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1.
J Cereb Blood Flow Metab ; : 271678X20918816, 2020 May 19.
Artigo em Inglês | MEDLINE | ID: mdl-32423329

RESUMO

We previously demonstrated that in the DEFUSE 3 trial, the union of the baseline core and the 24-h Tmax > 6 s perfusion lesion predicts the infarct volume at 24 h. Presently, we assessed if collateral robustness measured by the hypoperfusion intensity ratio (HIR) and cerebral blood volume (CBV) index accounts for the variance in these predictions. DEFUSE 3 patients underwent MRI/CT perfusion imaging at baseline and 24 h post-randomization. We compared baseline and follow-up HIR and CBV index across subgroups stratified by differences between predicted and observed 24-h infarct volumes. Of 123 eligible patients, 34 with 24-h infarcts larger than predicted had less favorable collaterals at baseline (HIR 0.43 vs. 0.32, p = 0.006; CBV Index 0.78 vs. 0.85, p = 0.001) and 24 h (HIR 0.56 vs. 0.07, p = 0.004; CBV Index 0.47 vs. 0.73, p = 0.006) compared to 71 patients with more accurate infarct volume prediction. Eighteen patients with 24-h infarcts smaller than predicted had similar baseline collateral scores but more favorable 24-h CBV indices (0.81 vs. 0.73, p = 0.040). Overall, patients with 24-h infarcts larger than predicted had evidence of less favorable baseline collaterals that fail within 24 h, while patients with 24-h infarcts smaller than predicted typically had favorable collaterals that persisted for 24 h.

2.
Neuroimage ; : 116886, 2020 May 07.
Artigo em Inglês | MEDLINE | ID: mdl-32389728

RESUMO

INTRODUCTION: Geometric distortions along the phase encoding direction caused by off-resonant spins are a major issue in EPI based functional and diffusion imaging. The widely used blip up/down approach estimates the underlying distortion field from a pair of images with inverted phase encoding direction. Typically, iterative methods are used to find a solution to the ill-posed problem of finding the displacement field that maps up/down acquisitions onto each other. Here, we explore the use of a deep convolutional network to estimate the displacement map from a pair of input images. METHODS: We trained a deep convolutional U-net architecture that was previously used to estimate optic flow between moving images to learn to predict the distortion map from an input pair of distorted EPI acquisitions. During the training step, the network minimizes a loss function (similarity metric) that is calculated from corrected input image pairs. This approach does not require the explicit knowledge of the ground truth distortion map, which is difficult to get for real life data. RESULTS: We used data from a total of Ntrain=22 healthy subjects to train our network. A separate dataset of Ntest=12 patients including some with abnormal findings and unseen acquisition modes, e.g. LR-encoding, coronal orientation) was reserved for testing and evaluation purposes. We compared our results to FSL's topup function with default parameters that served as the gold standard. We found that our approach results in a correction accuracy that is virtually identical to the optimum found by an iterative search, but with reduced computational time. CONCLUSION: By using a deep convolutional network, we can reduce the processing time to a few seconds per volume, which is significantly faster than iterative approaches like FSL's topup which takes around 10min on the same machine (but using only 1 CPU). This facilitates the use of a blip up/down scheme for all diffusion-weighted acquisitions and potential real-time EPI distortion correction without sacrificing accuracy.

4.
Artigo em Inglês | MEDLINE | ID: mdl-32286966

RESUMO

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.

5.
JAMA Netw Open ; 3(3): e200772, 2020 Mar 02.
Artigo em Inglês | MEDLINE | ID: mdl-32163165

RESUMO

Importance: Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke. Objectives: To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods. Design, Setting, and Participants: In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019. Main Outcomes and Measures: Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion. Results: Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance. Conclusions and Relevance: The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

6.
J Magn Reson Imaging ; 51(3): 657-674, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31566852

RESUMO

The use of magnetic resonance imaging (MRI) is increasing globally, and MRI safety issues regarding medical devices, which are constantly being developed or upgraded, represent an ongoing challenge for MRI personnel. To assist the MRI community, a panel of 10 radiologists with expertise in MRI safety from nine high-volume academic centers formed, with the objective of providing clarity on some of the MRI safety issues for the 10 most frequently questioned devices. Ten device categories were identified. The panel reviewed the literature, including key MRI safety issues regarding screening and adverse event reports, in addition to the manufacturer's Instructions For Use. Using a Delphi-inspired method, 36 practical recommendations were generated with 100% consensus that can aid the clinical MRI community. Level of Evidence: 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:657-674.

