Contrastive Clustering-Based Patient Normalization to Improve Automated In Vivo Oral Cancer Diagnosis from Multispectral Autofluorescence Lifetime Images.

Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.

Computer-assisted discrimination of cancerous and pre-cancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy.

Significance: Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients.

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach.

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently.

Clinical label-free endoscopic imaging of biochemical and metabolic autofluorescence biomarkers of benign, precancerous, and cancerous oral lesions.

Early detection is critical for improving the survival rate and quality of life of oral cancer patients; unfortunately, dysplastic and early-stage cancerous oral lesions are often difficult to distinguish from oral benign lesions during standard clinical oral examination. Therefore, there is a critical need for novel clinical technologies that would enable reliable oral cancer screening. The autofluorescence properties of the oral epithelial tissue provide quantitative information about morphological, biochemical, and metabolic tissue and cellular alterations accompanying carcinogenesis.

End-to-End Neural Network for Feature Extraction and Cancer Diagnosis of In Vivo Fluorescence Lifetime Images of Oral Lesions.

In contrast to previous studies that focused on classical machine learning algorithms and hand-crafted features, we present an end-to-end neural network classification method able to accommodate lesion heterogeneity for improved oral cancer diagnosis using multispectral autofluorescence lifetime imaging (maFLIM) endoscopy. Our method uses an autoencoder framework jointly trained with a classifier designed to handle overfitting problems with reduced databases, which is often the case in healthcare applications. The autoencoder guides the feature extraction process through the reconstruction loss and enables the potential use of unsupervised data for domain adaptation and improved generalization.

Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers.

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image.

Clinical label-free biochemical and metabolic fluorescence lifetime endoscopic imaging of precancerous and cancerous oral lesions.

Introduction: Incomplete head and neck cancer resection occurs in up to 85% of cases, leading to increased odds of local recurrence and regional metastases; thus, image-guided surgical tools for accurate, in situ and fast detection of positive margins during head and neck cancer resection surgery are urgently needed. Oral epithelial dysplasia and cancer development is accompanied by morphological, biochemical, and metabolic tissue and cellular alterations that can modulate the autofluorescence properties of the oral epithelial tissue.

Objective: This study aimed to test the hypothesis that autofluorescence biomarkers of oral precancer and cancer can be clinically imaged and quantified by means of multispectral fluorescence lifetime imaging (FLIM) endoscopy.

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM).

Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy.

Introduction: Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.

Methods: To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.

Accelerated positive contrast MRI of interventional devices using parallel compressed sensing imaging.

Susceptibility-based magnetic resonance imaging (MRI) method can image small MR-compatible devices with positive contrast. However, the relatively long data acquisition time required by the method hinders its practical applications. This study presents a parallel compressive sensing technique with a modified fast spin echo to accelerate data acquisition for the susceptibility-based positive contrast MRI.

Texture as an imaging biomarker for disease severity in golden retriever muscular dystrophy.

Introduction: Golden retriever muscular dystrophy (GRMD), an X-linked recessive disorder, causes similar phenotypic features to Duchenne muscular dystrophy (DMD). There is currently a need for a quantitative and reproducible monitoring of disease progression for GRMD and DMD.

Methods: To assess severity in the GRMD, we analyzed texture features extracted from multi-parametric MRI (T1w, T2w, T1m, T2m, and Dixon images) using 5 feature extraction methods and classified using support vector machines.

MARBLES - Metal Artifact Based Landmark Enhanced Susceptibility Weighted Imaging For Interventional Device Localization In MRI.

Susceptibility Weighted Imaging (SWI) is a method extensively studied for its application to improve contrast in MR imaging modality. The method enhances the visualization of magnetically susceptible content such as iron, calcium and zinc in the tissues by using the susceptibility differences in tissues to generate a unique image contrast. In this study, we propose an SWI based approach to improve the visualization of interventional devices in MRI data.

