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1.
Neuroimage ; 240: 118371, 2021 10 15.
Artigo em Inglês | MEDLINE | ID: mdl-34242783

RESUMO

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.


Assuntos
Mapeamento Encefálico/métodos , Encéfalo/metabolismo , Ferro/metabolismo , Imageamento por Ressonância Magnética/métodos , Esclerose Múltipla/metabolismo , Bainha de Mielina/metabolismo , Adulto , Encéfalo/diagnóstico por imagem , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Neurológicos , Esclerose Múltipla/diagnóstico por imagem , Adulto Jovem
2.
Sci Rep ; 11(1): 15276, 2021 07 27.
Artigo em Inglês | MEDLINE | ID: mdl-34315971

RESUMO

Diffusion-weighted imaging (DWI) is proven useful to differentiate benign and malignant soft tissue tumors (STTs). Radiomics utilizing a vast array of extracted imaging features has a potential to uncover disease characteristics. We aim to assess radiomics using DWI can outperform the conventional DWI for STT differentiation. In 151 patients with 80 benign and 71 malignant tumors, ADCmean and ADCmin were measured on solid portion within the mass by two different readers. For radiomics approach, tumors were segmented and 100 original radiomic features were extracted on ADC map. Eight radiomics models were built with training set (n = 105), using combinations of 2 different algorithms-multivariate logistic regression (MLR) and random forest (RF)-and 4 different inputs: radiomics features (R), R + ADCmin (I), R + ADCmean (E), R + ADCmin and ADCmean (A). All models were validated with test set (n = 46), and AUCs of ADCmean, ADCmin, MLR-R, RF-R, MLR-I, RF-I, MLR-E, RF-E, MLR-A and RF-A models were 0.729, 0.753 0.698, 0.700, 0.773, 0.807, 0.762, 0.744, 0.773 and 0.807, respectively, without statistically significant difference. In conclusion, radiomics approach did not add diagnostic value to conventional ADC measurement for differentiating benign and malignant STTs.


Assuntos
Imagem de Difusão por Ressonância Magnética/métodos , Neoplasias de Tecidos Moles/diagnóstico por imagem , Adulto , Idoso , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Neoplasias de Tecidos Moles/patologia
3.
NMR Biomed ; 34(10): e4571, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34129267

RESUMO

MR images based on phase contrast images have gained clinical interest as an in vivo tool for assessing anatomical and histological findings. The globus pallidus is an area of major iron metabolism and storage in the brain tissue. Calcium, another important metal in the body, is frequently deposited in the globus pallidus as well. Recently, we observed dense paramagnetic deposition with paradoxical calcifications in the globus pallidus and putamen. In this work, we explore detailed MR findings on these structures, and the histological source of the related findings using ex vivo CT and MR images. Ex vivo MR was obtained with a maximum 100 µm3 isotropic resolution using a 15.2 T MR system. 3D gradient echo images and quantitative susceptibility mapping were used because of their good sensitivity to metallic deposition, high signal-to-noise ratio, and excellent contrast to iron and calcium. We found dense paramagnetic deposition along the perforating arteries in the globus pallidus. This paramagnetic deposition was hyperdense on ex vivo CT scans. Histological studies confirmed this finding, and simultaneous deposition of iron and calcium, although more iron dominant, was observed along the vessel walls of the globus pallidus. This was an exclusive finding for the penetrating arteries of the globus pallidus. Thus, our results suggest that several strong and paradoxical paramagnetic sources at the globus pallidus can be associated with vascular degeneration.

4.
Radiology ; 300(2): 260-278, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34100679

RESUMO

Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of the midbrain. There are various imaging markers for Parkinson disease. Recent advances in MRI have enabled elucidation of the underlying pathophysiologic changes in the nigral structure. This has contributed to accurate and early diagnosis and has improved disease progression monitoring. This article aims to review recent developments in nigral imaging for Parkinson disease and other parkinsonian syndromes, including nigrosome imaging, neuromelanin imaging, quantitative iron mapping, and diffusion-tensor imaging. In particular, this article examines nigrosome imaging using 7-T MRI and 3-T susceptibility-weighted imaging. Finally, this article discusses volumetry and its clinical importance related to symptom manifestation. This review will improve understanding of recent advancements in nigral imaging of Parkinson disease. Published under a CC BY 4.0 license.