7.
J Magn Reson Imaging ; 51(3): 734-747, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31294898

RESUMO

BACKGROUND: To maintain cerebral blood flow (CBF), cerebral blood vessels dilate and contract in response to blood supply through cerebrovascular reactivity (CR). PURPOSE: Cardiovascular (CV) disease is associated with increased stroke risk, but which risk factors specifically impact CR is unknown. STUDY TYPE: Prospective longitudinal. SUBJECTS: Fifty-three subjects undergoing carotid endarterectomy or stenting. FIELD STRENGTH/SEQUENCE: 3T, 3D pseudo-continuous arterial spin labeling (PCASL) ASL, and T1 3D fast spoiled gradient echo (FSPGR). ASSESSMENT: We evaluated group differences in CBF changes for multiple cardiovascular risk factors in patients undergoing carotid revascularization surgery. STATISTICAL TESTS: PRE (baseline), POST (48-hour postop), and 6MO (6 months postop) whole-brain CBF measurements, as 129 CBF maps from 53 subjects were modeled as within-subject analysis of variance (ANOVA). To identify CV risk factors associated with CBF change, the CBF change from PRE to POST, POST to 6MO, and PRE to 6MO were modeled as multiple linear regression with each CV risk factor as an independent variable. Statistical models were performed controlling for age on a voxel-by-voxel basis using SPM8. Significant clusters were reported if familywise error (FWE)-corrected cluster-level was P < 0.05, while the voxel-level significance threshold was set for P < 0.001. RESULTS: The entire group showed significant (cluster-level P < 0.001) CBF increase from PRE to POST, decrease from POST to 6MO, and no significant difference (all voxels with P > 0.001) from PRE to 6MO. Of multiple CV risk factors evaluated, only elevated systolic blood pressure (SBP, P = 0.001), chronic renal insufficiency (CRI, P = 0.026), and history of prior stroke (CVA, P < 0.001) predicted lower increases in CBF PRE to POST. Over POST to 6MO, obesity predicted lower (P > 0.001) and cholesterol greater CBF decrease (P > 0.001). DATA CONCLUSION: The CV risk factors of higher SBP, CRI, CVA, BMI, and cholesterol may indicate altered CR, and may warrant different stroke risk mitigation and special consideration for CBF change evaluation. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2020;51:734-747.

8.
J Cereb Blood Flow Metab ; 40(3): 539-551, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-30732551

RESUMO

Recent clinical trials of new revascularization therapies in acute ischemic stroke have highlighted the importance of physiological imaging to identify optimal treatments for patients. Oxygen extraction fraction (OEF) is a hallmark of at-risk tissue in stroke, and can be quantified from the susceptibility effect of deoxyhemoglobin molecules in venous blood on MRI phase scans. We measured OEF within cerebral veins using advanced quantitative susceptibility mapping (QSM) MRI reconstructions in 20 acute stroke patients. Absolute OEF was elevated in the affected (29.3 ± 3.4%) versus the contralateral hemisphere (25.5 ± 3.1%) of patients with large diffusion-perfusion lesion mismatch (P = 0.032). In these patients, OEF negatively correlated with relative CBF measured by dynamic susceptibility contrast MRI (P = 0.004), suggesting compensation for reduced flow. Patients with perfusion-diffusion match or no hypo-perfusion showed less OEF difference between hemispheres. Nine patients received longitudinal assessment and showed OEF ratio (affected to contralateral) of 1.2 ± 0.1 at baseline that normalized (decreased) to 1.0 ± 0.1 at follow-up three days later (P = 0.03). Our feasibility study demonstrates that QSM MRI can non-invasively quantify OEF in stroke patients, relates to perfusion status, and is sensitive to OEF changes over time. Clinical trial registration: Longitudinal MRI examinations of patients with brain ischemia and blood brain barrier permeability; clinicaltrials.org : NCT02077582.

