Query: MRI

title: Management of Unilateral Axillary Lymphadenopathy Detected on Breast MRI in the Era of Coronavirus Disease (COVID 19) Vaccination.
abstract: Vaccination is an established but uncommon cause of unilateral axillary lymphadenopathy. Early clinical experience with coronavirus disease (COVID 19) vaccination suggests that the approved COVID 19 vaccines cause a notably higher incidence of axillary lymphadenopathy on breast MRI compared to other vaccines. Guidelines are needed to appropriately manage MRI detected unilateral axillary lymphadenopathy in the era of COVID 19 vaccination and to avoid biopsies of benign reactive nodes. This article examines the available data on vaccine related lymphadenopathy and offers a basic strategy to assess axillary lymphadenopathy on MRI and to guide management. At our institution, we are adding questions regarding the date(s) and laterality of administration of COVID 19 vaccination to our intake form before all breast imaging examinations. We consider MRI detected isolated unilateral axillary lymphadenopathy ipsilateral to the vaccination arm to be most likely COVID 19 vaccine related if within four weeks of either dose. In these cases, we assess the lymphadenopathy as BI RADS 3 and recommend a follow up ultrasound be performed within 6 8 weeks after the second dose. These guidelines may be refined as we gain further data on the expected time course of axillary lymphadenopathy post COVID 19 vaccination. Until that time, this management pathway will help avoid unnecessary biopsies of benign vaccine related reactive lymphadenopathy.

title: Time Dependent Deep Image Prior for Dynamic MRI
abstract: We propose a novel unsupervised deep learning based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study of moving organs such as the heart. We introduce a generalized version of the deep image prior approach, which optimizes the weights of a reconstruction network to fit a sequence of sparsely acquired dynamic MRI measurements. Our method needs neither prior training nor additional data. In particular, for cardiac images, it does not require the marking of heartbeats or the reordering of spokes. The key ingredients of our method are threefold: 1) a fixed low dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in k space. Our method outperforms the state of the art methods quantitatively and qualitatively in both retrospective and real fetal cardiac datasets. To the best of our knowledge, this is the first unsupervised deep learning based method that can reconstruct the continuous variation of dynamic MRI sequences with high spatial resolution.

title: Gradient waveform design for tensor valued encoding in diffusion MRI
abstract: Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b tensor. The benefit of tensor valued diffusion encoding is that it unlocks the 'shape of the b tensor' as a new encoding dimension. By modulating the b tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.

title: Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks
abstract: Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12, 802 T1 weighted MRI images and a further 6, 885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors DBrainAge AgePredicted AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between DBrainAge and the image derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the DBrainAge from models such as this network were predictive of any health outcomes. Highlights Brain age is estimated using a 3D CNN from 12,802 full T1 weighted images. Regions used to drive predictions are different for linearly and nonlinearly registered data. Linear registrations utilise a greater diversity of biologically meaningful areas. Correlations with IDPs and non imaging variables are consistent with other publications. Excluding subjects with various health conditions had minimal impact on main correlations.

title: Scattered slice SHARD reconstruction for motion correction in multi shell diffusion MRI
abstract: Highlights Subject motion in dMRI leads to a set of scattered slices with unique contrast. We introduce a slice to volume reconstruction framework for multi shell HARDI data* Based on a data driven representation as spherical harmonics and radial decomposition (SHARD) The method is evaluated in test retest scans and in the neonatal dHCP cohort. Results show robust reconstruction in severely motion corrupted scans.

title: Uncertainty Quantification in Deep MRI Reconstruction
abstract: Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction. With this in mind, this study aims to quantify the uncertainty in image recovery with DL models. To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low quality) short scans with aliasing artifacts to the diagnostic quality ones. The VAE encodes the acquisition uncertainty in a latent code and naturally offers a posterior of the image from which one can generate pixel variance maps using Monte Carlo sampling. Accurately predicting risk requires knowledge of the bias as well, for which we leverage Stein's Unbiased Risk Estimator (SURE) as a proxy for mean squared error (MSE) A range of empirical experiments is performed for Knee MRI reconstruction under different training losses (adversarial and pixel wise) and unrolled recurrent network architectures. Our key observations indicate that: 1) adversarial losses introduce more uncertainty; and 2) recurrent unrolled nets reduce the prediction uncertainty and risk.

title: Multitask Learning for Estimating Multitype Cardiac Indices in MRI and CT Based on Adversarial Reverse Mapping
abstract: The estimation of multitype cardiac indices from cardiac magnetic resonance imaging (MRI) and computed tomography (CT) images attracts great attention because of its clinical potential for comprehensive function assessment. However, the most exiting model can only work in one imaging modality (MRI or CT) without transferable capability. In this article, we propose the multitask learning method with the reverse inferring for estimating multitype cardiac indices in MRI and CT. Different from the existing forward inferring methods, our method builds a reverse mapping network that maps the multitype cardiac indices to cardiac images. The task dependencies are then learned and shared to multitask learning networks using an adversarial training approach. Finally, we transfer the parameters learned from MRI to CT. A series of experiments were conducted in which we first optimized the performance of our framework via ten fold cross validation of over 2900 cardiac MRI images. Then, the fine tuned network was run on an independent data set with 2360 cardiac CT images. The results of all the experiments conducted on the proposed adversarial reverse mapping show excellent performance in estimating multitype cardiac indices.