Assuntos
Imageamento por Ressonância Magnética/métodos , Doença de Parkinson/diagnóstico por imagem , Transtornos Parkinsonianos/diagnóstico por imagem , Substância Negra/diagnóstico por imagem , Humanos
5.
IEEE Trans Med Imaging ; PP2021 Jun 30.
Artigo em Inglês | MEDLINE | ID: mdl-34191724

RESUMO

Magnetic resonance imaging (MRI) can provide multiple contrast-weighted images using different pulse sequences and protocols. However, a long acquisition time of the images is a major challenge. To address this limitation, a new pulse sequence referred to as quad-contrast imaging is presented. The quad-contrast sequence enables the simultaneous acquisition of four contrast-weighted images (proton density (PD)-weighted, T2-weighted, PD-fluid attenuated inversion recovery (FLAIR), and T2-FLAIR), and the synthesis of T1-weighted images and T1-and T2-maps in a single scan. The scan time is less than 6 min and is further reduced to 2 min 50 s using a deep learning-based parallel imaging reconstruction. The natively acquired quad contrasts demonstrate high quality images, comparable to those from the conventional scans. The deep learning-based reconstruction successfully reconstructed highly accelerated data (acceleration factor 6), reporting smaller normalized root mean squared errors (NRMSEs) and higher structural similarities (SSIMs) than those from conventional generalized autocalibrating partially parallel acquisitions (GRAPPA)-reconstruction (mean NRMSE of 4.36% vs. 10.54% and mean SSIM of 0.990 vs. 0.953). In particular, the FLAIR contrast is natively acquired and does not suffer from lesion-like artifacts at the boundary of tissue and cerebrospinal fluid, differentiating the proposed method from synthetic imaging methods. The quad-contrast imaging method may have the potentials to be used in a clinical routine as a rapid diagnostic tool.

6.
Radiology ; 299(3): 626-632, 2021 06.
Artigo em Inglês | MEDLINE | ID: mdl-33787335

RESUMO

Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bone metastases. Materials and Methods In this retrospective study, patients who underwent contrast-enhanced abdominal CT and were diagnosed with a bone island or osteoblastic metastasis between 2015 to 2019 at either of two different institutions were included: institution 1 for the training set and institution 2 for the external test set. Radiomics features were extracted. The random forest (RF) model was built using 10 selected features, and subsequent 10-fold cross-validation was performed. In the test phase, the RF model was tested with an external test set. Three radiologists reviewed the CT images for the test set. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated for the models and each of the three radiologists. The AUCs of the radiomics model and radiologists were compared. Results A total of 177 patients (89 with a bone island and 88 with metastasis; mean age, 66 years ± 12 [standard deviation]; 111 men) were in the training set, and 64 (23 with a bone island and 41 with metastasis; mean age, 69 years ± 14; 59 men) were in the test set. Radiomics features (n = 1218) were extracted. The average AUC of the RF model from 10-fold cross-validation was 0.89 (sensitivity, 85% [75 of 88 patients]; specificity, 82% [73 of 89 patients]; and accuracy, 84% [148 of 177 patients]). In the test set, the AUC of the trained RF model was 0.96 (sensitivity, 80% [33 of 41 patients]; specificity, 96% [22 of 23 patients]; and accuracy, 86% [55 of 64 patients]). The AUCs for the three readers were 0.95 (95% CI: 0.90, 1.00), 0.96 (95% CI: 0.90, 1.00), and 0.88 (95% CI: 0.80, 0.96). The AUC of radiomics model was higher than that of only reader 3 (0.96 vs 0.88, respectively; P = .03). Conclusion A CT radiomics-based random forest model was proven useful for differentiating bone islands from osteoblastic metastases and showed better diagnostic performance compared with an inexperienced radiologist. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Vannier in this issue.