9.
Stroke ; 51(2): 489-497, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31884904

RESUMO

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

10.
J Magn Reson Imaging ; 51(1): 183-194, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31044459

RESUMO

BACKGROUND: H2 15 O-positron emission tomography (PET) is considered the reference standard for absolute cerebral blood flow (CBF). However, this technique requires an arterial input function measured through continuous sampling of arterial blood, which is invasive and has limitations with tracer delay and dispersion. PURPOSE: To demonstrate a new noninvasive method to quantify absolute CBF with a PET/MRI hybrid scanner. This blood-free approach, called PC-PET, takes the spatial CBF distribution from a static H2 15 O-PET scan, and scales it to the whole-brain average CBF value measured by simultaneous phase-contrast MRI. STUDY TYPE: Observational. SUBJECTS: Twelve healthy controls (HC) and 13 patients with Moyamoya disease (MM) as a model of chronic ischemic disease. FIELD STRENGTH/SEQUENCES: 3T/2D cardiac-gated phase-contrast MRI and H2 15 O-PET. ASSESSMENT: PC-PET CBF values from whole brain (WB), gray matter (GM), and white matter (WM) in HCs were compared with literature values since 2000. CBF and cerebrovascular reactivity (CVR), which is defined as the percent CBF change between baseline and post-acetazolamide (vasodilator) scans, were measured by PC-PET in MM patients and HCs within cortical regions corresponding to major vascular territories. Statistical Tests: Linear, mixed effects models were created to compare CBF and CVR, respectively, between patients and controls, and between different degrees of stenosis. RESULTS: The mean CBF values in WB, GM, and WM in HC were 42 ± 7 ml/100 g/min, 50 ± 7 ml/100 g/min, and 23 ± 3 ml/100 g/min, respectively, which agree well with literature values. Compared with normal regions (57 ± 23%), patients showed significantly decreased CVR in areas with mild/moderate stenosis (47 ± 17%, P = 0.011) and in severe/occluded areas (40 ± 16%, P = 0.016). Data Conclusion: PC-PET identifies differences in cerebrovascular reactivity between healthy controls and cerebrovascular patients. PC-PET is suitable for CBF measurement when arterial blood sampling is not accessible, and warrants comparison to fully quantitative H2 15 O-PET in future studies. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;51:183-194.

11.
J Magn Reson Imaging ; 51(1): 175-182, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31050074

RESUMO

BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging. PURPOSE: To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep-learning approach based on a fully convolution neural network (CNN). STUDY TYPE: Retrospective. POPULATION: In all, 156 patients with brain metastases from several primary cancers were included. FIELD STRENGTH: 1.5T and 3T. [Correction added on May 24, 2019, after first online publication: In the preceding sentence, the first field strength listed was corrected.] SEQUENCE: Pretherapy MR images included pre- and postgadolinium T1 -weighted 3D fast spin echo (CUBE), postgadolinium T1 -weighted 3D axial IR-prepped FSPGR (BRAVO), and 3D CUBE fluid attenuated inversion recovery (FLAIR). ASSESSMENT: The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1-3), multiple (4-10), and many (>10) lesions. STATISTICAL TESTS: Network performance was evaluated using precision, recall, Dice/F1 score, and receiver operating characteristic (ROC) curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per-metastasis basis. The Wilcoxon rank sum test was used to test the differences between patient subgroups. RESULTS: The area under the ROC curve (AUC), averaged across all patients, was 0.98 ± 0.04. The AUC in the subgroups was 0.99 ± 0.01, 0.97 ± 0.05, and 0.97 ± 0.03 for patients having 1-3, 4-10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice score were 0.79 ± 0.20, 0.53 ± 0.22, and 0.79 ± 0.12, respectively. At the same probability threshold, the network showed an average false-positive rate of 8.3/patient (no lesion-size limit) and 3.4/patient (10 mm3 lesion size limit). DATA CONCLUSION: A deep-learning approach using multisequence MRI can automatically detect and segment brain metastases with high accuracy. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:175-182.

