publications

2026

1. DICOM-CLIP: Zero-Shot Acquisition Parameter Retrieval from Unprocessed DICOM Files

   Prahlad Anand, Aaron Carass, Ellen M. Mowry, Scott D. Newsome, Jerry L. Prince, and Blake E. Dewey

   In Proceedings of IEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026

   Abs DOI

   Contrast variation in magnetic resonance (MR) images arises from acquisition-specific parameters such as echo time, repetition time, and flip angle, which vary widely across individual acquisitions and hardware. These parameters are included in imaging metadata tags, but not often used for image categorization. Instead, broad labels such as "T1-weighted" and "T2-FLAIR" are commonly used, but the resultant images exhibit wide intra-label contrast variation. Creating anatomy-invariant contrast representations that are faithful to intra-contrast variation is valuable for image contrast identification and downstream tasks such as image harmonization. However, in large-scale real-world acquisitions, metadata is often erroneous, inconsistent, or missing, making interpretation difficult and complex. To recover these parameters and learn fine-grained representations of MR contrast, we propose DICOM-CLIP, a multimodal contrastive learning framework that embeds raw DICOM slices in the same space as their associated metadata. DICOM-CLIP is trained in a single-stage supervised contrastive (SupCon) setting using ground-truth metadata, optimizing embedding separability using exact-match supervision. Our approach yields up to 6% more accurate retrieval and up to 23% improvement in Mean Average Error (MAE) over the current state-of-the-art.

2. ECLARE: efficient cross-planar learning for anisotropic resolution enhancement

   Samuel W. Remedios, Shuwen Wei, Shuo Han, Jinwei Zhang, Aaron Carass, Kurt G. Schilling, Dzung L. Pham, Jerry L. Prince, and Blake E. Dewey

   Journal of Medical Imaging, 2026

   Abs DOI

   Purpose: In clinical imaging, magnetic resonance (MR) image volumes are often acquired as stacks of 2D slices with decreased scan times, improved signal-to-noise ratio, and image contrasts unique to 2D MR pulse sequences. Although this is sufficient for clinical evaluation, automated algorithms designed for 3D analysis perform poorly on multislice 2D MR volumes, especially those with thick slices and gaps between slices. Superresolution (SR) methods aim to address this problem, but previous methods do not address all of the following: slice profile shape estimation, slice gap, domain shift, and noninteger or arbitrary upsampling factors.ApproachWe propose ECLARE (Efficient Cross-planar Learning for Anisotropic Resolution Enhancement), a self-SR method that addresses each of these factors. ECLARE uses a slice profile estimated from the multislice 2D MR volume, trains a network to learn the mapping from low-resolution to high-resolution in-plane patches from the same volume, performs SR with antialiasing, and respects the image FOV during resampling. We compared ECLARE with cubic B-spline interpolation, SMORE, and other contemporary SR methods. We used realistic and representative simulations on human head MR volumes so that quantitative performance against ground truth can be computed. Specifically, healthy T1-w and people with MS T2-w FLAIR datasets were used for evaluations. We used the peak signal-to-noise ratio and structural similarity index measure as signal recovery metrics. We additionally used two independent brain parcellation algorithms, SLANT and SynthSeg, to compute the consistency Dice similarity coefficient and the R2 coefficient of determination, respectively, as comparison metrics.ResultsFor images with up to 5 mm of slice thickness and up to 1.5 mm of gap, ECLARE achieves greater mean PSNR and SSIM compared with other methods. In representative regions of interest, such as the ventricles, caudate, cerebral white matter, and cerebellar white matter, ECLARE performs comparably or better than other approaches. These trends are similar for both investigated datasets.ConclusionsThe use of slice profile estimation, FOV-aware resampling, and self-SR allowed ECLARE to robustly superresolve anisotropic images without the need for external training data. Future work will investigate the utility of ECLARE on other organs, species, modalities, and resolutions. Our code is open-source and available at https://www.github.com/sremedios/eclare.

3. Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI

   Samuel W. Remedios, Shuwen Wei, Aaron Carass, Blake E. Dewey, and Jerry L. Prince

   In Proceedings of Information Processing in Medical Imaging, 2026

   Abs DOI

   Magnetic resonance (MR) images are often acquired as aniso-tropic volumes in clinical settings. Such volumes have a worse through-plane resolution than in-plane resolution, hampering results in many processing pipelines that expect isotropic resolutions. Super-resolution (SR) is a promising methodology to address this problem, but there is concern whether the estimated high-resolution (HR) image suffers from egregious hallucinations, especially with deep learning methods that produce aesthetically pleasing results. One approach to restrict the impact of hallucinations is to guarantee that the estimated HR image is exactly cycle-consistent with the low-resolution observation. The denoising diffusion null space model (DDNM) achieves this through a range null space decomposition, but the specific design of the forward map is left to the application. In this work, we analyze the forward problem in 2D MR acquisition and construct an appropriate linear map A. We train a denoising diffusion probabilistic model on T}}_1}}1-weighted (T}}_1}}1-w) head MR images from multiple datasets and implement DDNM using A for the SR task. We show that the approach yields exact cycle-consistent solutions that are also realistic. We evaluated the approach in a wide variety of T}}_1}}1-w MR datasets, including withheld subjects from training sites and two sites outside of the training domain. We achieve excellent qualitative and quantitative results according to both distortion and perceptual metrics.

4. Likelihood-Separable Diffusion Inference for Multi-Image MRI Super-Resolution

   Samuel W. Remedios, Zhangxing Bian, Shuwen Wei, Aaron Carass, Jerry L. Prince, and Blake E. Dewey

   2026

   Abs DOI arXiv

   Diffusion models are the current state-of-the-art for solving inverse problems in imaging. Their impressive generative capability allows them to approximate sampling from a prior distribution, which alongside a known likelihood function permits posterior sampling without retraining the model. While recent methods have made strides in advancing the accuracy of posterior sampling, the majority focuses on single-image inverse problems. However, for modalities such as magnetic resonance imaging (MRI), it is common to acquire multiple complementary measurements, each low-resolution along a different axis. In this work, we generalize common diffusion-based inverse single-image problem solvers for multi-image super-resolution (MISR) MRI. We show that the DPS likelihood correction allows an exactly-separable gradient decomposition across independently acquired measurements, enabling MISR without constructing a joint operator, modifying the diffusion model, or increasing network function evaluations. We derive MISR versions of DPS, DMAP, DPPS, and diffusion-based PnP/ADMM, and demonstrate substantial gains over SISR across 4x/8x/16x anisotropic degradations. Our results achieve state-of-the-art super-resolution of anisotropic MRI volumes and, critically, enable reconstruction of near-isotropic anatomy from routine 2D multi-slice acquisitions, which are otherwise highly degraded in orthogonal views.

5. Solving a Nonlinear Blind Inverse Problem for Tagged MRI with Physics and Deep Generative Priors

   Zhangxing Bian, Shuwen Wei, Samuel W. Remedios, Junyu Chen, Aaron Carass, Blake E. Dewey, and Jerry L Prince

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

   Abs DOI arXiv

   Tagged MRI enables tracking internal tissue motion non-invasively. It encodes motion by modulating anatomy with periodic tags, which deform along with tissue. However, the entanglement between anatomy, tags and motion poses significant challenges for post-processing. The existence of tags and imaging blur hinders downstream tasks such as segmenting anatomy. Tag fading, due to T1-relaxation, disrupts the brightness constancy assumption for motion tracking. For decades, these challenges have been handled in isolation and sub-optimally. In contrast, we introduce a blind and nonlinear inverse framework for tagged MRI that, for the first time, unifies these tasks: anatomical image recovery, high-resolution cine image synthesis, and motion estimation. At its core, the synergy of MR physics and generative priors enables us to blindly estimate the unknown forward imaging models and high-resolution underlying anatomy, while simultaneously tracking 3D diffeomorphic Lagrangian motion over time. Experiments on tagged brain MRI demonstrate that our approach yields high-resolution anatomy images, cine images, and more accurate motion than specialized methods.

6. Harmonizing MR Images Across 100+ Scanners: Multi-site Validation with Traveling Subjects and Real-world Protocols

   Savannah P. Hays, Lianrui Zuo, Muhammad Faizyab Ali Chaudhary, Kathleen M. Bartz, Samuel W. Remedios, Jinwei Zhang, Jiachen Zhuo, Murat Bilgel, Shiv Saidha, Ellen M. Mowry, Scott D. Newsome, Jerry L. Prince, and 2 more authors

