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- Jeffrey A. FesslerSuggested from co-authorship
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Works161 from public data
- 360
- Multi-Temporal Recurrent Neural Networks for Progressive Non-uniform Single Image Deblurring with Incremental Temporal Training278
This work proposes incremental temporal training with constructed MT level dataset from time-resolved dataset, develops novel MT-RNNs with recurrent feature maps, and investigates progressive single image deblurring over iterations.
- Task-Aware Variational Adversarial Active Learning185
The proposed methods exploit variational adversarial active learning (VAAL), that considered data distribution of both label and unlabeled pools, by incorporating learning loss prediction module and RankCGAN concept into VAAL by modeling loss prediction as a ranker.
- MRI-Based Nonrigid Motion Correction in Simultaneous PET/MRI185
Tagged MRI motion correction in simultaneous PET/MRI significantly improves lesion detection compared with respiratory gating and no motion correction while reducing radiation dose and provides the rationale for evaluation in simultaneous whole-body PET/ MRI clinical studies.
- PAIP 2019: Liver cancer segmentation challenge132
The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assurance Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets and has the potential to aid the development and benchmarking of cancer diagnosis and segmentation.
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- Nonrigid PET motion compensation in the lower abdomen using simultaneous tagged-MRI and PET imaging116
This work shows that PET motion correction using motion fields measured with tagged-MRI in simultaneous PET-MRI acquisitions can be made practical for clinical application and that doing so has the potential to remove motion blur in whole-body PET studies of the torso.
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- A Simple Regularizer for B-spline Nonrigid Image Registration That Encourages Local Invertibility87
This paper describes sufficient conditions for local invertibility of deformations based on B-spline bases and incorporates these conditions into nonrigid image registration methods based on a simple penalty approach that encourages diffeomorphic deformations.
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- A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection63
This work proposes a single multi-task DNN that yields accurate detections of objects, grasp position and relationship reasoning among objects, and yields state-of-the-art performance.
- Improved quantitative 90 Y bremsstrahlung SPECT / CT reconstruction with Monte Carlo scatter modeling63
Quantitative accuracy of 90Y bremsstrahlung imaging can be substantially improved with MC scatter modeling without significant degradation in image noise or intensive computational requirements.
- Training Deep Learning Based Image Denoisers From Undersampled Measurements Without Ground Truth and Without Image Prior61
This work proposes novel methods based on two well-grounded theories: denoiser-approximate message passing (D-AMP) and Stein's unbiased risk estimator (SURE), able to train deep learning based image denoisers from undersampled measurements without ground truth images and without additional image priors, and to recover images with state-of-the-art qualities fromundersampled data.
- Correction for Collimator-Detector Response in SPECT Using Point Spread Function Template56
An alternative approach to CDR modeling is proposed that uses a Gaussian function plus a 2-D B-spline PSF template and fit the model to measurements of an I-131 point source at several distances and captures the characteristics of the septal penetration tails.
- DATID-3D: Diversity-Preserved Domain Adaptation Using Text-to-Image Diffusion for 3D Generative Model51
DATID-3D, a domain adaptation method tailored for 3D generative models using text-to-image diffusion models that can synthesize diverse images per text prompt without collecting additional images and camera information for the target domain, is proposed and demonstrated.
- Bias Atlases for Segmentation-Based PET Attenuation Correction Using PET-CT and MR50
This study applied multiple thresholds to the CTs of 23 patients to classify tissues to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction and shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.6%, and 12.9% RMSE bias in lungs,Fat/in-phase MR images, respectively.
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- Specific visualization of neuromelanin-iron complex and ferric iron in the human post-mortem substantia nigra using MR relaxometry at 7T47
The aim of this study was to non‐invasively delineate the spatial distributions of paramagnetic magnetic susceptibility perturbers, such as NM‐iron complex and ferric iron in SN.
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- Unsupervised Training of Denoisers for Low-Dose CT Reconstruction Without Full-Dose Ground Truth45
This work attempts to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNN's for denoising sensor measurements or sinograms without full-dose ground truth images.
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- PAIP 2020: Microsatellite instability prediction in colorectal cancer37
This paper supports the effectiveness of digital pathology for identifying the relationship between colorectal cancer and the MSI characteristics and summarizes the various methods used in the PAIP 2020 challenge.
- Alternating Direction Method of Multiplier for Tomography With Nonlocal Regularizers36
This work proposes to use variable splitting to separate the likelihood term and the regularizer term for penalized emission tomographic image reconstruction problem and to optimize it using the alternating direction method of multiplier (ADMM), and proposes a fast algorithm to optimize the ADMM parameter based on convergence rate analysis.
- Cancelable ECG Biometrics Using Compressive Sensing-Generalized Likelihood Ratio Test35
This work proposes methods to overcome the issue of irrevocability in ECG biometrics without compromising performance and proposes a cancelable secure user template with a novel revocation process that is practically non-invertible and robust to record multiplicity attack.
- Noise Properties of Motion-Compensated Tomographic Image Reconstruction Methods33
These theoretical noise analyses show that the variances of PMM and MTR are lower than or comparable to the variance of PMC due to the statistical weighting, and investigate the noise properties of MCIR methods with Poisson models and quadratic regularizers by deriving accurate and fast variance prediction formulas using an “analytical approach.
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- PAIP2021: Perineural Invasion in Multiple Organ Cancer (Colon, Prostate, and Pancreatobiliary tract)5
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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