13.
Stroke ; 50(12): 3408-3415, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31619150

RESUMO

Background and Purpose- Imaging is frequently used to select acute stroke patients for intra-arterial therapy. Quantitative cerebral blood flow can be measured noninvasively with arterial spin labeling magnetic resonance imaging. Cerebral blood flow levels in the contralateral (unaffected) hemisphere may affect capacity for collateral flow and patient outcome. The goal of this study was to determine whether higher contralateral cerebral blood flow (cCBF) in acute stroke identifies patients with better 90-day functional outcome. Methods- Patients were part of the prospective, multicenter iCAS study (Imaging Collaterals in Acute Stroke) between 2013 and 2017. Consecutive patients were enrolled after being diagnosed with anterior circulation acute ischemic stroke. Inclusion criteria were ischemic anterior circulation stroke, baseline National Institutes of Health Stroke Scale score ≥1, prestroke modified Rankin Scale score ≤2, onset-to-imaging time <24 hours, with imaging including diffusion-weighted imaging and arterial spin labeling. Patients were dichotomized into high and low cCBF groups based on median cCBF. Outcomes were assessed by day-1 and day-5 National Institutes of Health Stroke Scale; and day-30 and day-90 modified Rankin Scale. Multivariable logistic regression was used to test whether cCBF predicted good neurological outcome (modified Rankin Scale score, 0-2) at 90 days. Results- Seventy-seven patients (41 women) met the inclusion criteria with median (interquartile range) age of 66 (55-76) yrs, onset-to-imaging time of 4.8 (3.6-7.7) hours, and baseline National Institutes of Health Stroke Scale score of 13 (9-20). Median cCBF was 38.9 (31.2-44.5) mL per 100 g/min. Higher cCBF predicted good outcome at day 90 (odds ratio, 4.6 [95% CI, 1.4-14.7]; P=0.01), after controlling for baseline National Institutes of Health Stroke Scale, diffusion-weighted imaging lesion volume, and intra-arterial therapy. Conclusions- Higher quantitative cCBF at baseline is a significant predictor of good neurological outcome at day 90. cCBF levels may inform decisions regarding stroke triage, treatment of acute stroke, and general outcome prognosis. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT02225730.

14.
Front Neurol ; 10: 869, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31474928

RESUMO

Many clinical applications based on deep learning and pertaining to radiology have been proposed and studied in radiology for classification, risk assessment, segmentation tasks, diagnosis, prognosis, and even prediction of therapy responses. There are many other innovative applications of AI in various technical aspects of medical imaging, particularly applied to the acquisition of images, ranging from removing image artifacts, normalizing/harmonizing images, improving image quality, lowering radiation and contrast dose, and shortening the duration of imaging studies. This article will address this topic and will seek to present an overview of deep learning applied to neuroimaging techniques.

15.
IEEE Trans Radiat Plasma Med Sci ; 3(4): 498-503, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31396580

RESUMO

A significant challenge during high-resolution PET brain imaging on PET/MR scanners is patient head motion. This challenge is particularly significant for clinical patient populations who struggle to remain motionless in the scanner for long periods of time. Head motion also affects the MR scan data. An optical motion tracking technique, which has already been demonstrated to perform MR motion correction during acquisition, is used with a list-mode PET reconstruction algorithm to correct the motion for each recorded event and produce a corrected reconstruction. The technique is demonstrated on real Alzheimer's disease patient data for the GE SIGNA PET/MR scanner.

16.
Eur J Nucl Med Mol Imaging ; 46(13): 2700-2707, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31254036

RESUMO

INTRODUCTION: Recently there have been significant advances in the field of machine learning and artificial intelligence (AI) centered around imaging-based applications such as computer vision. In particular, the tremendous power of deep learning algorithms, primarily based on convolutional neural network strategies, is becoming increasingly apparent and has already had direct impact on the fields of radiology and nuclear medicine. While most early applications of computer vision to radiological imaging have focused on classification of images into disease categories, it is also possible to use these methods to improve image quality. Hybrid imaging approaches, such as PET/MRI and PET/CT, are ideal for applying these methods. METHODS: This review will give an overview of the application of AI to improve image quality for PET imaging directly and how the additional use of anatomic information from CT and MRI can lead to further benefits. For PET, these performance gains can be used to shorten imaging scan times, with improvement in patient comfort and motion artifacts, or to push towards lower radiotracer doses. It also opens the possibilities for dual tracer studies, more frequent follow-up examinations, and new imaging indications. How to assess quality and the potential effects of bias in training and testing sets will be discussed. CONCLUSION: Harnessing the power of these new technologies to extract maximal information from hybrid PET imaging will open up new vistas for both research and clinical applications with associated benefits in patient care.

