Does the Head Position Affect Neonatal Lateral Ventricular Volume?

OBJECTIVE: This study aimed to determine whether there are differences in the lateral ventricular volumes, measured by three-dimensional ultrasound (3D US) depending on the posture of the neonate (right and left lateral decubitus). STUDY DESIGN: This was a prospective analysis of the lateral ventricular volumes of preterm neonates recruited from Victoria Hospital, London, Ontario (June 2018-November 2019). A total of 24 premature neonates were recruited. The first cohort of 18 unstable premature neonates were imaged with 3D US in their current sides providing 15 right-sided and 16 left-sided 3D US images. The neonates in the second cohort of six relatively stable infants were imaged after positioning in each lateral decubitus position for 30 minutes, resulting in 40 3D US images obtained from 20 posture change sessions. The images were segmented and the ventricle volumes in each lateral posture were compared with determine whether the posture of the head influenced the volume of the upper and lower ventricle. RESULTS: For the first cohort who did not have their posture changed, the mean of the right and left ventricle volumes were 23.81 ± 15.51 and 21.61 ± 16.19 cm3, respectively, for the 15 images obtained in a right lateral posture and 13.96 ± 8.69 and 14.92 ± 8.77 cm3, respectively, for the 16 images obtained in the left lateral posture. Similarly, for the second cohort who had their posture changed, the mean of right and left ventricle volumes were 20.92 ± 17.3 and 32.74 ± 32.33 cm3, respectively, after 30 minutes in the right lateral posture, and 21.25 ± 18.4 and 32.65 ± 31.58 cm3, respectively, after 30 minutes in the left lateral posture. Our results failed to show a statistically significant difference in ventricular volumes dependence on posture. CONCLUSION: Head positioned to any lateral side for 30 minutes does not have any effect on the lateral ventricular volumes of neonates. KEY POINTS: · Three-dimensional cranial ultrasound can measure neonatal ventricle volume.. · Ventricle volume in each lateral ventricle may be affected by posture of the neonate.. · The 30 minutes in any lateral posture is not sufficient to create volume difference in lateral ventricles..

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation.

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3He MRI (r = 0.90, P < .001). Both model-predicted and HP 3He MRI VDP were correlated with forced expiratory volume in 1 second (FEV1) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV1 to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.

A framework for Fourier-decomposition free-breathing pulmonary 1 H MRI ventilation measurements.

PURPOSE: To develop a rapid Fourier decomposition (FD) free-breathing pulmonary 1 H MRI (FDMRI) image processing and biomarker pipeline for research use. METHODS: We acquired MRI in 20 asthmatic subjects using a balanced steady-state free precession (bSSFP) sequence optimized for ventilation imaging. 2D 1 H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right-to-left lung volume-ratio. The segmented lung series were co-registered using a coarse-to-fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation-weighted maps and ventilation-defect-percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung-masks, deformed manual lung-masks, and fiducials in the moving and fixed images using Dice-similarity-coefficient (DSC), mean-absolute-distance (MAD), and target-registration-error (TRE). The relationship of FD-VDP and 3 He-VDP was evaluated using the Pearson-correlation-coefficient (r) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient-of-variation (CoV) and intra-class-correlation-coefficient (ICC) for segmentation, registration, and FD-VDP components. RESULTS: For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD-VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD-VDP was correlated with 3 He-VDP (r = 0.69, P < 0.001) although there was a bias toward lower FD-VDP (bias = -4.9%). CONCLUSIONS: We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.

Intravenous delivery of a multi-mechanistic cancer-targeted oncolytic poxvirus in humans.

The efficacy and safety of biological molecules in cancer therapy, such as peptides and small interfering RNAs (siRNAs), could be markedly increased if high concentrations could be achieved and amplified selectively in tumour tissues versus normal tissues after intravenous administration. This has not been achievable so far in humans. We hypothesized that a poxvirus, which evolved for blood-borne systemic spread in mammals, could be engineered for cancer-selective replication and used as a vehicle for the intravenous delivery and expression of transgenes in tumours. JX-594 is an oncolytic poxvirus engineered for replication, transgene expression and amplification in cancer cells harbouring activation of the epidermal growth factor receptor (EGFR)/Ras pathway, followed by cell lysis and anticancer immunity. Here we show in a clinical trial that JX-594 selectively infects, replicates and expresses transgene products in cancer tissue after intravenous infusion, in a dose-related fashion. Normal tissues were not affected clinically. This platform technology opens up the possibility of multifunctional products that selectively express high concentrations of several complementary therapeutic and imaging molecules in metastatic solid tumours in humans.