   2026

   Abs DOI arXiv

   Reliable harmonization of heterogeneous magnetic resonance (MR) image datasets, especially those acquired in pragmatic clinical trials, is critical to advance multi-center neuroimaging studies and translational machine learning in healthcare. We present an enhanced and rigorously validated version of the HACA3 harmonization algorithm, which we refer to as HACA3+, incorporating key methodological enhancements: (1) an improved artifact encoder to better isolate and mitigate image artifacts, (2) background and foreground-sensitive attention mechanisms to increase harmonization specificity, and (3) extensive training using data spanning 100+ scanners from 64 independent sites, providing a broader diversity of scanners than other harmonization methods. Our study focuses on four commonly acquired MR image contrasts (T1-weighted, T2-weighted, proton density, & fluid-attenuated inversion recovery), reflecting realistic clinical protocols. We perform inter-site harmonization experiments using traveling subjects to assess the generalization and robustness of the harmonization model. We compare the results of the publicly available version of HACA3 and our implementation, HACA3+. Downstream relevance is further established through whole brain segmentation and image imputation. Finally, we justify each enhancement through an ablation experiment. Pre-trained weights and code for HACA3+ are made publicly available.

2025

1. Super-Resolution in Clinically Available Spinal Cord MRIs Enables Automated Atrophy Analysis

   Blake E. Dewey, Samuel W. Remedios, Muraleetharan Sanjayan, Nicole Bou Rjeily, Alexandra Zambriczki Lee, Chelsea Wyche, Safiya Duncan, Jerry L. Prince, Peter A. Calabresi, Kathryn C. Fitzgerald, and Ellen M. Mowry

   American Journal of Neuroradiology, 2025

   Abs DOI

   BACKGROUND AND PURPOSE: Measurement of the mean upper cervical cord area (MUCCA) is an important biomarker in the study of neurodegeneration. However, dedicated high-resolution (HR) scans of the cervical spinal cord are rare in standard-of-care imaging due to timing and clinical usability. Most clinical cervical spinal cord imaging is sagittally acquired in 2D with thick slices and anisotropic voxels. As a solution, previous work describes HR T1-weighted brain imaging for measuring the upper cord area, but this is still not common in clinical care.MATERIALS AND METHODS: We propose using a zero-shot super-resolution technique, synthetic multi-orientation resolution enhancement (SMORE), already validated in the brain, to enhance the resolution of 2D-acquired scans for upper cord area calculations. To incorporate super-resolution in spinal cord analysis, we validate SMORE against HR research imaging and in a real-world longitudinal data analysis.RESULTS: Super-resolved (SR) images reconstructed by using SMORE showed significantly greater similarity to the ground truth than low-resolution (LR) images across all tested resolutions (P < .001 for all resolutions in peak signal-to-noise ratio [PSNR] and mean structural similarity [MSSIM]). MUCCA results from SR scans demonstrate excellent correlation with HR scans (r > 0.973 for all resolutions) compared with LR scans. Additionally, SR scans are consistent between resolutions (r > 0.969), an essential factor in longitudinal analysis. Compared with clinical outcomes such as walking speed or disease severity, MUCCA values from LR scans have significantly lower correlations than those from HR scans. SR results have no significant difference. In a longitudinal real-world data set, we show that these SR volumes can be used in conjunction with T1-weighted brain scans to show a significant rate of atrophy (-0.790, P = .020 versus -0.438, P = .301 with LR).CONCLUSIONS: Super-resolution is a valuable tool for enabling large-scale studies of cord atrophy, as LR images acquired in clinical practice are common and available.9HPT9-hole peg testCSCcervical spinal cordEDSSExpanded Disability Status ScaleHRhigh-resolutionLRlow-resolutionMSFCMS functional compositeMSSIMmean structural similarityMUCCAmean upper cervical cord areaPMJpontomedullary junctionPSNRpeak signal-to-noise ratioSMOREsynthetic multi-orientation resolution enhancementSRsuper-resolvedT25FWtimed 25-foot walk

2. Breaking data silos: incorporating the DICOM imaging standard into the OMOP CDM to enable multimodal research

   Woo Yeon Park, Teri Sippel Schmidt, Gabriel Salvador, Kevin O’Donnell, Brad Genereaux, Kyulee Jeon, Seng Chan You, Blake E. Dewey, Paul Nagy, and Alzheimer’s Disease Neuroimaging Initiative