Assuntos
Neoplasias Ósseas/diagnóstico por imagem , Aprendizado de Máquina , Osteosclerose/diagnóstico por imagem , Radiografia Abdominal/métodos , Tomografia Computadorizada por Raios X/métodos , Idoso , Meios de Contraste , Diagnóstico Diferencial , Feminino , Humanos , Achados Incidentais , Masculino , República da Coreia , Estudos Retrospectivos
7.
Parkinsonism Relat Disord ; 85: 84-90, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33761389

RESUMO

OBJECTIVES: Despite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD). METHODS: In this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the "pROC" package of R (version 1.16.2). RESULTS: The area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912-1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment. CONCLUSIONS: Our deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.

8.
Eur Radiol ; 31(8): 6147-6155, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33758957

RESUMO

OBJECTIVES: This study aimed to apply a radiomics approach to predict poor psychomotor development in preterm neonates using brain MRI. METHODS: Prospectively enrolled preterm neonates underwent brain MRI near or at term-equivalent age and neurodevelopment was assessed at a corrected age of 12 months. Two radiologists visually assessed the degree of white matter injury. The radiomics analysis on white matter was performed using T1-weighted images (T1WI) and T2-weighted images (T2WI). A total of 1906 features were extracted from the images and the minimum redundancy maximum relevance algorithm was used to select features. A prediction model for the binary classification of the psychomotor developmental index was developed and eightfold cross-validation was performed. The diagnostic performance of the model was evaluated using the AUC with and without including significant clinical and DTI parameters. RESULTS: A total of 46 preterm neonates (median gestational age, 29 weeks; 26 males) underwent brain MRI (median corrected gestational age, 37 weeks). Thirteen of 46 (28.3%) neonates showed poor psychomotor outcomes. There was one neonate among 46 with moderate to severe white matter injury on visual assessment. For the radiomics analysis, twenty features were selected for each analysis. The AUCs of prediction models based on T1WI, T2WI, and both T1WI and T2WI were 0.925, 0.834, and 0.902. Including gestational age or DTI parameters did not improve the prediction performance of T1WI. CONCLUSIONS: A radiomics analysis of white matter using early T1WI or T2WI could predict poor psychomotor outcomes in preterm neonates. KEY POINTS: • Radiomics analysis on T1-weighted images of preterm neonates showed the highest diagnostic performance (AUC, 0.925) for predicting poor psychomotor outcomes. • In spite of 45 of 46 neonates having no significant white matter injury on visual assessment, the radiomics analysis of early brain MRI showed good diagnostic performance (sensitivity, 84.6%; specificity, 78.8%) for predicting poor psychomotor outcomes. • Radiomics analysis on early brain MRI can help to predict poor neurodevelopmental outcomes in preterm neonates.


Assuntos
Imageamento por Ressonância Magnética , Substância Branca , Idade Gestacional , Humanos , Lactente , Recém-Nascido , Masculino , Neuroimagem , Estudos Retrospectivos , Substância Branca/diagnóstico por imagem
9.
Med Phys ; 48(6): 2939-2950, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-33733464

RESUMO

PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1 , T2 , and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. METHODS: The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8. RESULTS: It is demonstrated that the normalized root-mean-square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. CONCLUSION: Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.


Assuntos
Aprendizado Profundo , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Redes Neurais de Computação , Tomografia Computadorizada por Raios X
10.
J Magn Reson Imaging ; 53(2): 360-373, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-32009271

RESUMO

Myelin water imaging (MWI) is an MRI imaging biomarker for myelin. This method can generate an in vivo whole-brain myelin water fraction map in approximately 10 minutes. It has been applied in various applications including neurodegenerative disease, neurodevelopmental, and neuroplasticity studies. In this review we start with a brief introduction of myelin biology and discuss the contributions of myelin in conventional MRI contrasts. Then the MRI properties of myelin water and four different MWI methods, which are categorized as T2 -, T2 *-, T1 -, and steady-state-based MWI, are summarized. After that, we cover more practical issues such as availability, interpretation, and validation of these methods. To illustrate the utility of MWI as a clinical research tool, MWI studies for two diseases, multiple sclerosis and neuromyelitis optica, are introduced. Additional topics about imaging myelin in gray matter and non-MWI methods for myelin imaging are also included. Although technical and physiological limitations exist, MWI is a potent surrogate biomarker of myelin that carries valuable and useful information of myelin. Evidence Level: 5 Technical Efficacy: 1 J. MAGN. RESON. IMAGING 2021;53:360-373.