Accuracy Validation of an Automated Method for Prostate Segmentation in Magnetic Resonance Imaging.

Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm3 in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task.

3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets.

Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10-30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patient's fontanels have closed around 12-18months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice.

Mechatronic system for in-bore MRI-guided insertion of needles to the prostate: An in vivo needle guidance accuracy study.

BACKGROUND: To present our experiences in initial clinical evaluation of a novel mechatronic system for in-bore guidance of needles to the prostate for MRI-guided prostate interventions in 10 patients. We report accuracy of this device in the context of focal laser ablation therapy for localized prostate cancer. METHODS: An MRI-compatible needle guidance device was developed for transperineal prostate interventions. Ten patients underwent MRI-guided focal laser ablation therapy with device-mediated laser fiber delivery. We recorded needle guidance error and needle delivery time. RESULTS: A total of 37 needle insertions were evaluated. Median needle guidance error was 3.5 mm (interquartile range, 2.1-5.4 mm), and median needle delivery time was 9 min (interquartile range, 6.5-12 min). CONCLUSION: This system provides a reliable method of accurately aligning needle guides for in-bore transperineal needle delivery to the prostate.

Evaluation of MRI-TRUS fusion versus cognitive registration accuracy for MRI-targeted, TRUS-guided prostate biopsy.

OBJECTIVE: The purpose of this article is to compare transrectal ultrasound (TRUS) biopsy accuracies of operators with different levels of prostate MRI experience using cognitive registration versus MRI-TRUS fusion to assess the preferred method of TRUS prostate biopsy for MRI-identified lesions. SUBJECTS AND METHODS; One hundred patients from a prospective prostate MRI-TRUS fusion biopsy study were reviewed to identify all patients with clinically significant prostate adenocarcinoma (PCA) detected on MRI-targeted biopsy. Twenty-five PCA tumors were incorporated into a validated TRUS prostate biopsy simulator. Three prostate biopsy experts, each with different levels of experience in prostate MRI and MRI-TRUS fusion biopsy, performed a total of 225 simulated targeted biopsies on the MRI lesions as well as regional biopsy targets. Simulated biopsies performed using cognitive registration with 2D TRUS and 3D TRUS were compared with biopsies performed under MRI-TRUS fusion. RESULTS: Two-dimensional and 3D TRUS sampled only 48% and 45% of clinically significant PCA MRI lesions, respectively, compared with 100% with MRI-TRUS fusion. Lesion sampling accuracy did not statistically significantly vary according to operator experience or tumor volume. MRI-TRUS fusion-naïve operators showed consistent errors in targeting of the apex, midgland, and anterior targets, suggesting that there is biased error in cognitive registration. The MRI-TRUS fusion expert correctly targeted the prostate apex; however, his midgland and anterior mistargeting was similar to that of the less-experienced operators. CONCLUSION: MRI-targeted TRUS-guided prostate biopsy using cognitive registration appears to be inferior to MRI-TRUS fusion, with fewer than 50% of clinically significant PCA lesions successfully sampled. No statistically significant difference in biopsy accuracy was seen according to operator experience with prostate MRI or MRI-TRUS fusion.

Pulmonary abnormalities and carotid atherosclerosis in ex-smokers without airflow limitation.