   Journal of the American Medical Informatics Association, Oct 2025

   Abs DOI

   This work incorporates the Digital Imaging Communications in Medicine (DICOM) Standard into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) to standardize and accurately represent imaging studies, such as acquisition parameters, in multimodal research studies.DICOM is the internationally adopted standard that defines entities and relationships for biomedical imaging data used for clinical imaging studies. Most of the complexity in the DICOM data structure centers around the metadata. This metadata contains information about the patient and the modality acquisition parameters. We parsed the DICOM vocabularies in Parts 3, 6, and 16 to obtain structured metadata definitions and added these as custom concepts in the OMOP CDM vocabulary. To validate our pipeline, we harvested and transformed DICOM metadata from magnetic resonance images in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.We extracted and added 5183 attributes and 3628 coded values from the DICOM standard as custom concepts to the OMOP CDM vocabulary. We ingested 545 ADNI imaging studies containing 4756 series and harvested 691 224 metadata values. They were filtered, transformed, and loaded in the OMOP CDM imaging extension using the OMOP concepts for the DICOM attributes and values.This work is adaptable to clinical DICOM data. Future work will validate scalability and incorporate outcomes from automated analysis to provide a complete characterization research study within the OMOP framework.The incorporation of medical imaging into clinical observational studies has been a barrier to multi model research. This work demonstrates detailed phenotypes and paves the way for observational multimodal research.

3. Diffusion-Driven Generation of Minimally Preprocessed Brain MRI

   Samuel W. Remedios, Aaron Carass, Jerry L. Prince, and Blake E. Dewey

   Oct 2025

   DOI arXiv

2024

1. Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

   Woo Yeon Park, Kyulee Jeon, Teri Sippel Schmidt, Haridimos Kondylakis, Tarik Alkasab, Blake E. Dewey, Seng Chan You, and Paul Nagy

   Journal of Imaging Informatics in Medicine, Apr 2024

   Abs DOI

   The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.

2. Beyond MR Image Harmonization: Resolution Matters Too

   Savannah P. Hays, Samuel W. Remedios, Lianrui Zuo, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Blake E. Dewey, and Jerry L. Prince

   In Proceedings of Simulation and Synthesis in Medical Imaging, Apr 2024

   Abs DOI

   Magnetic resonance (MR) imaging is commonly used in the clinical setting to non-invasively monitor the body. There exists a large variability in MR imaging due to differences in scanner hardware, software, and protocol design. Ideally, a processing algorithm should perform robustly to this variability, but that is not always the case in reality. This introduces a need for image harmonization to overcome issues of domain shift when performing downstream analysis such as segmentation. Most image harmonization models focus on acquisition parameters such as inversion time or repetition time, but they ignore an important aspect in MR imaging—resolution. In this paper, we evaluate the impact of image resolution on harmonization using a pretrained harmonization algorithm. We simulate 2D acquisitions of various slice thicknesses and gaps from 3D acquired, 1 mm3 isotropic MR images and demonstrate how the performance of a state-of-the-art image harmonization algorithm varies as resolution changes. We discuss the most ideal scenarios for image resolution including acquisition orientation when 3D imaging is not available, which is common for many clinical scanners. Our results show that harmonization on low-resolution images does not account for acquisition resolution and orientation variations. Super-resolution can be used to alleviate resolution variations but it is not always used. Our methodology can generalize to help evaluate the impact of image acquisition resolution for multiple tasks. Determining the limits of a pretrained algorithm is important when considering preprocessing steps and trust in the results.

2023

1. Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap

   Samuel W. Remedios, Shuo Han, Lianrui Zuo, Aaron Carass, Dzung L. Pham, Jerry L. Prince, and Blake E. Dewey

   In Proceedings of Simulation and Synthesis in Medical Imaging, Apr 2023

   Abs DOI

   Magnetic resonance (MR) images are often acquired as multi-slice volumes to reduce scan time and motion artifacts while improving signal-to-noise ratio. These slices often are thicker than their in-plane resolution and sometimes are acquired with gaps between slices. Such thick-slice image volumes (possibly with gaps) can impact the accuracy of volumetric analysis and 3D methods. While many super-resolution (SR) methods have been proposed to address thick slices, few have directly addressed the slice gap scenario. Furthermore, data-driven methods are sensitive to domain shift due to the variability of resolution, contrast in acquisition, pathology, and differences in anatomy. In this work, we propose a self-supervised SR technique to address anisotropic MR images with and without slice gap. We compare against competing methods and validate in both signal recovery and downstream task performance on two open-source datasets and show improvements in all respects. Our code publicly available at https://gitlab.com/iacl/smore.

2. HACA3: A unified approach for multi-site MR image harmonization

   Lianrui Zuo, Yihao Liu, Yuan Xue, Blake E. Dewey, Samuel W. Remedios, Savannah P. Hays, Murat Bilgel, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Susan M. Resnick, Jerry L. Prince, and 1 more author

   Computerized Medical Imaging and Graphics, Apr 2023

   Abs DOI

   The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.

