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Article ; Online: Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI.

Carass, Aaron / Greenman, Danielle / Dewey, Blake E / Calabresi, Peter A / Prince, Jerry L / Pham, Dzung L

Neuroimage. Reports

2024 Volume 4, Issue 1

Abstract: Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) ...

Abstract	Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
Language	English
Publishing date	2024-02-02
Publishing country	Netherlands
Document type	Journal Article
ISSN	2666-9560
ISSN (online)	2666-9560
DOI	10.1016/j.ynirp.2024.100195
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Reproducibility evaluation of the effects of MRI defacing on brain segmentation.

Gao, Chenyu / Landman, Bennett A / Prince, Jerry L / Carass, Aaron

Journal of medical imaging (Bellingham, Wash.)

2023 Volume 10, Issue 6, Page(s) 64001

Abstract: Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number ... ...

Abstract	Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last 5 years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in the previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach: We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results: Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms, such as Quickshear, MRI_Deface, and FSL_deface. Compared to FreeSurfer, SLANT is less affected by defacing. On outputs that pass the quality check, the effects of defacing are less pronounced than those of rescanning, as measured by the Dice similarity coefficient. Conclusions: The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it is encouraged to include multiple brain segmentation pipelines.
Language	English
Publishing date	2023-11-08
Publishing country	United States
Document type	Journal Article
ISSN	2329-4302
ISSN	2329-4302
DOI	10.1117/1.JMI.10.6.064001
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Article: A reproducibility evaluation of the effects of MRI defacing on brain segmentation.

Gao, Chenyu / Landman, Bennett A / Prince, Jerry L / Carass, Aaron

medRxiv : the preprint server for health sciences

2023

Abstract	Purpose: Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. While some qualities of these defacing algorithms, such as patient identifiability, have been explored in previous works, the potential impact of defacing on neuroimage processing has yet to be explored. Approach: We qualitatively evaluate eight MR defacing algorithms on 179 subjects from the OASIS-3 cohort and the 21 subjects from the Kirby-21 dataset. We also evaluate the effects of defacing on two neuroimaging pipelines-SLANT and FreeSurfer-by comparing the segmentation consistency between the original and defaced images. Results: Defacing can alter brain segmentation and even lead to catastrophic failures, which are more frequent with some algorithms such as Conclusions: The effects of defacing are noticeable and should not be disregarded. Extra attention, in particular, should be paid to the possibility of catastrophic failures. It is crucial to adopt a robust defacing algorithm and perform a thorough quality check before releasing defaced datasets. To improve the reliability of analysis in scenarios involving defaced MRIs, it's encouraged to include multiple brain segmentation pipelines.
Language	English
Publishing date	2023-05-21
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.05.15.23289995
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Article ; Online: ESPRESO: An algorithm to estimate the slice profile of a single magnetic resonance image.

Han, Shuo / Remedios, Samuel W / Schär, Michael / Carass, Aaron / Prince, Jerry L

Magnetic resonance imaging

2023 Volume 98, Page(s) 155–163

Abstract: To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in- ... ...

Abstract	To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance. In this work, we present an algorithm to estimate the slice profile of a 2D multi-slice acquisition given only its own image volume without any external training data. We assume that an anatomical image is isotropic in the sense that, after accounting for a correctly estimated slice profile, the image patches along different orientations have the same probability distribution. Our proposed algorithm uses a modified generative adversarial network (GAN) where the generator network estimates the slice profile to reduce the resolution of the in-plane direction, and the discriminator network determines whether a direction is generated or real low resolution. The proposed algorithm, ESPRESO, which stands for "estimating the slice profile for resolution enhancement of a single image only", was tested with a state-of-the-art internally supervised SR algorithm. Specifically, ESPRESO is used to create training data for this SR algorithm, and results show improvements when ESPRESO is used over commonly-used PSFs.
MeSH term(s)	Magnetic Resonance Imaging/methods ; Algorithms ; Phantoms, Imaging ; Radionuclide Imaging ; Image Processing, Computer-Assisted
Language	English
Publishing date	2023-01-24
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	604885-7
ISSN	1873-5894 ; 0730-725X
ISSN (online)	1873-5894
ISSN	0730-725X
DOI	10.1016/j.mri.2023.01.012
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Article: Longitudinal deep network for consistent OCT layer segmentation.

He, Yufan / Carass, Aaron / Liu, Yihao / Calabresi, Peter A / Saidha, Shiv / Prince, Jerry L

Biomedical optics express

2023 Volume 14, Issue 5, Page(s) 1874–1893

Abstract: Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. ... ...

Abstract	Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.
Language	English
Publishing date	2023-04-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.487518
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Book ; Online: DrDisco

Bian, Zhangxing / Shao, Muhan / Carass, Aaron / Prince, Jerry L.

Deep Registration for Distortion Correction of Diffusion MRI with single phase-encoding

2023

Abstract: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geometric ...

Abstract	Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive way of imaging white matter tracts in the human brain. DW-MRIs are usually acquired using echo-planar imaging (EPI) with high gradient fields, which could introduce severe geometric distortions that interfere with further analyses. Most tools for correcting distortion require two minimally weighted DW-MRI images (B0) acquired with different phase-encoding directions, and they can take hours to process per subject. Since a great amount of diffusion data are only acquired with a single phase-encoding direction, the application of existing approaches is limited. We propose a deep learning-based registration approach to correct distortion using only the B0 acquired from a single phase-encoding direction. Specifically, we register undistorted T1-weighted images and distorted B0 to remove the distortion through a deep learning model. We apply a differentiable mutual information loss during training to improve inter-modality alignment. Experiments on the Human Connectome Project dataset show the proposed method outperforms SyN and VoxelMorph on several metrics, and only takes a few seconds to process one subject. Comment: To appear in Medical Imaging: Image Processing 2023
Keywords	Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2023-04-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Coordinate Translator for Learning Deformable Medical Image Registration.