17.
Med Phys ; 46(8): 3555-3564, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31131901

RESUMO

PURPOSE: Our goal was to use a generative adversarial network (GAN) with feature matching and task-specific perceptual loss to synthesize standard-dose amyloid Positron emission tomography (PET) images of high quality and including accurate pathological features from ultra-low-dose PET images only. METHODS: Forty PET datasets from 39 participants were acquired with a simultaneous PET/MRI scanner following injection of 330 ± 30 MBq of the amyloid radiotracer 18F-florbetaben. The raw list-mode PET data were reconstructed as the standard-dose ground truth and were randomly undersampled by a factor of 100 to reconstruct 1% low-dose PET scans. A 2D encoder-decoder network was implemented as the generator to synthesize a standard-dose image and a discriminator was used to evaluate them. The two networks contested with each other to achieve high-visual quality PET from the ultra-low-dose PET. Multi-slice inputs were used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce hallucinated structures. Task-specific perceptual loss was designed to maintain the correct pathological features. The image quality was evaluated by peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) metrics with and without each of these modules. Two expert radiologists were asked to score image quality on a 5-point scale and identified the amyloid status (positive or negative). RESULTS: With only low-dose PET as input, the proposed method significantly outperformed Chen et al.'s method (Chen et al. Radiology. 2018;290:649-656) (which shows the best performance in this task) with the same input (PET-only model) by 1.87 dB in PSNR, 2.04% in SSIM, and 24.75% in RMSE. It also achieved comparable results to Chen et al.'s method which used additional magnetic resonance imaging (MRI) inputs (PET-MR model). Experts' reading results showed that the proposed method could achieve better overall image quality and maintain better pathological features indicating amyloid status than both PET-only and PET-MR models proposed by Chen et al. CONCLUSION: Standard-dose amyloid PET images can be synthesized from ultra-low-dose images using GAN. Applying adversarial learning, feature matching, and task-specific perceptual loss are essential to ensure image quality and the preservation of pathological features.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Tomografia por Emissão de Pósitrons , Doses de Radiação , Razão Sinal-Ruído
18.
J Nucl Med ; 60(10): 1340-1346, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31123099

RESUMO

Since the introduction of simultaneous PET/MRI in 2011, there have been significant advancements. In this review, we highlight several technical advancements that have been made primarily in attenuation and motion correction and discuss the status of multiple clinical applications using PET/MRI. This review is based on the experience at the first PET/MRI conference cosponsored by the International Society for Magnetic Resonance in Medicine and the Society of Nuclear Medicine and Molecular Imaging.

19.
Stroke ; 50(3): 626-631, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30727840

RESUMO

Background and Purpose- Accurate prediction of the subsequent infarct volume early after stroke onset helps determine appropriate interventions and prognosis. In the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke), we evaluated the accuracy of baseline ischemic core and hypoperfusion volumes for predicting infarct volume 24 hours after randomization to endovascular thrombectomy versus medical management. We also assessed if the union of baseline ischemic core and the volume of persistent hypoperfusion at 24 hours after randomization predicts infarct volume. Methods- Patients in DEFUSE 3 with computed tomography perfusion imaging or magnetic resonance diffusion weighted imaging/perfusion imaging acquired at baseline and at 24 hours after randomization were included. Ischemic core and Tmax >6s hypoperfusion volumes at baseline and follow-up were calculated using RAPID software and compared with the infarct volumes obtained 24 hours after randomization. Patients were stratified by reperfusion status for analyses. Results- Of 125 eligible patients, 59 patients with >90% reperfusion had a strong correlation between baseline ischemic core volume and infarct volume 24 hours postrandomization ( r=0.83; P<0.0001), and 14 patients with <10% reperfusion had a strong correlation between baseline Tmax >6s volume and infarct volume 24 hours postrandomization ( r=0.77; P<0.001). In the 52 patients with 10% to 90% reperfusion, as well as in all 125 patients, the union of the baseline ischemic core and the follow-up Tmax >6s perfusion volume was highly correlated with infarct volume 24 hours postrandomization (for N=125; r=0.83; P<0.0001), with a median absolute difference of 21.3 mL between observed and predicted infarct volumes. Conclusions- The union of the irreversibly injured ischemic core and persistently hypoperfused tissue volumes, as identified by computed tomography perfusion or magnetic resonance diffusion weighted imaging/perfusion, predicted infarct volume at 24 hours after randomization in DEFUSE 3 patients. Clinical Trial Registration- URL: https://www.clinicaltrials.gov . Unique identifier: NCT02586415.