2022

1. Chapter 11 - Medical image harmonization through synthesis

   Blake E. Dewey, Yufan He, Yihao Liu, Lianrui Zuo, and Jerry L. Prince

   In Biomedical Image Synthesis and Simulation, Apr 2022

   Abs DOI

   Differences in acquisition hardware and acquisition protocols often cause medical image analysis methods to be unreliable or inaccurate. This is especially problematic in modern deep learning-based methods, which are typically highly tuned to a particular type of input data during training. Image synthesis can be used to overcome acquisition variability by creating harmonized versions of images, which mimic a standard acquisition and produce robust results. This chapter considers how modern image synthesis can be used to create realistic and reliable harmonized images in magnetic resonance imaging and optical coherence tomography.

2021

1. SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning

   Can Zhao, Blake E. Dewey, Dzung L. Pham, Peter A. Calabresi, Daniel S. Reich, and Jerry L. Prince

   IEEE Transactions on Medical Imaging, Apr 2021

   Abs DOI

   High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images. This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods

2. Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory

   Lianrui Zuo, Blake E. Dewey, Yihao Liu, Yufan He, Scott D. Newsome, Ellen M. Mowry, Susan M. Resnick, Jerry L. Prince, and Aaron Carass

   NeuroImage, Apr 2021

   Abs DOI

   In magnetic resonance (MR) imaging, a lack of standardization in acquisition often causes pulse sequence-based contrast variations in MR images from site to site, which impedes consistent measurements in automatic analyses. In this paper, we propose an unsupervised MR image harmonization approach, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), which aims to alleviate contrast variations in multi-site MR imaging. Designed using information bottleneck theory, CALAMITI learns a globally disentangled latent space containing both anatomical and contrast information, which permits harmonization. In contrast to supervised harmonization methods, our approach does not need a sample population to be imaged across sites. Unlike traditional unsupervised harmonization approaches which often suffer from geometry shifts, CALAMITI better preserves anatomy by design. The proposed method is also able to adapt to a new testing site with a straightforward fine-tuning process. Experiments on MR images acquired from ten sites show that CALAMITI achieves superior performance compared with other harmonization approaches.

3. Information-Based Disentangled Representation Learning for Unsupervised MR Harmonization

   Lianrui Zuo, Blake E. Dewey, Aaron Carass, Yihao Liu, Yufan He, Peter A. Calabresi, and Jerry L. Prince

   In Proceedings of Information Processing in Medical Imaging, Apr 2021

   Abs DOI

   Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate for contrast variation in MR images. Current harmonization approaches either require cross-site traveling subjects for supervised training or heavily rely on site-specific harmonization models to encourage harmonization accuracy. These requirements potentially limit the application of current harmonization methods in large-scale multi-site studies. In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory. CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. Our model is also able to adapt itself to harmonize MR images from a new site with fine tuning solely on images from the new site. Both qualitative and quantitative results show that the proposed method achieves superior performance compared with other unsupervised harmonization approaches.

4. MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions

   Blake E. Dewey, Xiang Xu, Linda Knutsson, Amod Jog, Jerry L. Prince, Peter B. Barker, Peter C.M. Zijl, Richard Leigh, and Paul Nyquist