title: MADGAN: unsupervised medical anomaly detection GAN using multiple adjacent brain MRI slice reconstruction
abstract: Unsupervised learning can discover various unseen abnormalities, relying on large scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a 2D/3D single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's disease (AD) Moreover, no study has shown how unsupervised anomaly detection is associated with either disease stages, various (i.e. more than two types of) diseases, or multi sequence magnetic resonance imaging (MRI) scans.  We propose unsupervised medical anomaly detection generative adversarial network (MADGAN) a novel two step method using GAN based multiple adjacent brain MRI slice reconstruction to detect brain anomalies at different stages on multi sequence structural MRI: (Reconstruction) Wasserstein loss with Gradient Penalty 100 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin} 69pt} \begin{document}\ell _1$\end{document}l1 loss trained on 3 healthy brain axial MRI slices to reconstruct the next 3 ones reconstructs unseen healthy/abnormal scans; (Diagnosis) Average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin} 69pt} \begin{document}\ell _2$\end{document}l2 loss per scan discriminates them, comparing the ground truth/reconstructed slices. For training, we use two different datasets composed of 1133 healthy T1 weighted (T1) and 135 healthy contrast enhanced T1 (T1c) brain MRI scans for detecting AD and brain metastases/various diseases, respectively. Our self attention MADGAN can detect AD on T1 scans at a very early stage, mild cognitive impairment (MCI) with area under the curve (AUC) 0.727, and AD at a late stage with AUC 0.894, while detecting brain metastases on T1c scans with AUC 0.921.  Similar to physicians' way of performing a diagnosis, using massive healthy training data, our first multiple MRI slice reconstruction approach, MADGAN, can reliably predict the next 3 slices from the previous 3 ones only for unseen healthy images. As the first unsupervised various disease diagnosis, MADGAN can reliably detect the accumulation of subtle anatomical anomalies and hyper intense enhancing lesions, such as (especially late stage) AD and brain metastases on multi sequence MRI scans.

title: QAIS DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched Filter Technique and Deep Spiking Neural Network
abstract: Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods' reliability is at a high level, and such systems are used directly. In recent decades, several methods of segmentation of various images, such as MRI, CT, and PET, have been proposed for brain tumors. Advanced brain tumor segmentation has been a challenging issue in the scientific community. The reason for this is the existence of various tumor dimensions with disproportionate boundaries in medical imaging. This research provides an optimized MRI segmentation method to diagnose tumors. It first offers a preprocessing approach to reduce noise with a new method called Quantum Matched Filter Technique (QMFT) Then, the deep spiking neural network (DSNN) is implemented for segmentation using the conditional random field structure. However, a new algorithm called the Quantum Artificial Immune System (QAIS) is used in its SoftMax layer due to its slowness and nonsegmentation and the identification of suitable features for selection and extraction. The proposed approach, called QAIS DSNN, has a high ability to segment and distinguish brain tumors from MRI images. The simulation results using the BraTS2018 dataset show that the accuracy of the proposed approach is 98.21% average error squared rate is 0.006, signal to noise ratio is 97.79 dB, and lesion structure criteria including the tumor nucleus are 80.15% The improved tumor is 74.50% and the entire tumor is 91.92% which shows a functional advantage over similar previous methods. Also, the execution time of this method is 2.58 seconds.

title: Long term evolution of multiple sclerosis iron rim lesions in 7 T MRI.
abstract: Recent data suggest that multiple sclerosis white matter lesions surrounded by a rim of iron containing microglia, termed iron rim lesions, signify patients with more severe disease course and a propensity to develop progressive multiple sclerosis. So far, however, little is known regarding the dynamics of iron rim lesions over long time follow up. In a prospective longitudinal cohort study in 33 patients (17 females; 30 relapsing remitting, three secondary progressive multiple sclerosis; median age 36.6 years (18.6 62.6) we characterized the evolution of iron rim lesions by MRI at 7 T with annual scanning. The longest follow up was 7 years in a subgroup of eight patients. Median and mean observation period were 1 (0 7) and 2.9 2.6) years, respectively. Images were acquired using a fluid attenuated inversion recovery sequence fused with iron sensitive MRI phase data, termed FLAIR SWI, as well as a magnetization prepared two rapid acquisition gradient echoes, termed MP2RAGE. Volumes and T1 relaxation times of lesions with and without iron rims were assessed by manual segmentation. The pathological substrates of periplaque signal changes outside the iron rims were corroborated by targeted histological analysis on 17 post mortem cases (10 females; two relapsing remitting, 13 secondary progressive and two primary progressive multiple sclerosis; median age 66 years (34 88) four of them with available post mortem 7 T MRI data. We observed 16 nascent iron rim lesions, which mainly formed in relapsing remitting multiple sclerosis. Iron rim lesion fraction was significantly higher in relapsing remitting than progressive disease (17.8 versus 7.2% P 0.001) In secondary progressive multiple sclerosis only, iron rim lesions showed significantly different volume dynamics (P 0.034) compared with non rim lesions, which significantly shrank with time in both relapsing remitting (P 0.001) and secondary progressive multiple sclerosis (P 0.004) The iron rims themselves gradually diminished with time (P 0.008) Compared with relapsing remitting multiple sclerosis, iron rim lesions in secondary progressive multiple sclerosis were significantly more destructive than non iron rim lesions (P 0.001) reflected by prolonged lesional T1 relaxation times and by progressively increasing changes ascribed to secondary axonal degeneration in the periplaque white matter. Our study for the first time shows that chronic active lesions in multiple sclerosis patients evolve over many years after their initial formation. The dynamics of iron rim lesions thus provide one explanation for progressive brain damage and disability accrual in patients. Their systematic recording might become useful as a tool for predicting disease progression and monitoring treatment in progressive multiple sclerosis.