Assuntos
Esclerose Múltipla , Doenças Neurodegenerativas , Encéfalo/diagnóstico por imagem , Humanos , Imageamento por Ressonância Magnética , Bainha de Mielina , Água
11.
Eur J Radiol ; 134: 109398, 2021 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33264728

RESUMO

OBJECTIVE: To determine whether susceptibility map-weighted imaging (SMWI) was superior to conventional susceptibility-weighted imaging (SWI) in the diagnosis of Parkinson's disease (PD) and in its correlation with 123I-2bcarbomethoxy-3b-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane single photon emission computerized tomography (123I-FP-CIT SPECT). METHODS: Between May 2017 and February 2018, 125 consecutive patients diagnosed with idiopathic PD, vascular pseudoparkinsonism, essential tremor, or drug-induced parkinsonism and who underwent 123I-FP-CIT SPECT imaging and 3 T SWI on the same day or within 3 months were included in this retrospective study. In all patients, SMWI images were generated from SWI images. On both MRIs, two neuroradiologists independently evaluated the status of nigral hyperintensity on each side of substantia nigra. Inter-observer agreements for the nigral hyperintensity were tested. Using consensus reading, concordance between SWI, SMWI, and 123I-FP-CIT SPECT were evaluated, and the diagnostic performance between SWI and SMWI for PD was compared. RESULTS: Inter-observer agreement for the nigral hyperintensity was higher for SMWI (right, κ = 0.919; left, κ = 0.984) than for SWI (right, κ = 0.918; left, κ = 0.902). SMWI (right 67.2 %, left 68.0 %) showed a higher concordance rate with the results of 123I-FP-CIT SPECT than SWI (right 60.0 %, left 59.2 %). SMWI (area under curve [AUC], 0.791) provided significantly higher diagnostic performance for PD than SWI (AUC, 0.720; P = 0.0005). CONCLUSION: SMWI may be a superior assessment tool for nigral hyperintensity than SWI and may be an improved diagnostic imaging modality for patients with suspected PD.


Assuntos
Doença de Parkinson , Transtornos Parkinsonianos , Humanos , Imageamento por Ressonância Magnética , Doença de Parkinson/diagnóstico por imagem , Transtornos Parkinsonianos/diagnóstico por imagem , Estudos Retrospectivos , Substância Negra/diagnóstico por imagem , Tomografia Computadorizada de Emissão de Fóton Único
12.
J Magn Reson Imaging ; 53(3): 818-826, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33219624

RESUMO

BACKGROUND: Automated measurement and classification models with objectivity and reproducibility are required for accurate evaluation of the breast cancer risk of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE). PURPOSE: To develop and evaluate a machine-learning algorithm for breast FGT segmentation and BPE classification. STUDY TYPE: Retrospective. POPULATION: A total of 794 patients with breast cancer, 594 patients assigned to the development set, and 200 patients to the test set. FIELD STRENGTH/SEQUENCE: 3T and 1.5T; T2 -weighted, fat-saturated T1 -weighted (T1 W) with dynamic contrast enhancement (DCE). ASSESSMENT: Manual segmentation was performed for the whole breast and FGT regions in the contralateral breast. The BPE region was determined by thresholding using the subtraction of the pre- and postcontrast T1 W images and the segmented FGT mask. Two radiologists independently assessed the categories of FGT and BPE. A deep-learning-based algorithm was designed to segment and measure the volume of whole breast and FGT and classify the grade of BPE. STATISTICAL TESTS: Dice similarity coefficients (DSC) and Spearman correlation analysis were used to compare the volumes from the manual and deep-learning-based segmentations. Kappa statistics were used for agreement analysis. Comparison of area under the receiver operating characteristic (ROC) curves (AUC) and F1 scores were calculated to evaluate the performance of BPE classification. RESULTS: The mean (±SD) DSC for manual and deep-learning segmentations was 0.85 ± 0.11. The correlation coefficient for FGT volume from manual- and deep-learning-based segmentations was 0.93. Overall accuracy of manual segmentation and deep-learning segmentation in BPE classification task was 66% and 67%, respectively. For binary categorization of BPE grade (minimal/mild vs. moderate/marked), overall accuracy increased to 91.5% in manual segmentation and 90.5% in deep-learning segmentation; the AUC was 0.93 in both methods. DATA CONCLUSION: This deep-learning-based algorithm can provide reliable segmentation and classification results for BPE. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.


