94 research outputs found
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
Comparison of CT, PET, and PET/CT for Staging of Patients with Indolent Non-Hodgkin’s Lymphoma
The aim was to investigate the potential impact of positron emission tomography (PET)/computed tomography (CT) as compared to PET and CT on the staging of patients with indolent lymphoma.
PET/CTs from 45 patients with indolent lymphoma undergoing staging or restaging were studied. Clinical follow-up, additional imaging, and histology served as the gold standard.
PET/CT correctly diagnosed 92 nodal regions as positive for lymphomatous involvement and 458 as disease free vs 68 and 449 for PET and 64 and 459 for CT, respectively. The respective sensitivities, specificities, and accuracies were 99%, 100%, and 99.8% for PET/CT, 68%, 97.5%, and 92.2% for PET, and 70%, 100%, and 94.7% for CT. PET/CT performed significantly better than PET (p < 0.001 for sensitivity, specificity, and accuracy) and CT (p < 0.001 for sensitivity and accuracy). PET/CT also correctly identified significantly more extra-nodal lesions (22) than CT (14) and PET (nine).
PET/CT provides significantly more accurate information compared to PET and CT for the staging and re-staging of patients with indolent lymphoma
Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome
Cerebellar mutism syndrome, characterised by mutism, emotional lability and cerebellar motor signs, occurs in up to 39% of children following resection of medulloblastoma, the most common malignant posterior fossa tumour of childhood. Its pathophysiology remains unclear, but prior studies have implicated damage to the superior cerebellar peduncles. In this study, the objective was to conduct high-resolution spatial profilometry of the cerebellar peduncles and identify anatomic biomarkers of cerebellar mutism syndrome. In this retrospective study, twenty-eight children with medulloblastoma (mean age 8.8 ± 3.8 years) underwent diffusion MRI at four timepoints over one year. Forty-nine healthy children (9.0 ± 4.2 years), scanned at a single timepoint, served as age- and sex-matched controls. Automated Fibre Quantification was used to segment cerebellar peduncles and compute fractional anisotropy (FA) at 30 nodes along each tract. Thirteen patients developed cerebellar mutism syndrome. FA was significantly lower in the distal third of the left superior cerebellar peduncle pre-operatively in all patients compared to controls (FA in proximal third 0.228, middle and distal thirds 0.270, p = 0.01, Cohen's d = 0.927). Pre-operative differences in FA did not predict cerebellar mutism syndrome. However, post-operative reductions in FA were highly specific to the distal left superior cerebellar peduncle, and were most pronounced in children with cerebellar mutism syndrome compared to those without at the 1–4 month follow up (0.325 vs 0.512, p = 0.042, d = 1.36) and at the 1-year follow up (0.342, vs 0.484, p = 0.038, d = 1.12). High spatial resolution cerebellar profilometry indicated a site-specific alteration of the distal segment of the superior cerebellar peduncle seen in cerebellar mutism syndrome which may have important surgical implications in the treatment of these devastating tumours of childhood
Federated Learning on Heterogenous Data using Chest CT
Large data have accelerated advances in AI. While it is well known that
population differences from genetics, sex, race, diet, and various
environmental factors contribute significantly to disease, AI studies in
medicine have largely focused on locoregional patient cohorts with less diverse
data sources. Such limitation stems from barriers to large-scale data share in
medicine and ethical concerns over data privacy. Federated learning (FL) is one
potential pathway for AI development that enables learning across hospitals
without data share. In this study, we show the results of various FL strategies
on one of the largest and most diverse COVID-19 chest CT datasets: 21
participating hospitals across five continents that comprise >10,000 patients
with >1 million images. We present three techniques: Fed Averaging (FedAvg),
Incremental Institutional Learning (IIL), and Cyclical Incremental
Institutional Learning (CIIL). We also propose an FL strategy that leverages
synthetically generated data to overcome class imbalances and data size
disparities across centers. We show that FL can achieve comparable performance
to Centralized Data Sharing (CDS) while maintaining high performance across
sites with small, underrepresented data. We investigate the strengths and
weaknesses for all technical approaches on this heterogeneous dataset including
the robustness to non-Independent and identically distributed (non-IID)
diversity of data. We also describe the sources of data heterogeneity such as
age, sex, and site locations in the context of FL and show how even among the
correctly labeled populations, disparities can arise due to these biases
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT
The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID−) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis
Integrating neuroimaging biomarkers into the multicentre, high-dose erythropoietin for asphyxia and encephalopathy (HEAL) trial: rationale, protocol and harmonisation
Introduction: MRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms.
Methods and analysis: Neonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation-thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans.
Ethics and dissemination: IRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor
Comparison of CT, PET, and PET/CT for Staging of Patients with Indolent Non-Hodgkin’s Lymphoma
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