Journal of Medical Imaging

PURPOSE: The objective of this study is to review the accuracy of an augmented reality navigational guidance system designed to facilitate improved visualization, guidance, and accuracy during percutaneous needle-based procedures including biopsies and ablations. APPROACH: Using the HoloLens 2, the system registers and projects 3D CT-based models of segmented anatomy along with live ultrasound, fused with electromagnetically tracked instruments including ultrasound probes and needles, giving the operator comprehensive stereoscopic visualization for intraoperative planning and navigation during procedures...

PURPOSE: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. APPROACH: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected <mml:math xmlns:mml="https://www...

PURPOSE: Timely detection and treatment of visual impairments and age-related eye diseases are essential for maintaining a longer, healthier life. However, the shortage of appropriate medical equipment often impedes early detection. We have developed a portable self-imaging slit-light device utilizing NIR light and a scanning mirror. The objective of our study is to assess the accuracy and compare the performance of our device with conventional nonportable slit-lamp microscopes and anterior segment optical coherence tomography (AS-OCT) for screening and remotely diagnosing eye diseases, such as cataracts and glaucoma, outside of an eye clinic...

PURPOSE: Segmentation of the prostate and surrounding organs at risk from computed tomography is required for radiation therapy treatment planning. We propose an automatic two-step deep learning-based segmentation pipeline that consists of an initial multi-organ segmentation network for organ localization followed by organ-specific fine segmentation. APPROACH: Initial segmentation of all target organs is performed using a hybrid convolutional-transformer model, axial cross-attention UNet...

PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform...

PURPOSE: Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured. APPROACH: To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space...

PURPOSE: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. APPROACH: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (<mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>N</mml:mi><mml:mo>=</mml:mo><mml:mn>69</mml:mn></mml:mrow></mml:math>; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18 fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response...

PURPOSE: Ischemic myocardial scarring (IMS) is a common outcome of coronary artery disease that potentially leads to lethal arrythmias and heart failure. Late-gadolinium-enhanced cardiac magnetic resonance (CMR) imaging scans have served as the diagnostic bedrock for IMS, with recent advancements in machine learning enabling enhanced scar classification. However, the trade-off for these improvements is intensive computational and time demands. As a solution, we propose a combination of lightweight preprocessing (LWP) and template matching (TM) to streamline IMS classification...

PURPOSE: The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency. APPROACH: The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing...

PURPOSE: X-ray scatter significantly affects the image quality of cone beam computed tomography (CBCT). Although convolutional neural networks (CNNs) have shown promise in correcting x-ray scatter, their effectiveness is hindered by two main challenges: the necessity for extensive datasets and the uncertainty regarding model generalizability. This study introduces a task-based paradigm to overcome these obstacles, enhancing the application of CNNs in scatter correction. APPROACH: Using a CNN with U-net architecture, the proposed methodology employs a two-stage training process for scatter correction in CBCT scans...

PURPOSE: Colon cancer is one of the top three diseases in gastrointestinal cancers, and colon polyps are an important trigger of colon cancer. Early diagnosis and removal of colon polyps can avoid the incidence of colon cancer. Currently, colon polyp removal surgery is mainly based on artificial-intelligence (AI) colonoscopy, supplemented by deep-learning technology to help doctors remove colon polyps. With the development of deep learning, the use of advanced AI technology to assist in medical diagnosis has become mainstream and can maximize the doctor's diagnostic time and help doctors to better formulate medical plans...

PURPOSE: The diagnosis of primary bone tumors is challenging as the initial complaints are often non-specific. The early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. We propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging. First, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms...

Purpose: The goal of this study was to develop a fully convolutional network (FCN) tool to automatedly segment the left-ventricular (LV) myocardium in displacement encoding with stimulated echoes MRI. The segmentation results are used for LV chamber quantification and strain analyses in breast cancer patients susceptible to cancer therapy-related cardiac dysfunction (CTRCD). Approach: A DeepLabV3+ FCN with a ResNet-101 backbone was custom-designed to conduct chamber quantification on 45 female breast cancer datasets (23 training, 11 validation, and 11 test sets)...

PURPOSE: Training and evaluation of the performance of a supervised deep-learning model for the segmentation of hepatic tumors from intraoperative US (iUS) images, with the purpose of improving the accuracy of tumor margin assessment during liver surgeries and the detection of lesions during colorectal surgeries. APPROACH: In this retrospective study, a U-Net network was trained with the nnU-Net framework in different configurations for the segmentation of CRLM from iUS...

PURPOSE: Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). APPROACH: A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated...

PURPOSE: Joint space width (JSW) is a common metric used to evaluate joint structure on plain radiographs. For the hand, quantitative techniques are available for evaluation of the JSW of finger joints; however, such techniques have been difficult to establish for the trapeziometacarpal (TMC) joint. This study aimed to develop a validated method for measuring the radiographic joint space of the healthy TMC joint. APPROACH: Computed tomographic scans were taken of 15 cadaveric hands...

PURPOSE: Single-energy computed tomography (CT) often suffers from poor contrast yet remains critical for effective radiotherapy treatment. Modern therapy systems are often equipped with both megavoltage (MV) and kilovoltage (kV) X-ray sources and thus already possess hardware for dual-energy (DE) CT. There is unexplored potential for enhanced image contrast using MV-kV DE-CT in radiotherapy contexts. APPROACH: A single-line integral toy model was designed for computing basis material signal-to-noise ratio (SNR) using estimation theory...

PURPOSE: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery. APPROACH: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation...

PURPOSE: Diagnostic performance of prostate MRI depends on high-quality imaging. Prostate MRI quality is inversely proportional to the amount of rectal gas and distention. Early detection of poor-quality MRI may enable intervention to remove gas or exam rescheduling, saving time. We developed a machine learning based quality prediction of yet-to-be acquired MRI images solely based on MRI rapid localizer sequence, which can be acquired in a few seconds. APPROACH: The dataset consists of 213 (147 for training and 64 for testing) prostate sagittal T2-weighted (T2W) MRI localizer images and rectal content, manually labeled by an expert radiologist...

PURPOSE: Accurate whole-gland prostate segmentation is crucial for successful ultrasound-MRI fusion biopsy, focal cancer treatment, and radiation therapy techniques. Commercially available artificial intelligence (AI) models, using deep learning algorithms (DLAs) for prostate gland segmentation, are rapidly increasing in numbers. Typically, their performance in a true clinical context is scarcely examined or published. We used a heterogenous clinical MRI dataset in this study aiming to contribute to validation of AI-models...