It is well-established that COPD patients have a burden of vascular disease that cannot be fully-explained by smoking history but the mechanistic links between atherosclerosis and pulmonary disease in COPD patients are not well-understood. Moreover, in ex-smokers without symptoms or other evidence of COPD, subclinical pulmonary and vascular disease, although potentially present, has not been described or evaluated. Hence our aim was to use sensitive three-dimensional (3D) pulmonary and carotid imaging to quantify pulmonary airway/parenchyma abnormalities and atherosclerosis in ex-smokers without airflow limitation or symptoms consistent with COPD. We evaluated 61 subjects without airflow limitation including 34 never- (72 ± 6 years) and 27 ex-smokers (73 ± 9 years), who provided written informed consent to spirometry, plethysmography, (3)He magnetic resonance imaging (MRI) and carotid ultrasound (US) and, for ex-smokers alone, thoracic X-ray computed tomography (CT). Ex-smokers had significantly greater (3)He ventilation defect percent (VDP = 7%, p = 0.001) and carotid total plaque volume (TPV = 250 mm(3), p = 0.002) than never-smokers, although there were no significant differences for spirometry or plethysmography, and CT airway and emphysema measurements were normal. There were univariate relationships for (3)He VDP with carotid intima media thickness (IMT, r = 0.42, p = 0.004), TPV (r = 0.41, p = 0.006) and vessel wall volume (VWV, r = 0.40, p = 0.007). Multivariate models that included age, BMI, FEV1, DLCO and VDP showed that only VDP significantly predicted IMT (ß = 0.41, p = 0.001), VWV (ß = 0.45, p = 0.003) and TPV (ß = 0.38, p = 0.005). In summary, there was imaging evidence of mild airways disease and carotid plaque burden that were related and significantly greater in ex-smokers without airflow limitation than in never-smokers.

Volume of carotid artery ulceration as a predictor of cardiovascular events.

BACKGROUND AND PURPOSE: Previous studies have shown the presence of ulceration in atherosclerotic plaque either by categorizing the plaque as complex (irregular morphology with ulcers) or smooth or by quantifying the number of ulcers observed in a specific region of interest. The aim of this study was to quantify carotid total ulcer volume by 3-dimensional ultrasound to investigate the relationship of total ulcer volume to vascular events (strokes, transient ischemic attack, myocardial infarction, revascularization, or death because of cardiovascular reasons). METHODS: In total, 349 at-risk subjects provided written informed consent to carotid 3-dimensional ultrasound and were analyzed for ulcerations. Ulcer volume was defined as a distinct discontinuity in an atherosclerotic plaque, with a volume≥1.00 mm3 as measured using manual segmentation. The sum of the volumes of all ulcers seen in both carotids was the total ulcer volume. Participants were monitored for ≤5 years for outcomes, including cardiovascular events and death. RESULTS: Kaplan-Meier survival analysis showed that subjects with total ulcer volume≥5 mm3 experienced a significantly higher risk of developing stroke, transient ischemic attack, or death (P=0.009) and of developing stroke/transient ischemic attack/death/myocardial infarction/revascularization (P=0.017). Lower ulcer volumes did not predict events nor did ulcer depth. CONCLUSIONS: Volume of carotid ulceration on 3-dimensional ultrasound predicts cardiovascular events. In addition to improving risk stratification, ulceration is a potential therapeutic target.

Three-dimensional carotid ultrasound plaque texture predicts vascular events.

BACKGROUND AND PURPOSE: Carotid ultrasound atherosclerosis measurements, including those of the arterial wall and plaque, provide a way to monitor patients at risk of vascular events. Our objective was to examine carotid ultrasound plaque texture measurements and the change in carotid plaque texture during 1 year in patients at risk of events and to compare these with measurements of plaque volume and other risk factors as predictors of vascular events. METHODS: We evaluated 298 patients with carotid atherosclerosis using 3-dimensional (3D) ultrasound at baseline and after 1 year and measured carotid plaque volume and 376 measures of plaque texture. Patients were followed up to 5 years (median [range], 3.12 [0.77-4.66]) for myocardial infarction, transient ischemic attack, and stroke. Sparse Cox regression was used to select the most predictive plaque texture measurements in independent training sets using a 10-fold cross-validation, repeated 5×, to ensure unbiased results. RESULTS: Receiver operator curves and Kaplan-Meier analysis showed that changes in texture and total plaque volume combined provided the best predictor of vascular events. In multivariate Cox regression, changes in plaque texture (median hazard ratio, 1.4; P<0.001) and total plaque volume (median hazard ratio, 1.5 per 100 mm(3); P<0.001) were both significant predictors, whereas the Framingham risk score was not. CONCLUSIONS: Changes in both plaque texture and volume are strongly predictive of vascular events. In high-risk patients, 3D ultrasound plaque measurements should be considered for vascular event risk prediction.

Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound.

