Towards U-Net-based intraoperative 2D dose prediction in high dose rate prostate brachytherapy.

Background: Poor needle placement in prostate high-dose-rate brachytherapy (HDR-BT) results in sub-optimal dosimetry and mentally predicting these effects during HDR-BT is difficult, creating a barrier to widespread availability of high-quality prostate HDR-BT.

Purpose: To provide earlier feedback on needle implantation quality, we trained machine learning models to predict 2D dosimetry for prostate HDR-BT on axial TRUS images.

Methods And Materials: Clinical treatment plans from 248 prostate HDR-BT patients were retrospectively collected and randomly split 80/20 for training/testing.

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.

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 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 ( ) across 3 years.

Approach: Hyperpolarized MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm.

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.

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.

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 enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation.

Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images.

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.

Clinical implementation of 3D transvaginal ultrasound for intraoperative guidance of needle implant in template interstitial gynecologic high-dose-rate brachytherapy.

Purpose: To demonstrate novel clinical implementation of a 3D transvaginal ultrasound (3DTVUS) system for intraoperative needle insertion guidance in perineal template interstitial gynecologic high-dose-rate brachytherapy and assess its impact on implant quality.

Methods And Materials: Interstitial implants began with preimplant 3DTVUS to visualize the tumor and anatomy, with intermittent 3DTVUS to assess the implant and guide needle adjustment. Analysis includes visualization of the implant relative to anatomy, identification of cases where 3DTVUS is beneficial, dosimetry, and a survey distributed to 3DTVUS clinicians.

Cost-effective, portable, patient-dedicated three-dimensional automated breast ultrasound for point-of-care breast cancer screening.

Breast cancer screening has substantially reduced mortality across screening populations. However, a clinical need persists for more accessible, cost-effective, and robust approaches for increased-risk and diverse patient populations, especially those with dense breasts where screening mammography is suboptimal. We developed and validated a cost-effective, portable, patient-dedicated three-dimensional (3D) automated breast ultrasound (ABUS) system for point-of-care breast cancer screening.

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.

3D US-CT/MRI registration for percutaneous focal liver tumor ablations.

Purpose: US-guided percutaneous focal liver tumor ablations have been considered promising curative treatment techniques. To address cases with invisible or poorly visible tumors, registration of 3D US with CT or MRI is a critical step. By taking advantage of deep learning techniques to efficiently detect representative features in both modalities, we aim to develop a 3D US-CT/MRI registration approach for liver tumor ablations.

Cross-sectional reliability and concurrent validity of a quantitative 2-dimensional ultrasound image analysis of effusion and synovial hypertrophy in knee osteoarthritis.

Objective: Effusion-synovitis is related to pain and progression in knee osteoarthritis (OA), but current gold standard ultrasound (US) measures are limited to semi-quantitative grading of joint distension or 1-dimensional thickness measures. A novel quantitative 2-dimensional image analysis methodology is applied to US images of effusion-synovitis; reliability and concurrent validity was assessed in patients with knee OA.

Methods: Cross sectional analysis of US images collected from 51 patients with symptomatic knee OA were processed in ImageJ and segmented in 3DSlicer to produce a binary mask of the supra-patellar synovitis region of interest (ROI).

Ultrasound carotid plaque segmentation via image reconstruction-based self-supervised learning with limited training labels.

Carotid total plaque area (TPA) is an important contributing measurement to the evaluation of stroke risk. Deep learning provides an efficient method for ultrasound carotid plaque segmentation and TPA quantification. However, high performance of deep learning requires datasets with many labeled images for training, which is very labor-intensive.

A power Doppler ultrasound method for improving intraoperative tip localization for visually obstructed needles in interstitial prostate brachytherapy.

Purpose: High-dose-rate (HDR) interstitial brachytherapy (BT) is a common treatment technique for localized intermediate to high-risk prostate cancer. Transrectal ultrasound (US) imaging is typically used for guiding needle insertion, including localization of the needle tip which is critical for treatment planning. However, image artifacts can limit needle tip visibility in standard brightness (B)-mode US, potentially leading to dose delivery that deviates from the planned dose.

Can ventricular 3D ultrasound of neonates with posthemorrhagic hydrocephalus inform on the need for a ventriculoperitoneal shunt?