Assuntos
Neoplasias da Mama , Mama , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Humanos , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Reprodutibilidade dos Testes , Estudos Retrospectivos
13.
Eur Radiol ; 31(4): 2084-2093, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33006658

RESUMO

OBJECTIVES: To evaluate the additional prognostic value of multiparametric MR-based radiomics in patients with glioblastoma when combined with conventional clinical and genetic prognostic factors. METHODS: In this single-center study, patients diagnosed with glioblastoma between October 2007 and December 2019 were retrospectively screened and grouped into training and test sets with a 7:3 distribution. Segmentations of glioblastoma using multiparametric MRI were performed automatically via a convolutional-neural network. Prognostic factors in the clinical model included age, sex, type of surgery/post-operative treatment, and tumor location; those in the genetic model included statuses of isocitrate dehydrogenase-1 mutation and O-6-methylguanine-DNA-methyltransferase promoter methylation. Univariate and multivariate Cox proportional hazards analyses were performed for overall survival (OS) and progression-free survival (PFS). Integrated time-dependent area under the curve (iAUC) for survival was calculated and compared between prognostic models via the bootstrapping method (performances were validated with prediction error curves). RESULTS: Overall, 120 patients were included (training set, 85; test set, 35). The mean OS and PFS were 25.5 and 18.6 months, respectively. The prognostic performances of multivariate models improved when radiomics was added to the clinical model (iAUC: OS, 0.62 to 0.73; PFS, 0.58 to 0.66), genetic model (iAUC: OS, 0.59 to 0.67; PFS, 0.59 to 0.65), and combined model (iAUC: OS, 0.65 to 0.73; PFS, 0.62 to 0.67). In the test set, the combined model (clinical, genetic, and radiomics) demonstrated robust validation for risk prediction of OS and PFS. CONCLUSIONS: Radiomics increased the prognostic value when combined with conventional clinical and genetic prognostic models for OS and PFS in glioblastoma patients. KEY POINTS: • CNN-based automatic segmentation of glioblastoma on multiparametric MRI was useful in extracting radiomic features. • Patients with glioblastoma with high-risk radiomics scores had poor overall survival (hazards ratio 8.33, p < 0.001) and progression-free survival (hazards ratio 3.76, p < 0.001). • MR-based radiomics improved the survival prediction when combined with clinical and genetic factors (overall and progression-free survival iAUC from 0.65 to 0.73 and 0.62 to 0.67, respectively; both p < 0.001).


Assuntos
Neoplasias Encefálicas , Glioblastoma , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/genética , Glioblastoma/diagnóstico por imagem , Glioblastoma/genética , Humanos , Imageamento por Ressonância Magnética , Prognóstico , Estudos Retrospectivos
14.
J Clin Neurol ; 16(4): 562-572, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33029961