BACKGROUND: Three-dimensional (3D) ultrasound (US) imaging has shown promise in non-invasive monitoring of changes in the lateral brain ventricles of neonates suffering from intraventricular hemorrhaging. Due to the poorly defined anatomical boundaries and low signal-to-noise ratio, fully supervised methods for segmentation of the lateral ventricles in 3D US images require a large dataset of annotated images by trained physicians, which is tedious, time-consuming, and expensive. Training fully supervised segmentation methods on a small dataset may lead to overfitting and hence reduce its generalizability. Semi-supervised learning (SSL) methods for 3D US segmentation may be able to address these challenges but most existing SSL methods have been developed for magnetic resonance or computed tomography (CT) images. PURPOSE: To develop a fast, lightweight, and accurate SSL method, specifically for 3D US images, that will use unlabeled data towards improving segmentation performance. METHODS: We propose an SSL framework that leverages the shape-encoding ability of an autoencoder network to enforce complex shape and size constraints on a 3D U-Net segmentation model. The autoencoder created pseudo-labels, based on the 3D U-Net predicted segmentations, that enforces shape constraints. An adversarial discriminator network then determined whether images came from the labeled or unlabeled data distributions. We used 887 3D US images, of which 87 had manually annotated labels and 800 images were unlabeled. Training/validation/testing sets of 25/12/50, 25/12/25 and 50/12/25 images were used for model experimentation. The Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and absolute volumetric difference (VD) were used as metrics for comparing to other benchmarks. The baseline benchmark was the fully supervised vanilla 3D U-Net while dual task consistency, shape-aware semi-supervised network, correlation-aware mutual learning, and 3D U-Net Ensemble models were used as state-of-the-art benchmarks with DSC, MAD, and VD as comparison metrics. The Wilcoxon signed-rank test was used to test statistical significance between algorithms for DSC and VD with the threshold being p < 0.05 and corrected to p < 0.01 using the Bonferroni correction. The random-access memory (RAM) trace and number of trainable parameters were used to compare the computing efficiency between models. RESULTS: Relative to the baseline 3D U-Net model, our shape-encoding SSL method reported a mean DSC improvement of 6.5%, 7.7%, and 4.1% with a 95% confidence interval of 4.2%, 5.7%, and 2.1% using image data splits of 25/12/50, 25/12/25, and 50/12/25, respectively. Our method only used a 1GB increase in RAM compared to the baseline 3D U-Net and required less than half the RAM and trainable parameters compared to the 3D U-Net ensemble method. CONCLUSIONS: Based on our extensive literature survey, this is one of the first reported works to propose an SSL method designed for segmenting organs in 3D US images and specifically one that incorporates unlabeled data for segmenting neonatal cerebral lateral ventricles. When compared to the state-of-the-art SSL and fully supervised learning methods, our method yielded the highest DSC and lowest VD while being computationally efficient.

Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.

Purpose: Our objective was to train machine-learning algorithms on hyperpolarized He 3 magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s ( FEV 1 ) across 3 years. Approach: Hyperpolarized He 3 MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis. Results: We evaluated 88 ex-smoker participants with 31 ± 7 months follow-up data, 57 of whom (22 females/35 males, 70 ± 9 years) had negligible changes in FEV 1 and 31 participants (7 females/24 males, 68 ± 9 years) with worsening FEV 1 ≥ 60 mL / year . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict FEV 1 decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone. Conclusion: For the first time, we have employed hyperpolarized He 3 MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in FEV 1 with 82% accuracy.

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation.

BACKGROUND AND OBJECTIVES: Total Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. METHODS: In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. RESULTS: Our network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). CONCLUSIONS: The results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.

Three-dimensional ultrasound to investigate synovitis in first carpometacarpal osteoarthritis: A feasibility study.