Objective: Some neonates born prematurely with intraventricular hemorrhage develop posthemorrhagic hydrocephalus and require lifelong treatment to divert the flow of CSF. Early prediction of the eventual need for a ventriculoperitoneal shunt (VPS) is difficult, and early discussions with families are based on statistics and the grade of hemorrhage. The authors hypothesize that change in ventricular volume during ventricular taps that is measured with repeated 3D ultrasound (3D US) imaging of the lateral ventricles could be used to assess the risk of the future requirement of a VPS.

Carotid Vessel-Wall-Volume Ultrasound Measurement via a UNet++ Ensemble Algorithm Trained on Small Data Sets.

Vessel wall volume (VWV) is a 3-D ultrasound measurement for the assessment of therapy in patients with carotid atherosclerosis. Deep learning can be used to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) and to quantify VWV automatically; however, it typically requires large training data sets with expert manual segmentation, which are difficult to obtain. In this study, a UNet++ ensemble approach was developed for automated VWV measurement, trained on five small data sets (n = 30 participants) and tested on 100 participants with clinically diagnosed coronary artery disease enrolled in a multicenter CAIN trial.

Validation of a surface-based deformable MRI-3D ultrasound image registration algorithm toward clinical implementation for interstitial prostate brachytherapy.

Purpose: The purpose of this study was to evaluate and clinically implement a deformable surface-based magnetic resonance imaging (MRI) to three-dimensional ultrasound (US) image registration algorithm for prostate brachytherapy (BT) with the aim to reduce operator dependence and facilitate dose escalation to an MRI-defined target.

Methods And Materials: Our surface-based deformable image registration (DIR) algorithm first translates and scales to align the US- and MR-defined prostate surfaces, followed by deformation of the MR-defined prostate surface to match the US-defined prostate surface. The algorithm performance was assessed in a phantom using three deformation levels, followed by validation in three retrospective high-dose-rate BT clinical cases.

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.

Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee.

Objective: This study aimed to develop a deep learning-based approach to automatically segment the femoral articular cartilage (FAC) in 3D ultrasound (US) images of the knee to increase time efficiency and decrease rater variability.

Design: Our method involved deep learning predictions on 2DUS slices sampled in the transverse plane to view the cartilage of the femoral trochlea, followed by reconstruction into a 3D surface. A 2D U-Net was modified and trained using a dataset of 200 2DUS images resliced from 20 3DUS images.

Proof-of-Concept Study of a 3-D Ultrasound Scanner Used for Ankle Joint Assessment.

Joint arthropathies often require continuous monitoring of the joint condition, typically performed using magnetic resonance (MR) or ultrasound (US) imaging. US imaging is often the preferred screening or diagnostic tool as it is fast and inexpensive. However, conventional 2-D US has limited capability to compare imaging results between examinations because of its operator dependence and challenges related to repeat imaging in the same location and orientation.

Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs.

With the increase of the ageing in the world's population, the ageing and degeneration studies of physiological characteristics in human skin, bones, and muscles become important topics. Research on the ageing of bones, especially the skull, are paid much attention in recent years. In this study, a novel deep learning method representing the ageing-related dynamic attention (ARDA) is proposed.

3D US-Based Evaluation and Optimization of Tumor Coverage for US-Guided Percutaneous Liver Thermal Ablation.

Complete tumor coverage by the thermal ablation zone and with a safety margin (5 or 10 mm) is required to achieve the entire tumor eradication in liver tumor ablation procedures. However, 2D ultrasound (US) imaging has limitations in evaluating the tumor coverage by imaging only one or multiple planes, particularly for cases with multiple inserted applicators or irregular tumor shapes. In this paper, we evaluate the intra-procedural tumor coverage using 3D US imaging and investigate whether it can provide clinically needed information.

Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images.

Atherosclerotic carotid plaques have been shown to be closely associated with the risk of stroke. Since patients with symptomatic carotid plaques have a greater risk for stroke, stroke risk stratification based on the classification of carotid plaques into symptomatic or asymptomatic types is crucial in diagnosis, treatment planning, and medical treatment monitoring. A deep learning technique would be a good choice for implementing classification.