Relevant bibliographies by topics / T1-weighted

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'T1-weighted'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "T1-weighted"

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Gulati, Parveen, Neetika Gupta, Aishwarya Gulati, Arif Mirza, and Vaibhav Gulati. "Intracranial T1 Weighted Hyperintense Lesions." MAMC Journal of Medical Sciences 3, no. 2 (2017): 61. http://dx.doi.org/10.4103/mamcjms.mamcjms_34_17.

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Huang, Teng-Yi, Ing-Jye Huang, Cheng-Yu Chen, Klaus Scheffler, Hsiao-Wen Chung, and Hui-Cheng Cheng. "Are TrueFISP imagesT2/T1-weighted?" Magnetic Resonance in Medicine 48, no. 4 (2002): 684–88. http://dx.doi.org/10.1002/mrm.10260.

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Sujata Tukaram Bhairnallykar, Et al. "T1- Weighted MRI Image Segmentation." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2078–84. http://dx.doi.org/10.17762/ijritcc.v11i9.9208.

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Growing evidence in recent years indicates that interest in the development of automated image analysis techniques for medical imaging, especially with regard to the discipline of magnetic resonance imaging. T1-weighted MRI scans are often used for both diagnosis and monitoring various neurological disorders, making accurate segmentation of these images crucial for effective treatment planning. In this work, we offer a new method for T1-weighted MRI image segmentation using patch densenet, an image segmentation-specific deep learning architecture. Our method aims to improve the accuracy and efficiency of segmentation, while also addressing some of the challenges associated with traditional segmentation methods. Traditional segmentation methods typically rely on features that are handcrafted and may struggle to accurately capture the intricate details present in MRI images. By utilizing patch densenet, our method automatically learn and extract relevant features from the T1-weighted MRI images and further enhance the accuracy and specificity of the segmentation results. Ultimately, we believe that our proposed approach can greatly improve diagnosis and treatment planning process for neurological disorders.
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Lavdas, Eleftherios, Marianna Vlychou, Nikos Arikidis, Eftychia Kapsalaki, Violetta Roka, and Ioannis V. Fezoulidis. "Comparison of T1-weighted fast spin-echo and T1-weighted fluid-attenuated inversion recovery images of the lumbar spine at 3.0 tesla." Acta Radiologica 51, no. 3 (2010): 290–95. http://dx.doi.org/10.3109/02841850903501650.

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Background: T1-weighted fluid-attenuated inversion recovery (FLAIR) sequence has been reported to provide improved contrast between lesions and normal anatomical structures compared to T1-weighted fast spin-echo (FSE) imaging at 1.5T regarding imaging of the lumbar spine. Purpose: To compare T1-weighted FSE and fast T1-weighted FLAIR imaging in normal anatomic structures and degenerative and metastatic lesions of the lumbar spine at 3.0T. Material and Methods: Thirty-two consecutive patients (19 females, 13 males; mean age 44 years, range 30–67 years) with lesions of the lumbar spine were prospectively evaluated. Sagittal images of the lumbar spine were obtained using T1-weighted FSE and fast T1-weighted FLAIR sequences. Both qualitative and quantitative analyses measuring the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and relative contrast (ReCon) between degenerative and metastatic lesions and normal anatomic structures were conducted, comparing these sequences. Results: On quantitative evaluation, SNRs of cerebrospinal fluid (CSF), nerve root, and fat around the root of fast T1-weighted FLAIR imaging were significantly lower than those of T1-weighted FSE images ( P<0.001). CNRs of normal spinal cord/CSF and disc herniation/ CSF for fast T1-weighted FLAIR images were significantly higher than those for T1-weighted FSE images ( P<0.001). ReCon of normal spinal cord/CSF, disc herniation/CSF, and vertebral lesions/CSF for fast T1-weighted FLAIR images were significantly higher than those for T1-weighted FSE images ( P<0.001). On qualitative evaluation, it was found that CSF nulling and contrast at the spinal cord (cauda equina)/CSF interface for T1-weighted FLAIR images were significantly superior compared to those for T1-weighted FSE images ( P<0.001), and the disc/spinal cord (cauda equina) interface was better for T1-weighted FLAIR images ( P<0.05). Conclusion: The T1-weighted FLAIR sequence may be considered as the preferred lumbar spine imaging sequence compared to T1-weighted FSE, as it has demonstrated superior CSF nulling, better conspicuousness of normal anatomic structures and degenerative and metastatic lesions, and improved image contrast.
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Fatimah, Fatimah, Rini Indrati, and Adjie Suroso. "Brain Mr Imaging With A T1-Weighted Image Fluid Attenuated Inversion Recovery Sequence : Comparison Study With T1-Weighted Image Spin Echo Sequence." Jurnal Riset Kesehatan 1, no. 2 (2015): 94–99. https://doi.org/10.31983/jrk.v1i2.374.