BACKGROUND: Synovitis is one of the defining characteristics of osteoarthritis (OA) in the carpometacarpal (CMC1) joint of the thumb. Quantitative characterization of synovial volume is important for furthering our understanding of CMC1 OA disease progression, treatment response, and monitoring strategies. In previous studies, three-dimensional ultrasound (3-D US) has demonstrated the feasibility of being a point-of-care system for monitoring knee OA. However, 3-D US has not been tested on the smaller joints of the hand, which presents unique physiological and imaging challenges. PURPOSE: To develop and validate a novel application of 3-D US to monitor soft-tissue characteristics of OA in a CMC1 OA patient population compared to the current gold standard, magnetic resonance imaging (MRI). METHODS: A motorized submerged transducer moving assembly was designed for this device specifically for imaging the joints of the hands and wrist. The device used a linear 3-D scanning approach, where a 14L5 2-D transducer was translated over the region of interest. Two imaging phantoms were used to test the linear and volumetric measurement accuracy of the 3-D US device. To evaluate the accuracy of the reconstructed 3-D US geometry, a multilayer monofilament string-grid phantom (10 mm square grid) was scanned. To validate the volumetric measurement capabilities of the system, a simulated synovial tissue phantom with an embedded synovial effusion was fabricated and imaged. Ten CMC1 OA patients were imaged by our 3-D US and a 3.0 T MRI system to compare synovial volumes. The synovial volumes were manually segmented by two raters on the 2D slices of the 3D US reconstruction and MR images, to assess the accuracy and precision of the device for determining synovial tissue volumes. The Standard Error of Measurement and Minimal Detectable Change was used to assess the precision and sensitivity of the volume measurements. Paired sample t-tests were used to assess statistical significance. Additionally, rater reliability was assessed using Intra-Class Correlation (ICC) coefficients. RESULTS: The largest percent difference observed between the known physical volume of synovial extrusion in the phantom and the volume measured by our 3D US was 1.1% (p-value = 0.03). The mean volume difference between the 3-D US and the gold standard MRI was 1.78% (p-value = 0.48). The 3-D US synovial tissue volume measurements had a Standard Error Measurement (SEm ) of 11.21 mm3 and a Minimal Detectible Change (MDC) of 31.06 mm3 , while the MRI synovial tissue volume measurements had an SEM of 16.82 mm3 and an MDC of 46.63 mm3 . Excellent inter- and intra-rater reliability (ICCs = 0.94-0.99) observed across all imaging modalities and raters. CONCLUSION: Our results indicate the feasibility of applying 3-D US technology to provide accurate and precise CMC1 synovial tissue volume measurements, similar to MRI volume measurements. Lower MDC and SEm values for 3-D US volume measurements indicate that it is a precise measurement tool to assess synovial volume and that it is sensitive to variation between volume segmentations. The application of this imaging technique to monitor OA pathogenesis and treatment response over time at the patient's bedside should be thoroughly investigated in future studies.

JoCoRank: Joint correlation learning with ranking similarity regularization for imbalanced fetal brain age regression.

Estimating fetal brain age based on sulci by magnetic resonance imaging (MRI) is clinically crucial in determining the normal development of fetal brains. Deep learning provides a possible way for fetal brain age estimation using MRI. Previous studies have mainly emphasized optimizing individual-wise correlation criteria, such as mean square error. However, they ignored the very important global and peer-wise criterion, which are essential for learning the structured relationships among regression samples. Moreover, the imbalanced label distribution introduces an adverse bias, which impairs the reliability and interpretation of correlation estimation and the model's fairness and generalizability. In this work, we propose a novel joint correlation learning with ranking similarity regularization (JoCoRank) algorithm for deep imbalanced regression of fetal brain age. Joint correlation learning concurrently captures individual, global, and peer-level valuable relationship information, and the customized optimization scheme for each criterion exhibits strong robustness against outliers and imbalanced regression. Ranking similarity regularization is designed to calibrate the biased feature representations by aligning the sorted list of neighbors in the label space with those in the feature space. A total of 1327 MRI images from 157 healthy fetuses between 22 and 34 weeks were collected at Wuhan Children's Hospital and utilized to evaluate the performance of JoCoRank in fetal brain age estimation. JoCoRank achieved promising results with an average mean absolute error of 0.693±0.064 weeks and R2 coefficient of 0.930±0.019. Our fetal brain age estimation algorithm would be useful for identifying abnormalities in fetal brain development and undertaking early intervention in clinical practice.

Three-dimensional trans-rectal and trans-abdominal ultrasound image fusion for the guidance of gynecologic brachytherapy procedures: a proof of concept study.

