Evaluating the Predictive Value of Glioma Growth Models for Low-Grade Glioma After Tumor Resection
Tumor growth models have the potential to model and predict the spatiotemporal evolution of glioma in individual patients. Infiltration of glioma cells is known to be faster along the white matter tracts, and therefore structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) can be used to inform the model. However, applying and evaluating growth models in real patient data is challenging. In this work, we propose to formulate the problem of tumor growth as a ranking problem, as opposed to a segmentation problem, and use the average precision (AP) as a performance metric. This enables an evaluation of...
Source: IEE Transactions on Medical Imaging - July 25, 2023 Category: Biomedical Engineering Source Type: research

Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer
Diagnosis of cancerous diseases relies on digital histopathology images from stained slides. However, the staining varies among medical centers, which leads to a domain gap of staining. Existing generative adversarial network (GAN) based stain transfer methods highly rely on distinct domains of source and target, and cannot handle unseen domains. To overcome these obstacles, we propose a self-supervised disentanglement network (SDN) for domain-independent optimization and arbitrary domain stain transfer. SDN decomposes an image into features of content and stain. By exchanging the stain features, the staining style of an i...
Source: IEE Transactions on Medical Imaging - July 24, 2023 Category: Biomedical Engineering Source Type: research

New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI
Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part...
Source: IEE Transactions on Medical Imaging - July 21, 2023 Category: Biomedical Engineering Source Type: research

Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging
Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer’s characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the ...
Source: IEE Transactions on Medical Imaging - July 20, 2023 Category: Biomedical Engineering Source Type: research

A Novel Spatial Position Prediction Navigation System Makes Surgery More Accurate
This article proposes a novel 3D spatial predictive positioning navigation (SPPN) technique to predict the real-time tip position of surgical instruments. In the first stage, we propose a trajectory prediction algorithm integrated with instrumental morphological constraints to generate the initial trajectory. Then, a novel hybrid physical model is designed to estimate the trajectory’s energy and mechanics. In the second stage, a point cloud clustering algorithm applies multi-information fusion to generate the maximum probability endpoint cloud. Then, an energy-weighted probability density function is introduced using sta...
Source: IEE Transactions on Medical Imaging - July 20, 2023 Category: Biomedical Engineering Source Type: research

Parameterized Gompertz-Guided Morphological AutoEncoder for Predicting Pulmonary Nodule Growth
The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our p...
Source: IEE Transactions on Medical Imaging - July 20, 2023 Category: Biomedical Engineering Source Type: research

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound
Ultrafast ultrasound has recently emerged as an alternative to traditional focused ultrasound. By virtue of the low number of insonifications it requires, ultrafast ultrasound enables the imaging of the human body at potentially very high frame rates. However, unaccounted for speed-of-sound variations in the insonified medium often result in phase aberrations in the reconstructed images. The diagnosis capability of ultrafast ultrasound is thus ultimately impeded. Therefore, there is a strong need for adaptive beamforming methods that are resilient to speed-of-sound aberrations. Several of such techniques have been proposed...
Source: IEE Transactions on Medical Imaging - July 14, 2023 Category: Biomedical Engineering Source Type: research

Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer’s Disease Diagnosis
Multi-modality medical data provide complementary information, and hence have been widely explored for computer-aided AD diagnosis. However, the research is hindered by the unavoidable missing-data problem, i.e., one data modality was not acquired on some subjects due to various reasons. Although the missing data can be imputed using generative models, the imputation process may introduce unrealistic information to the classification process, leading to poor performance. In this paper, we propose the Disentangle First, Then Distill (DFTD) framework for AD diagnosis using incomplete multi-modality medical images. First, we ...
Source: IEE Transactions on Medical Imaging - July 14, 2023 Category: Biomedical Engineering Source Type: research

One-Shot Weakly-Supervised Segmentation in 3D Medical Images
Deep neural networks typically require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot and weakly-supervised learning are promising research directions that reduce labeling effort by learning a new class from only one annotated image and using coarse labels instead, respectively. In this work, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings. Firstly a propagation-reconstruction network is proposed to propagate scribbles from one annotated volume to unlabeled 3D images based on the assumpti...
Source: IEE Transactions on Medical Imaging - July 13, 2023 Category: Biomedical Engineering Source Type: research