RESUMO

BACKGROUND AND PURPOSE: Iron retained by activated microglia and macrophages in multiple sclerosis (MS) lesions may serve as a marker of innate immune system activation. Among several magnetic resonance imaging (MRI) methods, there has been recent interest in using quantitative susceptibility mapping (QSM) as a potential tool for assessing iron levels in the human brain. This study examined QSM findings in MS and neuromyelitis optica spectrum disorder (NMOSD) lesions obtained with 3-T MRI to assess imaging characteristics related to paramagnetic rims around brain lesions in MS and NMOSD. METHODS: This study included 32 MS and 21 seropositive NMOSD patients. MRI images were obtained using two 3-T MRI devices (Ingenia, Philips Healthcare; and Magnetom Verio, Siemens Healthineers) during routine diagnosis and treatment procedures. Multi and single echo gradient echo magnitude and phase images were obtained for QSM reconstruction. QSM images were used to characterize the detected lesions, and the findings were compared between MS and NMOSD. RESULTS: Totals of 71 and 35 MRI scans were performed during the study period in MS and NMOSD patients, respectively. In QSM images, paramagnetic rims were found in 26 (81.2%) MS patients and 1 (4.8%) NMOSD patient. Eight of the 22 MS patients and only 1 of the 10 NMOSD patients who underwent follow-up MRI showed new paramagnetic rims. The paramagnetic rim lesions appeared after enhancement or in new T2-weighted lesions without enhancement. CONCLUSIONS: Paramagnetic rims might be a characteristic MRI finding for MS, and therefore they have potential as an imaging marker for differentially diagnosing MS from NMOSD using 3-T MRI.

15.
Hum Brain Mapp ; 41(18): 5313-5324, 2020 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-32897599

RESUMO

We investigated the narrow-sense heritability of MRI-visible dilated perivascular spaces (dPVS) in healthy young adult twins and nontwin siblings (138 monozygotic, 79 dizygotic twin pairs, and 133 nontwin sibling pairs; 28.7 ± 3.6 years) from the Human Connectome Project. dPVS volumes within basal ganglia (BGdPVS) and white matter (WMdPVS) were automatically calculated on three-dimensional T2-weighted MRI. In univariate analysis, heritability estimates of BGdPVS and WMdPVS after age and sex adjustment were 65.8% and 90.2%. In bivariate analysis, both BGdPVS and WMdPVS showed low to moderate genetic correlations (.30-.43) but high shared heritabilities (71.8-99.9%) with corresponding regional volumes, intracranial volumes, and other regional dPVS volumes. Older age was significantly associated with larger dPVS volume in both regions even after adjusting for clinical and volumetric variables, while blood pressure was not associated with dPVS volume although there was weak genetic correlation. dPVS volume, particularly WMdPVS, was highly heritable in healthy young adults, adding evidence of a substantial genetic contribution in dPVS development and differential effect by location. Age affects dPVS volume even in young adults, while blood pressure might have limited role in dPVS development in its normal range.

16.
Radiology ; 297(1): 143-150, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32692298

RESUMO

Background The relationship between administration of macrocyclic gadolinium-based contrast agents and T1-weighted signal intensity (SI) change of the globus pallidus (GP) and dentate nucleus (DN) is, to the knowledge of the authors, not known. Purpose To determine if quantitative susceptibility mapping (QSM) can detect changes in magnetic susceptibility of the GP and DN after serial administration of macrocyclic gadobutrol in patients with primary brain tumors. Materials and Methods Patients diagnosed with primary brain tumors from August 2014 to February 2019 were eligible for this single-center retrospective study. Among 501 consecutive adult patients who were given at least one administration of gadobutrol, those who were previously administered an unknown or linear gadolinium-based contrast agent were excluded. Brain MRI scans with three-dimensional gradient-recalled-echo image phase data for QSM processing were reviewed. Regions of interest were drawn on the GP and DN on the basis of semiautomatic thresholding. Univariable generalized estimation equations were used to determine the associations between MRI measures (SI on T1-weighted images and magnetic susceptibility on QSM) and number of gadobutrol doses. Potential confounding factors were adjusted for in multivariable generalized estimating equation. Results Ninety patients (mean age, 51 years ± 17 [standard deviation]; 51 men) with 199 MRI scans were analyzed. In models adjusted for repeated observations between injections, the number of injections of gadobutrol was associated with the magnetic susceptibility of the GP (1.4 × 10-3 ppm/number of gadobutrol injections; P = .01) and DN (8.1 × 10-4 ppm/number of gadobutrol injections; P = .03). After adjustment for confounders, the number of gadobutrol injections remained an independent predictor of increased magnetic susceptibility in the GP (1.3 × 10-3 ppm/number of gadobutrol injections; 95% confidence interval: 0.39 × 10-3, -2.4 × 10-3; P = .006). There were no associations between number of gadobutrol injections and SI or magnetic susceptibility in the DN. Conclusion The magnetic susceptibility of the globus pallidus increased after serial administration of gadobutrol. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Wang and Prince in this issue.