High dose-rate brachytherapy is a treatment technique for gynecologic cancers where intracavitary applicators are placed within the patient's pelvic cavity. To ensure accurate radiation delivery, localization of the applicator at the time of insertion is vital. This study proposes a novel method for acquiring, registering, and fusing three-dimensional (3D) trans-abdominal and 3D trans-rectal ultrasound (US) images for visualization of the pelvic anatomy and applicators during gynecologic brachytherapy. The workflow was validated using custom multi-modal pelvic phantoms and demonstrated during two patient procedures. Experiments were performed for three types of intracavitary applicators: ring-and-tandem, ring-and-tandem with interstitial needles, and tandem-and-ovoids. Fused 3D US images were registered to magnetic resonance (MR) and computed tomography (CT) images for validation. The target registration error (TRE) and fiducial localization error (FLE) were calculated to quantify the accuracy of our fusion technique. For both phantom and patient images, TRE and FLE across all modality registrations (3D US versus MR or CT) resulted in mean ± standard deviation of 4.01 ± 1.01 mm and 0.43 ± 0.24 mm, respectively. This work indicates proof of concept for conducting further clinical studies leveraging 3D US imaging as an accurate, accessible alternative to advanced modalities for localizing brachytherapy applicators.

Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles.

BACKGROUND: Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is required on transrectal ultrasound (TRUS) images during prostate brachytherapy procedures. Implanted needles cause artifacts that may make this task difficult and time-consuming. Thus, previous studies have focused on the simpler problem of segmentation in the absence of needles at the cost of reduced clinical utility. PURPOSE: To use a convolutional neural network (CNN) algorithm for segmentation of the prostatic CTV in TRUS images post-needle insertion obtained from prostate brachytherapy procedures to better meet the demands of the clinical procedure. METHODS: A dataset consisting of 144 3-dimensional (3D) TRUS images with implanted metal brachytherapy needles and associated manual CTV segmentations was used for training a 2-dimensional (2D) U-Net CNN using a Dice Similarity Coefficient (DSC) loss function. These were split by patient, with 119 used for training and 25 reserved for testing. The 3D TRUS training images were resliced at radial (around the axis normal to the coronal plane) and oblique angles through the center of the 3D image, as well as axial, coronal, and sagittal planes to obtain 3689 2D TRUS images and masks for training. The network generated boundary predictions on 300 2D TRUS images obtained from reslicing each of the 25 3D TRUS images used for testing into 12 radial slices (15° apart), which were then reconstructed into 3D surfaces. Performance metrics included DSC, recall, precision, unsigned and signed volume percentage differences (VPD/sVPD), mean surface distance (MSD), and Hausdorff distance (HD). In addition, we studied whether providing algorithm-predicted boundaries to the physicians and allowing modifications increased the agreement between physicians. This was performed by providing a subset of 3D TRUS images of five patients to five physicians who segmented the CTV using clinical software and repeated this at least 1 week apart. The five physicians were given the algorithm boundary predictions and allowed to modify them, and the resulting inter- and intra-physician variability was evaluated. RESULTS: Median DSC, recall, precision, VPD, sVPD, MSD, and HD of the 3D-reconstructed algorithm segmentations were 87.2 [84.1, 88.8]%, 89.0 [86.3, 92.4]%, 86.6 [78.5, 90.8]%, 10.3 [4.5, 18.4]%, 2.0 [-4.5, 18.4]%, 1.6 [1.2, 2.0] mm, and 6.0 [5.3, 8.0] mm, respectively. Segmentation time for a set of 12 2D radial images was 2.46 [2.44, 2.48] s. With and without U-Net starting points, the intra-physician median DSCs were 97.0 [96.3, 97.8]%, and 94.4 [92.5, 95.4]% (p < 0.0001), respectively, while the inter-physician median DSCs were 94.8 [93.3, 96.8]% and 90.2 [88.7, 92.1]%, respectively (p < 0.0001). The median segmentation time for physicians, with and without U-Net-generated CTV boundaries, were 257.5 [211.8, 300.0] s and 288.0 [232.0, 333.5] s, respectively (p = 0.1034). CONCLUSIONS: Our algorithm performed at a level similar to physicians in a fraction of the time. The use of algorithm-generated boundaries as a starting point and allowing modifications reduced physician variability, although it did not significantly reduce the time compared to manual segmentations.

Progression of carotid plaque volume predicts cardiovascular events.