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The purpose of this research to find out the differences between T1-Weighted Image Fluid Attenuated Inversion Recovery (T1WI FLAIR) and T1-Weighted Image Spin Echo (T1WI SE) sequences, use with MRI Superconducting Modality 0,5 Tesla and to determine its superiority between those sequences, at low field strengths. This is experimental research. Twenty four patients with brain lesions underwent T1-Weighted Image Spin Echo and T1-Weighted FLAIR imaging during the same imaging session. T1-Weighted Spin Echo and T1-Weighted FLAIR Images were compared on the basis their SNR and CNR. Data were analysed statistically using t-test and Mann-Whitney test. Contrast to Noise Ratio (CNRs) obtained with T1-Weighted Image FLAIR were comparable but statistically superior to those obtained with T1-Weighted Spin Echo imaging whether on the CNR lesion- white matter with p value 0,043 or lesion-gray matter with p value 0,015.
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Kongpromsuk, Sutasinee, Nantaporn Pitakvej, Nutchawan Jittapiromsak, and Supada Prakkamakul. "Detection of brain metastases using alternative magnetic resonance imaging sequences: a comparison between SPACE and VIBE sequences." Asian Biomedicine 14, no. 1 (2020): 27–35. http://dx.doi.org/10.1515/abm-2020-0005.

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AbstractBackgroundAccurate identification of brain metastases is crucial for cancer treatment.ObjectivesTo compare the ability to detect brain metastases of two alternative types of contrast-enhanced three-dimensional (3D) T1-weighted sequences called SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolutions) and VIBE (Volumetric Interpolated Brain Sequence) on magnetic resonance imaging (MRI) at 3 tesla.MethodsBetween April 2017 and February 2018, 27 consecutive adult Thai patients with a total number of 424 brain metastases were retrospectively included. The patients underwent both contrast-enhanced 3D T1-weighted SPACE and 3D T1-weighted VIBE MRI sequences at 3 tesla. Two neuroradiology experts independently reviewed the images to determine the number of enhancing lesions on each sequence. Wilcoxon signed rank test was used to compare the difference between the numbers of detectable parenchymal enhancing lesions. Interobserver reliability was calculated using intraclass correlation.Results3D T1-weighted SPACE detected more parenchymal enhancing lesions than 3D T1-weighted VIBE (424 vs. 378 lesions, median 6 vs. 5, P = 0.008). Fifteen patients (55.6%) had equal number of parenchymal enhancing lesions between two sequences. 3D T1-weighted SPACE detected more parenchymal enhancing lesions (up to 9 more lesions) in 10 patients (37%), while 3D T1-weighted VIBE detected more enhancing lesions (up to 2 more lesions) in 2 patients (7.4%). Interobserver reliability between the readers was excellent.ConclusionContrast-enhanced 3D T1-weighted SPACE sequence demonstrates a higher ability to detect brain metastases than contrast-enhanced 3D T1-weighted VIBE sequence at 3 tesla.
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Asri, Isnindar Tandya, Chomsin Sulistya Widodo, and Yuyun Yueniwati Prabowowati Wadjib. "Comparison of Grayscale Value in T1-Weighted Pre- and Post-Contrast Brain MRI Images: with and without Fat Suppression Technique." Journal of Physics: Conference Series 2049, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1742-6596/2049/1/012057.