Mapping Multi-Modal Brain Connectome for Brain Disorder Diagnosis via Cross-Modal Mutual Learning
Recently, the study of multi-modal brain connectome has recorded a tremendous increase and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural networks, e.g., functional and structural connectivity derived from fMRI and DTI, are in some manner interacted but are not necessarily linearly related. Accordingly, there remains a great challenge to leverage complementary information for brain connectome analysis. Recently, Graph Convolutional Networks (GNN) have been widely applied to the fusion of multi-modal brain connectome. However, most existing GNN methods fail to couple inter-modal re...
Source: IEE Transactions on Medical Imaging - July 13, 2023 Category: Biomedical Engineering Source Type: research

Improving Medical Vision-Language Contrastive Pretraining With Semantics-Aware Triage
Medical contrastive vision-language pretraining has shown great promise in many downstream tasks, such as data-efficient/zero-shot recognition. Current studies pretrain the network with contrastive loss by treating the paired image-reports as positive samples and the unpaired ones as negative samples. However, unlike natural datasets, many medical images or reports from different cases could have large similarity especially for the normal cases, and treating all the unpaired ones as negative samples could undermine the learned semantic structure and impose an adverse effect on the representations. Therefore, we design a si...
Source: IEE Transactions on Medical Imaging - July 13, 2023 Category: Biomedical Engineering Source Type: research

Near-Infrared Fluorescence Tomography and Imaging of Ventricular Cerebrospinal Fluid Flow and Extracranial Outflow in Non-Human Primates
The role of the lymphatics in the clearance of cerebrospinal fluid (CSF) from the brain has been implicated in multiple neurodegenerative conditions. In premature infants, intraventricular hemorrhage causes increased CSF production and, if clearance is impeded, hydrocephalus and severe developmental disabilities can result. In this work, we developed and deployed near-infrared fluorescence (NIRF) tomography and imaging to assess CSF ventricular dynamics and extracranial outflow in similarly sized, intact non-human primates (NHP) following microdose of indocyanine green (ICG) administered to the right lateral ventricle. Flu...
Source: IEE Transactions on Medical Imaging - July 13, 2023 Category: Biomedical Engineering Source Type: research

RECIST-Induced Reliable Learning: Geometry-Driven Label Propagation for Universal Lesion Segmentation
Automatic universal lesion segmentation (ULS) from Computed Tomography (CT) images can ease the burden of radiologists and provide a more accurate assessment than the current Response Evaluation Criteria In Solid Tumors (RECIST) guideline measurement. However, this task is underdeveloped due to the absence of large-scale pixel-wise labeled data. This paper presents a weakly-supervised learning framework to utilize the large-scale existing lesion databases in hospital Picture Archiving and Communication Systems (PACS) for ULS. Unlike previous methods to construct pseudo surrogate masks for fully supervised training through ...
Source: IEE Transactions on Medical Imaging - July 12, 2023 Category: Biomedical Engineering Source Type: research

A Causality-Aware Graph Convolutional Network Framework for Rigidity Assessment in Parkinsonians
Rigidity is one of the common motor disorders in Parkinson’s disease (PD), which lead to life quality deterioration. The widely-used rating-scale-based approach for rigidity assessment still depends on the availability of experienced neurologists and is limited by rating subjectivity. Given the recent successful applications of quantitative susceptibility mapping (QSM) in auxiliary PD diagnosis, automated assessment of PD rigidity can be essentially achieved through QSM analysis. However, a major challenge is the performance instability due to the confounding factors (e.g., noise and distribution shift) which conceal the...
Source: IEE Transactions on Medical Imaging - July 11, 2023 Category: Biomedical Engineering Source Type: research

Patient-Specific Heart Geometry Modeling for Solid Biomechanics Using Deep Learning
Automated volumetric meshing of patient-specific heart geometry can help expedite various biomechanics studies, such as post-intervention stress estimation. Prior meshing techniques often neglect important modeling characteristics for successful downstream analyses, especially for thin structures like the valve leaflets. In this work, we present DeepCarve (Deep Cardiac Volumetric Mesh): a novel deformation-based deep learning method that automatically generates patient-specific volumetric meshes with high spatial accuracy and element quality. The main novelty in our method is the use of minimally sufficient surface mesh la...
Source: IEE Transactions on Medical Imaging - July 11, 2023 Category: Biomedical Engineering Source Type: research