   American Journal of Neuroradiology, Apr 2021

   Abs DOI

   BACKGROUND AND PURPOSE: White matter lesions of presumed ischemic origin are associated with progressive cognitive impairment and impaired BBB function. Studying the longitudinal effects of white matter lesion biomarkers that measure changes in perfusion and BBB patency within white matter lesions is required for long-term studies of lesion progression. We studied perfusion and BBB disruption within white matter lesions in asymptomatic subjects.MATERIALS AND METHODS: Anatomic imaging was followed by consecutive dynamic contrast-enhanced and DSC imaging. White matter lesions in 21 asymptomatic individuals were determined using a Subject-Specific Sparse Dictionary Learning algorithm with manual correction. Perfusion-related parameters including CBF, MTT, the BBB leakage parameter, and volume transfer constant were determined.RESULTS: MTT was significantly prolonged (7.88 [SD, 1.03] seconds) within white matter lesions compared with normal-appearing white (7.29 [SD, 1.14] seconds) and gray matter (6.67 [SD, 1.35] seconds). The volume transfer constant, measured by dynamic contrast-enhanced imaging, was significantly elevated (0.013 [SD, 0.017] minutes-1) in white matter lesions compared with normal-appearing white matter (0.007 [SD, 0.011] minutes-1). BBB disruption within white matter lesions was detected relative to normal white and gray matter using the DSC-BBB leakage parameter method so that increasing BBB disruption correlated with increasing white matter lesion volume (Spearman correlation coefficient = 0.44; P < .046).CONCLUSIONS: A dual-contrast-injection MR imaging protocol combined with a 3D automated segmentation analysis pipeline was used to assess BBB disruption in white matter lesions on the basis of quantitative perfusion measures including the volume transfer constant (dynamic contrast-enhanced imaging), the BBB leakage parameter (DSC), and MTT (DSC). This protocol was able to detect early pathologic changes in otherwise healthy individuals.cSVDcerebrovascular small-vessel diseaseDCEdynamic contrast-enhancedGdgadoliniumK2BBB leakage parameterKtransvolume transfer constantWMLwhite matter lesion

2020

1. A Disentangled Latent Space for Cross-Site MRI Harmonization

   Blake E. Dewey, Lianrui Zuo, Aaron Carass, Yufan He, Yihao Liu, Ellen M. Mowry, Scott Newsome, Jiwon Oh, Peter A. Calabresi, and Jerry L. Prince

   In Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI), Apr 2020

   Abs DOI

   Accurate interpretation and quantification of magnetic resonance imaging (MRI) is vital to medical research and clinical practice. However, lack of MRI standardization and differences in acquisition protocols often lead to measurement inconsistencies across sites. Image harmonization techniques have been shown to improve qualitative and quantitative consistency between differently acquired scans. Unfortunately, these methods typically require paired training data from traveling subjects (for supervised methods) or assumptions about anatomical similarities between the populations (for unsupervised methods). We propose a deep learning-based harmonization technique with limited supervision for use in standardization across scanners and sites. By leveraging a disentangled latent space represented by a high-resolution anatomical information component (}}\backslashbeta }}β) and a low-dimensional contrast component (}}\backslashtheta }}θ), the proposed method trains a cross-site harmonization model using databases of multi-modal image pairs acquired separately from each of the scanners to be harmonized. In this manuscript, we show that by using T}}_1}}1-weighted and T}}_2}}2-weighted images acquired from different subjects at three different sites, we can achieve a stable extraction of }}\backslashbeta }}\betawith a continuous representation of }}\backslashtheta }}θ. We also demonstrate that this allows cross-site harmonization without the need for paired data between sites.

2019

1. DeepHarmony: A deep learning approach to contrast harmonization across scanner changes

   Blake E. Dewey, Can Zhao, Jacob C. Reinhold, Aaron Carass, Kathryn C. Fitzgerald, Elias S. Sotirchos, Shiv Saidha, Jiwon Oh, Dzung L. Pham, Peter A. Calabresi, Peter C.M. van Zijl, and Jerry L. Prince

   Magnetic Resonance Imaging, Apr 2019

   Abs DOI

   Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks reproducibility between protocols and scanners. It has been shown that even when care is taken to standardize acquisitions, any changes in hardware, software, or protocol design can lead to differences in quantitative results. This greatly impacts the quantitative utility of MRI in multi-site or long-term studies, where consistency is often valued over image quality. We propose a method of contrast harmonization, called DeepHarmony, which uses a U-Net-based deep learning architecture to produce images with consistent contrast. To provide training data, a small overlap cohort (n = 8) was scanned using two different protocols. Images harmonized with DeepHarmony showed significant improvement in consistency of volume quantification between scanning protocols. A longitudinal MRI dataset of patients with multiple sclerosis was also used to evaluate the effect of a protocol change on atrophy calculations in a clinical research setting. The results show that atrophy calculations were substantially and significantly affected by protocol change, whereas such changes have a less significant effect and substantially reduced overall difference when using DeepHarmony. This establishes that DeepHarmony can be used with an overlap cohort to reduce inconsistencies in segmentation caused by changes in scanner protocol, allowing for modernization of hardware and protocol design in long-term studies without invalidating previously acquired data.

2. Evaluating the impact of intensity normalization on MR image synthesis

   Jacob C. Reinhold, Blake E. Dewey, Aaron Carass, and Jerry L. Prince

   In Proceedings of Medical Imaging 2019: Image Processing, Apr 2019

   Abs DOI

   Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled—i.e., normalized—both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.