Implementation of High-Performance Correlation and Mapping Engine for Rapid Generation of Brain Connectivity Networks from Big fMRI Data.

With the emergence of the dynamic functional connectivity analysis, and the studies relying on real-time neurological feedback, the need for rapid processing methods becomes even more critical. Seed-based Correlation Analysis (SCA) of fMRI data has been used to create brain connectivity networks. With close to a million voxels in a fMRI dataset, the number of calculations involved in SCA becomes high.

New Similarity Metric for Registration of MRI to Histology: Golden Retriever Muscular Dystrophy Imaging.

Objective: Histology is often used as a gold standard to evaluate noninvasive imaging modalities such as a magnetic resonance imaging (MRI). Spatial correspondence between histology and MRI is a critical step in quantitative evaluation of skeletal muscle in golden retriever muscular dystrophy (GRMD). Registration becomes technically challenging due to nonorthogonal histology section orientation, section distortion, and the different image contrast and resolution.

Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system.

Purpose: To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system.

Methods: A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain.

Parallel compressive sensing in a hybrid space: Application in interventional MRI.

Recently, susceptibility based positive contrast MRI technique emerged as an effective method of visualizing the small MR compatible devices, such as brachytherapy seeds. One of the challenges associated with this method is the long scan time. In this work, we present an accelerated susceptibility based positive contrast MR imaging method, in which the susceptibility map can be generated from an under-sampled data.

Black-blood T2* mapping with delay alternating with nutation for tailored excitation.

Purpose: To develop a black-blood T2* mapping method using a Delay Alternating with Nutation for Tailored Excitation (DANTE) preparation combined with a multi-echo gradient echo (GRE) readout (DANTE-GRE).

Materials And Methods: Simulations of the Bloch equation for DANTE-GRE were performed to optimize sequence parameters. After optimization, the sequence was applied to a phantom scan and to neck and lower extremity scans conducted on 12 volunteers at 3T using DANTE-GRE, Motion-Sensitized Driven Equilibrium (MSDE)-GRE, and multi-echo GRE.

Compressed sensing MRI reconstruction from 3D multichannel data using GPUs.

Purpose: To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs).

Methods: The sparsity of MRI signals and parallel array receivers can reduce the data acquisition requirements. However, iterative CS reconstructions from data acquired using an array system may take a significantly long time, especially for a large number of parallel channels.

Accelerated susceptibility-based positive contrast imaging of MR compatible metallic devices based on modified fast spin echo sequences.

This study aims to develop an accelerated susceptibility-based positive contrast MR imaging method for visualizing MR compatible metallic devices. A modified fast spin echo sequence is used to accelerate data acquisition. Each readout gradient in the modified fast spin echo is slightly shifted by a short distance T .

High-resolution MRI of spinal cords by compressive sensing parallel imaging.

Spinal Cord Injury (SCI) is a common injury due to diseases or accidents. Noninvasive imaging methods play a critical role in diagnosing SCI and monitoring the response to therapy. Magnetic Resonance Imaging (MRI), by the virtue of providing excellent soft tissue contrast, is the most promising imaging method for this application.

MRI of biopsy needles by susceptibility mapping based on Wiener filter F and L1-regularization.

Biopsy needles are devices that have been used for intravenous therapy. However, the high susceptibility of needles results in signal loss and distortion which makes the location of needles hard to identify in the MRI images. A variety of approaches has been proposed to quantify the susceptibility of the materials being imaged because susceptibility is an intrinsic property that can be used to make a good contrast between different materials.

Correlating 2D histological slice with 3D MRI image volume using smart phone as an interactive tool for muscle study.

In muscle dystrophy studies, registration of histological image with MRI image volume enables cross validation of MRI biomarkers using pathological result. However, correlation of 2D histology slice with 3D MRI volume is technically challenging due to the potentially non-orthogonal slice plane and incomplete or distorted histological slice. This paper presents an efficient method to directly perform the 2D-3D registration.