Liu, Yihao / Zuo, Lianrui / Han, Shuo / Xue, Yuan / Prince, Jerry L / Carass, Aaron

Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings

2022 Volume 13594, Page(s) 98–109

Abstract: The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to ...

Abstract	The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator's with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively.
Language	English
Publishing date	2022-10-12
Publishing country	Switzerland
Document type	Journal Article
DOI	10.1007/978-3-031-18814-5_10
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Article: Evaluating the impact of MR image harmonization on thalamus deep network segmentation.

Shao, Muhan / Zuo, Lianrui / Carass, Aaron / Zhuo, Jiachen / Gullapalli, Rao P / Prince, Jerry L

Proceedings of SPIE--the International Society for Optical Engineering

2022 Volume 12032

Abstract: Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain's anatomical structures that may occur due to normal ... ...

Abstract	Medical image segmentation is one of the core tasks of medical image analysis. Automatic segmentation of brain magnetic resonance images (MRIs) can be used to visualize and track changes of the brain's anatomical structures that may occur due to normal aging or disease. Machine learning techniques are widely used in automatic structure segmentation. However, the contrast variation between the training and testing data makes it difficult for segmentation algorithms to generate consistent results. To address this problem, an image-to-image translation technique called MR image harmonization can be used to match the contrast between different data sets. It is important for the harmonization to transform image intensity while maintaining the underlying anatomy. In this paper, we present a 3D U-Net algorithm to segment the thalamus from multiple MR image modalities and investigate the impact of harmonization on the segmentation algorithm. Manual delineations of thalamic nuclei on two data sets are available. However, we aim to analyze the thalamus in another large data set where ground truth labels are lacking. We trained two segmentation networks, one with unharmonized images and the other with harmonized images, on one data set with manual labels, and compared their performances on the other data set with manual labels. These two data groups were diagnosed with two brain disorders and were acquired with similar imaging protocols. The harmonization target is the large data set without manual labels, which also has a different imaging protocol. The networks trained on unharmonized and harmonized data showed no significant difference when evaluating on the other data set; demonstrating that image harmonization can maintain the anatomy and does not affect the segmentation task. The two networks were evaluated on the harmonization target data set and the network trained on harmonized data showed significant improvement over the network trained on unharmonized data. Therefore, the network trained on harmonized data provides the potential to process large amounts of data from other sites, even in the absence of site-specific training data.
Language	English
Publishing date	2022-04-04
Publishing country	United States
Document type	Journal Article
ISSN	0277-786X
ISSN	0277-786X
DOI	10.1117/12.2613159
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation.

Wei, Shuwen / Liu, Yihao / Bian, Zhangxing / Wang, Yuli / Zuo, Lianrui / Calabresi, Peter A / Saidha, Shiv / Prince, Jerry L / Carass, Aaron

Ophthalmic medical image analysis : 10th International Workshop, OMIA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings. OMIA (Workshop) (10th : 2023 : Vancouver, B.C. ; Online)

2023 Volume 14096, Page(s) 42–51

Abstract: Optical coherence tomography (OCT) is a valuable imaging technique in ophthalmology, providing high-resolution, cross-sectional images of the retina for early detection and monitoring of various retinal and neurological diseases. However, discrepancies ... ...

Abstract	Optical coherence tomography (OCT) is a valuable imaging technique in ophthalmology, providing high-resolution, cross-sectional images of the retina for early detection and monitoring of various retinal and neurological diseases. However, discrepancies in retinal layer thickness measurements among different OCT devices pose challenges for data comparison and interpretation, particularly in longitudinal analyses. This work introduces the idea of a recurrent self fusion (RSF) algorithm to address this issue. Our RSF algorithm, built upon the self fusion methodology, iteratively denoises retinal OCT images. A deep learning-based retinal OCT segmentation algorithm is employed for downstream analyses. A large dataset of paired OCT scans acquired on both a Spectralis and Cirrus OCT device are used for validation. The results demonstrate that the RSF algorithm effectively reduces speckle contrast and enhances the consistency of retinal OCT segmentation.
Language	English
Publishing date	2023-09-16
Publishing country	Switzerland
Document type	Journal Article
DOI	10.1007/978-3-031-44013-7_5
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Article: AUTOMATED VENTRICLE PARCELLATION AND EVAN'S RATIO COMPUTATION IN PRE- AND POST-SURGICAL VENTRICULOMEGALY.

Wang, Yuli / Feng, Anqi / Xue, Yuan / Zuo, Lianrui / Liu, Yihao / Blitz, Ari M / Luciano, Mark G / Carass, Aaron / Prince, Jerry L

Proceedings. IEEE International Symposium on Biomedical Imaging

2023 Volume 2023

Abstract: Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized ... ...

Abstract	Normal pressure hydrocephalus (NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images (MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI. Because shunt surgery is often used to treat NPH, it is necessary that this process be robust to image artifacts caused by the shunt and related implants. In this paper, we propose a 3D regions-of-interest aware (ROI-aware) network for segmenting the ventricles. The method achieves state-of-the-art performance on both pre-surgery MRIs and post-surgery MRIs with artifacts. Based on our segmentation results, we also describe an automated approach to compute ER from these results. Experimental results on multiple datasets demonstrate the potential of the proposed method to assist clinicians in the diagnosis and management of NPH.
Language	English
Publishing date	2023-09-01
Publishing country	United States
Document type	Journal Article
ISSN	1945-7928
ISSN	1945-7928
DOI	10.1109/isbi53787.2023.10230729
Database	MEDical Literature Analysis and Retrieval System OnLINE

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