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Abstract The MRI T1-weighted image can provide information on the pre- and post-contrast images. Post-contrast images is an image obtained after the administration of GBCA In some cases, not all post-contrast images can show clear lesions so it requires additional technique in the form of Fat Suppression (FS), which works by suppressing the fat signal in an image. The T1-weighted images with and without FS have a different signal intensity. Therefore, the purpose of this study is to compare the signal intensity of the pre- and post-contrast T1-weighted images with and without the FS technique. The signal intensities are indicated with a grayscale value. There are seven T1-weighted images with FS and seven T1-weighted images without FS. Each of the image have a pre-and post-contrast. Image reading is done by a radiology specialist. Area plot was performed on abnormal tissues in each image. Each area will be measured with an ImageJ software to obtain the grayscale mean value. The measurements of the post contrast T1-weighted image showed an increase in the grayscale mean value with or without the FS technique. This showed that the administration of GBCA can increase the signal intensity on the T1-weighted images with or without the FS technique.
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Braga, Barbara, Clarissa L. Yasuda, and Fernando Cendes. "White Matter Atrophy in Patients with Mesial Temporal Lobe Epilepsy: Voxel-Based Morphometry Analysis of T1- and T2-Weighted MR Images." Radiology Research and Practice 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/481378.

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Introduction. Mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis is highly refractory to clinical treatment. MRI voxel-based morphometry (VBM) of T1-weighted images has revealed a widespread pattern of gray matter (GM) and white matter (WM) atrophy in MTLE. Few studies have investigated the role of T2-weighted images in revealing WM atrophy using VBM.Objectives. To compare the results of WM atrophy between T1- and T2-weighted images through VBM.Methods. We selected 28 patients with left and 27 with right MTLE and 60 normal controls. We analyzed T1- and T2- weighted images with SPM8, using VBM/DARTEL algorithm to extract maps of GM and WM. The second level of SPM was used to investigate areas of WM atrophy among groups.Results. Both acquisitions showed bilateral widespread WM atrophy. T1-weighted images showed higher sensibility to detect areas of WM atrophy in both groups of MTLE. T2-weighted images also showed areas of WM atrophy in a more restricted pattern, but still bilateral and with a large area of superposition with T1-weighted images.Conclusions. In MTLE, T1-weighted images are more sensitive to detect subtle WM abnormalities using VBM, compared to T2 images, although both present a good superposition of statistical maps.
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Yoon, Young Heon, Won Hee Jee, Bae Young Lee, Si Young Choi, Bum Soo Kim, and Kyu Ho Choi. "Benign Versus Malignant Vertebral Compression Fractures: Distinction with T1-weighted, Fast Spin-EchoT2-weighted, and Fat-suppressed Gadolinium-enhanced T1-weighted Images." Journal of the Korean Radiological Society 40, no. 1 (1999): 155. http://dx.doi.org/10.3348/jkrs.1999.40.1.155.

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Li, Hao, Meng Zhao, Yuming Jiao, et al. "Prediction of High-Grade Pediatric Meningiomas: Magnetic Resonance Imaging Features Based on T1-Weighted, T2-Weighted, and Contrast-Enhanced T1-Weighted Images." World Neurosurgery 91 (July 2016): 89–95. http://dx.doi.org/10.1016/j.wneu.2016.03.079.

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Dissertations / Theses on the topic "T1-weighted"

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Nazarpoor, Mahmood. "Flow measurement with T1-weighted MRI techniques." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403291.

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Teo, Arnold, and Daniel Tsada Yosief. "Influence of T1 and T2 weighted MRI images on automated diagnosis of Alzheimer's disease." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301735.