Assuntos
Mapeamento Encefálico/métodos , Neoplasias Encefálicas/diagnóstico por imagem , Meios de Contraste/administração & dosagem , Imageamento por Ressonância Magnética/métodos , Compostos Organometálicos/administração & dosagem , Núcleos Cerebelares/diagnóstico por imagem , Feminino , Seguimentos , Globo Pálido/diagnóstico por imagem , Humanos , Imageamento Tridimensional , Estudos Longitudinais , Masculino , Pessoa de Meia-Idade
17.
Sci Rep ; 10(1): 9556, 2020 06 12.
Artigo em Inglês | MEDLINE | ID: mdl-32533053

RESUMO

Acute appendicitis is one of the most common causes of abdominal emergencies. We investigated the feasibility of a neural-network-based diagnosis algorithm of appendicitis by using computed tomography (CT) for patients with acute abdominal pain visiting the emergency room (ER). A neural-network-based diagnostic algorithm of appendicitis was developed and validated using CT data from three institutions who visited the ER with abdominal pain and underwent abdominopelvic CT. For input data, 3D isotropic cubes including the appendix were manually extracted and labeled as appendicitis or a normal appendix. A 3D convolutional neural network (CNN) was trained to binary classification on the input. For model development and testing, 8-fold cross validation was conducted for internal validation and an ensemble model was used for external validation. Diagnostic performance was excellent in both the internal and external validation with an accuracy larger than 90%. The CNN-based diagnosis algorithm may be feasible in diagnosing acute appendicitis using the CT data of patients visiting the ER with acute abdominal pain.


Assuntos
Dor Abdominal/diagnóstico , Apendicite/diagnóstico , Abdome , Doença Aguda , Adulto , Algoritmos , Apêndice/diagnóstico por imagem , Meios de Contraste/administração & dosagem , Emergências , Serviço Hospitalar de Emergência , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Redes Neurais de Computação , Tomografia Computadorizada por Raios X/métodos
18.
Eur J Radiol ; 128: 109031, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32417712

RESUMO

PURPOSE: This study aimed to determine whether MR-based radiomics of glioblastoma can predict the isocitrate dehydrogenase-1 (IDH1) mutation status and compare predictive performances between manual and fully automatic deep-learning segmentations. METHOD: Forty-five glioblastoma patients with pretreatment T2-weighted MRIs were retrospectively evaluated. Manual segmentations of glioblastoma and peri-tumoral edema were trained via a deep neural network (V-Net). An independent external cohort of 137 glioblastoma patients from the Cancer Imaging Archive was also included (test set 1, n = 46; test set 2, n = 91). Test set 1-without known IDH1 status-was used to calculate dice similarity coefficients (DSC) between the two segmentation methods (manual & V-Net). From test set 2, all-relevant radiomic features were selected via a random forest-based wrapper algorithm for IDH1 prediction. Receiver operating characteristics (ROC) curves with areas under the curve (AUC) were plotted as performance metrics for both methods. RESULTS: Among 136 patients (45 and 91 patients from our institution and test set 2, respectively), 17 patients (11.2 %) had IDH1 mutations. The mean DSC of test set 1 was 0.78 ±â€¯0.14 (range, 0.34-0.94). A subset of 9 all-relevant features (8.4 %, 9/107) was selected. V-Net segmentation of the test set 2 yielded similar performance in predicting IDH1 mutation as compared to manual segmentation (V-Net AUC = 0.86 vs. manual AUC = 0.90). The optimal cut-point threshold of AUC yielded 86.8 % accuracy for manual segmentation and 75.8 % for V-Net segmentation. CONCLUSIONS: V-Net showed robust segmentation capability of glioblastoma on T2-weighted MRI. All-relevant radiomics features from both segmentation methods yielded a similar performance in IDH1 prediction.