BACKGROUND AND PURPOSE: Carotid ultrasound evaluation of intima-media thickness (IMT) and plaque burden has been used for risk stratification and for evaluation of antiatherosclerotic therapies. Increasing evidence indicates that measuring plaque burden is superior to measuring IMT for both purposes. We compared progression/regression of IMT, total plaque area (TPA), and total plaque volume (TPV) as predictors of cardiovascular outcomes. METHODS: IMT, TPA, and TPV were measured at baseline in 349 patients attending vascular prevention clinics; they had TPA of 40 to 600 mm(2) at baseline to qualify for enrollment. Participants were followed up for ≤5 years (median, 3.17 years) to ascertain vascular death, myocardial infarction, stroke, and transient ischemic attacks. Follow-up measurements 1 year later were available in 323 cases for IMT and TPA, and in 306 for TPV. RESULTS: Progression of TPV predicted stroke, death or TIA (Kaplan-Meier logrank P=0.001), stroke/death/MI (P=0.008) and Stroke/Death/TIA/Myocardial infarction (any Cardiovascular event) (P=0.001). Progression of TPA weakly predicted Stroke/Death/TIA (P=0.097) but not stroke/death/MI (P=0.59) or any CV event (P=0.143); likewise change in IMT did not predict Stroke/Death/MI (P=0.13) or any CV event (P=0.455 ). In Cox regression, TPV progression remained a significant predictor of events after adjustment for coronary risk factors (P=0.001) but change in TPA did not. IMT change predicted events in an inverse manner; regression of IMT predicted events (P=0.004). CONCLUSIONS: For assessment of response to antiatherosclerotic therapy, measurement of TPV is superior to both IMT and TPA.

Toward mechatronic MRI-guided focal laser ablation of the prostate: Robust registration for improved needle delivery.

BACKGROUND: Multiparametric MRI (mpMRI) is an effective tool for detecting and staging prostate cancer (PCa), guiding interventional therapy, and monitoring PCa treatment outcomes. MRI-guided focal laser ablation (FLA) therapy is an alternative, minimally invasive treatment method to conventional therapies, which has been demonstrated to control low-grade, localized PCa while preserving patient quality of life. The therapeutic success of FLA depends on the accurate placement of needles for adequate delivery of ablative energy to the target lesion. We previously developed an MR-compatible mechatronic system for prostate FLA needle guidance and validated its performance in open-air and clinical 3T in-bore experiments using virtual targets. PURPOSE: To develop a robust MRI-to-mechatronic system registration method and evaluate its in-bore MR-guided needle delivery accuracy in tissue-mimicking prostate phantoms. METHODS: The improved registration multifiducial assembly houses thirty-six aqueous gadolinium-filled spheres distributed over a 7.3 × 7.3 × 5.2 cm volume. MRI-guided needle guidance accuracy was quantified in agar-based tissue-mimicking prostate phantoms on trajectories (N = 44) to virtual targets covering the mechatronic system's range of motion. 3T gradient-echo recalled (GRE) MRI images were acquired after needle insertions to each target, and the air-filled needle tracks were segmented. Needle guidance error was measured as the shortest Euclidean distance between the target point and the segmented needle trajectory, and angular error was measured as the angle between the targeted trajectory and the segmented needle trajectory. These measurements were made using both the previously designed four-sphere registration fiducial assembly on trajectories (N = 7) and compared with the improved multifiducial assembly using a Mann-Whitney U test. RESULTS: The median needle guidance error of the system using the improved registration fiducial assembly at a depth of 10 cm was 1.02 mm with an interquartile range (IQR) of 0.42-2.94 mm. The upper limit of the one-sided 95% prediction interval of needle guidance error was 4.13 mm. The median (IQR) angular error was 0.0097 rad (0.0057-0.015 rad) with a one-sided 95% prediction interval upper limit of 0.022 rad. The median (IQR) positioning error using the previous four-sphere registration fiducial assembly was 1.87 mm (1.77-2.14 mm). This was found to be significantly different (p = 0.0012) from the median (IQR) positioning error of 0.28 mm (0.14-0.95 mm) using the new registration fiducial assembly on the same trajectories. No significant difference was detected between the medians of the angular errors (p = 0.26). CONCLUSION: This is the first study presenting an improved registration method and validation in tissue-mimicking phantoms of our remotely actuated MR-compatible mechatronic system for delivery of prostate FLA needles. Accounting for the effects of needle deflection, the system was demonstrated to be capable of needle delivery with an error of 4.13 mm or less in 95% of cases under ideal conditions, which is a statistically significant improvement over the previous method. The system will next be validated in a clinical setting.