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Alzheimer’s Disease (AD) is the most common form of dementia and according to the World Health Organization it contributes to 60-70% of the approximately 50 million total worldwide dementia cases. While AD is easy to diagnose from a patient’s symptoms alone, due to AD atrophying brain tissue, a Magnetic Resonance Imaging (MRI) scan can strengthen the diagnosis. Computer-Aided Diagnosis (CAD) refers to the use of machine learning methods for assistance with diagnosis. The main purpose of CAD is to assist, primarily radiologists, with a second opinion in a diagnosis and reduce the amount of false diagnosis. In some cases CAD can also aid in accurate early detection of AD and therefore allowing the patient the possibility to deploy preventive measures. This study investigates the influence of T1 or T2 weighted MRI images on the automated diagnosis of AD using the VGG-16 Deep Neural Network (DNN). Previous studies have researched classification accuracy of different algorithms or artificial neural networks on MRI images for automated diagnosis of AD. However, the choice of MRI weighting varies in these studies and reasoning for choosing said weightings are not explicitly given. This study thus aims to answer whether choice of MRI weighting can affect the classification accuracy of AD using automated diagnosis, and if so, how significantly? Two of the most common MRI weightings were chosen for the study, T1 and T2. 149 images of each weighting were manually collected from the ADNI database and used for training and validation of the VGG-16 DNN. The resulting difference in classification accuracy was significant, with T1 having an average accuracy of 59.41% and T2 an average of 74.71%. The obtained conclusion was that the selection of T1 or T2 weighted images could have a significant influence on the classification accuracy of a chosen CAD method. More research needs to be done however to see if the results of this study are repeated for other algorithms and/ or for larger datasets.<br>Alzheimers sjukdom (AD) är den vanligaste formen av demens och enligt Världshälsoorganisationen bidrar den till 60-70% av de approximativt 50 miljoner totala globala demensfallen. Medan AD är lätt att diagnostisera enbart utifrån en patients symptom, på grund av att AD atrofierar hjärnvävnad, kan en Magnetisk Resonanstomografisk (MR) undersökning stärka diagnosen. Datorassisterad diagnostisering (CAD) avser användningen av maskininlärningsmetoder för hjälp med diagnostisering. Huvudsyftet med CAD är att hjälpa, främst radiologer, med en andra åsikt vid en diagnos och minska mängden falska diagnoser. I vissa fall kan CAD också hjälpa med tidig upptäckt av AD och därmed ge patienten möjlighet att vidta förebyggande åtgärder. Denna studie undersöker påverkan av T1- eller T2-viktade MR-bilder på automatiska diagnostiseringen av AD med hjälp av Djupa Neurala Nätverket (DNN) VGG-16. Tidigare studier har undersökt klassificeringsnoggrannheten för olika algoritmer eller artificiella neurala nätverk på MR-bilder för automatisk diagnostisering av AD. Valet av MR-viktning varierar i dessa studier och resonemang för valet av viktning ges inte uttryckligen. Denna studie syftar således till att svara på om valet av MR-viktning kan påverka klassificeringsnoggrannheten på AD vid användning av automatiserad diagnos, och i så fall hur betydande? Två av de vanligaste MR-vikterna valdes för studien, T1 och T2. 149 bilder av vardera viktning samlades manuellt från ADNI-databasen och användes för träning och validering av VGG-16 DNN. Den resulterande skillnaden i klassificeringsnoggrannheten var betydlig, där T1 hade en genomsnittlig noggrannhet på 59.41% och T2 ett genomsnitt på 74.71%. Den erhållna slutsatsen var att valet av T1- eller T2-viktade bilder kan ha ett betydande inflytande på klassificeringsnoggrannheten för en vald CAD-metod. Mer forskning behöver dock göras för att se om resultaten av denna studie upprepas för andra algoritmer och/ eller för större datamängder.
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Petrek, Tomáš. "Zpracování difuzně vážených obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220421.

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Diploma thesis explores the possibility of using diffusion-weighted images in medicine. The paper is a brief physical principle of operation of the magnetic resonance as a tool for non-destructive imaging of the internal structure of substances, the principle of the display contrast as T1, T2 and diffusion weighted images, the course of the sequence for obtaining images with different contrast. Medicine is faced with the problem of classification of pathological tissue in the brain. Contrast diffusion-weighted images does not visually determine the shape of pathological tissue in the form of a tumor or edema. With the T1 and T2 weighted images were calculated mask corresponding tumor and edema, that have been applied to the diffusion-weighted images. Images of the tumor and edema have been subjected diffusivity measurements and statistical evaluation for the purpose of classifying the type of tumor. Investigations were seven findings glioma and metastatic five awards. The research was focused on classifying pathological tissue.
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Brand, Jonathan F., Lars R. Furenlid, Maria I. Altbach, et al. "Task-based optimization of flip angle for fibrosis detection in T1-weighted MRI of liver." SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2016. http://hdl.handle.net/10150/622346.

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Chronic liver disease is a worldwide health problem, and hepatic fibrosis (HF) is one of the hallmarks of the disease. The current reference standard for diagnosing HF is biopsy followed by pathologist examination; however, this is limited by sampling error and carries a risk of complications. Pathology diagnosis of HF is based on textural change in the liver as a lobular collagen network that develops within portal triads. The scale of collagen lobules is characteristically in the order of 1 to 5 mm, which approximates the resolution limit of in vivo gadolinium-enhanced magnetic resonance imaging in the delayed phase. We use MRI of formalin-fixed human ex vivo liver samples as phantoms that mimic the textural contrast of in vivo Gd-MRI. We have developed a local texture analysis that is applied to phantom images, and the results are used to train model observers to detect HF. The performance of the observer is assessed with the area-under-the-receiver-operator-characteristic curve (AUROC) as the figure-of-merit. To optimize the MRI pulse sequence, phantoms were scanned with multiple times at a range of flip angles. The flip angle that was associated with the highest AUROC was chosen as optimal for the task of detecting HF. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Shokouhimehr, Mohammadreza. "Prussian Blue Nanoparticles and its Analogues as New-Generation T1-Weighted MRI Contrast Agents for Cellular Imaging." Kent State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=kent1275612500.