Assuntos
Neoplasias Encefálicas/diagnóstico por imagem , Glioblastoma/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Isocitrato Desidrogenase/genética , Imageamento por Ressonância Magnética/métodos , Mutação/genética , Algoritmos , Área Sob a Curva , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Estudos de Coortes , Aprendizado Profundo , Feminino , Glioblastoma/genética , Glioblastoma/patologia , Humanos , Masculino , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos
19.
Neuroimage ; 211: 116619, 2020 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-32044437

RESUMO

Recently, deep neural network-powered quantitative susceptibility mapping (QSM), QSMnet, successfully performed ill-conditioned dipole inversion in QSM and generated high-quality susceptibility maps. In this paper, the network, which was trained by healthy volunteer data, is evaluated for hemorrhagic lesions that have substantially higher susceptibility than healthy tissues in order to test "linearity" of QSMnet for susceptibility. The results show that QSMnet underestimates susceptibility in hemorrhagic lesions, revealing degraded linearity of the network for the untrained susceptibility range. To overcome this limitation, a data augmentation method is proposed to generalize the network for a wider range of susceptibility. The newly trained network, which is referred to as QSMnet+, is assessed in computer-simulated lesions with an extended susceptibility range (-1.4 â€‹ppm to +1.4 â€‹ppm) and also in twelve hemorrhagic patients. The simulation results demonstrate improved linearity of QSMnet+ over QSMnet (root mean square error of QSMnet+: 0.04 â€‹ppm vs. QSMnet: 0.36 â€‹ppm). When applied to patient data, QSMnet+ maps show less noticeable artifacts to those of conventional QSM maps. Moreover, the susceptibility values of QSMnet+ in hemorrhagic lesions are better matched to those of the conventional QSM method than those of QSMnet when analyzed using linear regression (QSMnet+: slope â€‹= â€‹1.05, intercept â€‹= â€‹-0.03, R2 â€‹= â€‹0.93; QSMnet: slope â€‹= â€‹0.68, intercept â€‹= â€‹0.06, R2 â€‹= â€‹0.86), consolidating improved linearity in QSMnet+. This study demonstrates the importance of the trained data range in deep neural network-powered parametric mapping and suggests the data augmentation approach for generalization of network. The new network can be applicable for a wide range of susceptibility quantification.


Assuntos
Hemorragia Cerebral/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Assistida por Computador/normas , Imageamento por Ressonância Magnética/normas , Neuroimagem/normas , Adulto , Artefatos , Simulação por Computador , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Neuroimagem/métodos
20.
Neurobiol Aging ; 87: 125-131, 2020 03.
Artigo em Inglês | MEDLINE | ID: mdl-31918953

RESUMO

Although age-related changes of cerebral arteries were observed in in vivo magnetic resonance angiography (MRA), standard tools or methods measuring those changes were limited. In this study, we developed and evaluated a model to measure age-related changes in the cerebral arteries from 3D MRA using a 3D deep convolutional neural network. From participants without any medical abnormality, training (n = 800) and validation sets (n = 88) of 3D MRA were built. After preprocessing and data augmentation, a 3D convolutional neural network was trained to estimate each subject's chronological age from in vivo MRA data. There was good correlation between chronological age and predicted age (r = 0.83) in an independent test set (n = 354). The predicted age difference (PAD) of the test set was 2.41 ± 6.22. Interaction term between age and sex was significant for PAD (p = 0.008). After correcting for age and interaction term, men showed higher PAD (p < 0.001). Hypertension was associated with higher PAD with marginal significance (p = 0.073). We suggested that PAD might be a potential measurement of cerebral vascular aging.


Assuntos
Envelhecimento/patologia , Artérias Cerebrais/diagnóstico por imagem , Angiografia por Ressonância Magnética/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Artérias Cerebrais/patologia , Aprendizado Profundo , Feminino , Humanos , Hipertensão , Imageamento Tridimensional , Masculino , Pessoa de Meia-Idade , Adulto Jovem
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