developed a nerve-tissue contrast index (NTI) method. The NTI is a ratio of average brightness levels of surrounding tissue and the median nerve, both calculated on the basis of a US image. The area under the curve (AUC) from a receiver operating characteristic curve analysis and t test were used to assess the usefulness of the features for differentiation of patients with CTS from control participants. We obtained significant differences in the CSA and NTI parameters between the patients with CTS and control participants (P < .01), with the corresponding highest AUC values equal to 0.885 and 0.938, respectively. For the remaining investigated morphologic features, the AUC values were less than 0.685, and the differences in means between the patients and control participants were not statistically significant (P > .10). The wrist configuration had no impact on differences in average parameter values (P > .09). Patients with CTS can be differentiated from healthy individuals on the basis of the median nerve CSA and echogenicity. Carpal tunnel syndrome is not manifested in a change of the median nerve shape that could be related to circularity or contour variability.

Development of a robot-assisted ultrasound-guided radiation therapy (USgRT).

Radiation treatment is improved by the use of image-guided workflows. This work pursues the approach of using ultrasound (US) as a real-time imaging modality. The primary focus of this study is to develop and test a breathing and motion control for a robotic-guided US transducer. All control functions of the robot and the US image processing were then integrated into one software platform enabling US-guided radiation therapy. The robot (KUKA LBR iiwa 7 R800) and the US image processing workflows were integrated into the Medical Interaction Toolkit (MITK) (Nolden et al. in Int J Comput Assist Radiol Surg 8(4):607-620, 2013). The positions of the US probe were tracked with an optical tracking system. As a main function of robot positioning control, a highly sensitive breathing and motion compensation method was developed using KUKA's robotic application programming interface. The resulting autonomous robot motions were tested by the use of defined breathing patterns with two volunteers. Furthermore, a filter pipeline for 3D US image processing with MITK was developed. Thus, image registration of US images and previously acquired planning image data was enabled. The implemented breathing and motion compensation feature was successful with the addition of the remote rotating, translating capability of the US probe. Desired force applied to the US probe, and thus to the patient, is stable and enables a continuous US imaging. The developed filter pipeline for image processing facilitates registration and display of planning data and US image data in one graphical user interface. A stable and robust method for motion compensation for robot-assisted US imaging was developed and tested successfully. This is a first step toward the safe use of autonomous robot motions in interaction with patients. Furthermore, main software components were integrated into a single platform and used with the purpose of ultrasound-guided radiation therapy.

Ultrasound and magnetic resonance imaging for group stratification and treatment monitoring in the transgenic adenocarcinoma of the mouse prostate model.

The transgenic adenocarcinoma of the mouse prostate (TRAMP) is a widely used genetically engineered spontaneous prostate cancer model. However, both the degree of malignancy and time of cancer onset vary. While most mice display slowly progressing cancer, a subgroup develops fast-growing poorly differentiated (PD) tumors, making the model challenging to use. We investigated the feasibility of using ultrasound (US) imaging to screen for PD tumors and compared the performances of US and magnetic resonance imaging (MRI) in providing reliable measurements of disease burden. TRAMP mice (n = 74) were screened for PD tumors with US imaging and findings verified with MRI, or in two cases with gross pathology. PD tumor volume was estimated with US and MR imaging and the methods compared (n = 11). For non-PD mice, prostate volume was used as a marker for disease burden and estimated with US imaging, MRI, and histology (n = 11). The agreement between the measurements obtained by the various methods and the intraobserver variability (IOV) was assessed using Bland-Altman analysis. US screening showed 81% sensitivity, 91% specificity, 72% positive predictive value, and 91% negative predictive value. The smallest tumor detected by US screening was 14 mm3 and had a maximum diameter of 2.6 mm. MRI had the lowest IOV for both PD tumor and prostate volume estimation. US IOV was almost as low as MRI for PD tumor volumes but was considerably higher for prostate volumes. US imaging was found to be a good screening method for detecting PD tumors and estimating tumor volume in the TRAMP model. MRI had better repeatability than US, especially when estimating prostate volumes.