Assuntos
Isquemia Encefálica/diagnóstico por imagem , Infarto Cerebral/diagnóstico por imagem , Transtornos Cerebrovasculares/diagnóstico por imagem , Acidente Vascular Cerebral/diagnóstico por imagem , Idoso , Idoso de 80 Anos ou mais , Isquemia Encefálica/cirurgia , Transtornos Cerebrovasculares/cirurgia , Imagem de Difusão por Ressonância Magnética , Feminino , Fibrinolíticos/uso terapêutico , Humanos , Masculino , Pessoa de Meia-Idade , Imagem de Perfusão , Valor Preditivo dos Testes , Prognóstico , Reperfusão , Acidente Vascular Cerebral/tratamento farmacológico , Acidente Vascular Cerebral/cirurgia , Trombectomia , Ativador de Plasminogênio Tecidual/uso terapêutico , Tomografia Computadorizada por Raios X
20.
Stroke ; 50(2): 373-380, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30636572

RESUMO

Background and Purpose- Noninvasive imaging of brain perfusion has the potential to elucidate pathophysiological mechanisms underlying Moyamoya disease and enable clinical imaging of cerebral blood flow (CBF) to select revascularization therapies for patients. We used hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI) technology to characterize the distribution of hypoperfusion in Moyamoya disease and its relationship to vessel stenosis severity, through comparisons with a normative perfusion database of healthy controls. Methods- To image CBF, we acquired [15O]-water PET as a reference and simultaneously acquired arterial spin labeling (ASL) MRI scans in 20 Moyamoya patients and 15 age-matched, healthy controls on a PET/MRI scanner. The ASL MRI scans included a standard single-delay ASL scan with postlabel delay of 2.0 s and a multidelay scan with 5 postlabel delays (0.7-3.0s) to estimate and account for arterial transit time in CBF quantification. The percent volume of hypoperfusion in patients (determined as the fifth percentile of CBF values in the healthy control database) was the outcome measure in a logistic regression model that included stenosis grade and location. Results- Logistic regression showed that anterior ( P<0.0001) and middle cerebral artery territory regions ( P=0.003) in Moyamoya patients were susceptible to hypoperfusion, whereas posterior regions were not. Cortical regions supplied by arteries with stenosis on MR angiography showed more hypoperfusion than normal arteries ( P=0.001), but the extent of hypoperfusion was not different between mild-moderate versus severe stenosis. Multidelay ASL did not perform differently from [15O]-water PET in detecting perfusion abnormalities, but standard ASL overestimated the extent of hypoperfusion in patients ( P=0.003). Conclusions- This simultaneous PET/MRI study supports the use of multidelay ASL MRI in clinical evaluation of Moyamoya disease in settings where nuclear medicine imaging is not available and application of a normative perfusion database to automatically identify abnormal CBF in patients.


Assuntos
Bases de Dados Factuais , Imagem por Ressonância Magnética , Artéria Cerebral Média , Doença de Moyamoya , Tomografia por Emissão de Pósitrons , Adolescente , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Artéria Cerebral Média/diagnóstico por imagem , Artéria Cerebral Média/fisiopatologia , Doença de Moyamoya/diagnóstico por imagem , Doença de Moyamoya/fisiopatologia , Marcadores de Spin
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