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Kasahara, Seiko. "Hyperintense dentate nucleus on unenhanced T1-weighted MR images is associated with a history of brain irradiation." Kyoto University, 2011. http://hdl.handle.net/2433/151912.

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Chien, Claudia [Verfasser]. "Spinal cord atrophy measured from cerebral T1-weighted MRI: applications in clinical investigations of neuromyelitis optica spectrum disorders / Claudia Chien." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2021. http://d-nb.info/1228860939/34.

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Kašák, Pavel. "Měření difúsního koeficientu membrán dialyzačních filtrů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220057.

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This thesis focuses on the measurement of diffusion coefficient of dialysis membrane. The first part describes possibilities of membrane modelling. Basic models, which allow us to determine the basic characteristics of dialysis membranes like permeability and diffusion coefficient, are described. Next chapter deals with basic types and properties of membranes. The main part focuses on making the experimental installation, which is used to simulate permeance of contrast agent, used in DCE-MRI, through dialysis membrane. The last theoretical chapter describes calculations used to estimate diffusion coefficient. Practical part of this thesis uses a designed experimental installation for estimation of diffusion coefficient for two contrast agents Gadovist® and Multihance®.
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Ohno, Tsuyoshi. "Usefulness of breath-hold inversion recovery-prepared T1-weighted two-dimensional gradient echo sequence for detection of hepatocellular carcinoma in Gd-EOB-DTPA-enhanced MR imaging." Kyoto University, 2017. http://hdl.handle.net/2433/218009.

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Minsterová, Alžběta. "Srovnání preklinických DCE-MRI perfusních technik." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242190.

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This diploma thesis deals with DCE-MRI (Dynamic Contrast-Enhanced Magnetic Resonance Imaging) thus one of the contrast magnetic resonance imaging methods. It describes the principle of conventional continuous DCE-MRI, which uses single bolus of contrast agent and further it focuses on the dual bolus contrast agent techniques, especially the interleaved acquisition. The graphical interface for processing Bruker systems data was made. Synthetic data were used to evaluate the influence of this method on the perfusion parameters estimation. Simulations proved that the further the second bolus is from the first one, the better results are. Simulations of acquisition interruption did not lead to the clear result. However, two statements, which are expected to lead to as good estimation of perfusion parameters as possible, were formulated
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Books on the topic "T1-weighted"

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Lam, Diana L., and Habib Rahbar. Non-Mass Enhancement on MRI. Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0031.

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Breast cancer presents on MRI as an enhancing finding on post-contrast T1-weighted images that is distinct from normal background parenchymal enhancement (BPE), and these enhancing lesions can be further described as a focus, mass, or non-mass enhancement (NME). Each enhancing lesion, with the exception of a focus, can be described further with specific morphological features that are defined by the ACR BI-RADS Atlas. This chapter reviews the key imaging and clinical features, imaging protocols and pitfalls, differential diagnoses, and management recommendations of a focus of enhancement and non-mass enhancement on MRI. Topics discussed include distinguishing a focus from normal BPE, benign versus suspicious features of a focus, NME characterization, and kinetic enhancement curves.
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Tuschl, Karin, Peter T. Clayton, and Philippa B. Mills. Disorders of Manganese Metabolism. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0045.

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Manganese is an essential trace metal for numerous metalloenzymes. Manganese homeostasis requires tight regulation in vivo and disruption of this balance can lead to manganese overload and subsequent accumulation of manganese in brain, liver, and blood. Mutations in SLC30A10, a cell surface-localized manganese efflux transporter, cause an autosomal recessive hypermanganesemia syndrome with two distinct phenotypes: childhood onset dystonia and adult onset Parkinsonism, associated with chronic liver disease, polycythemia and features of iron depletion. MRI brain appearances are characteristic of Mn deposition with hyperintense basal ganglia on T1-weighted images. Chelation therapy with disodium calcium edetate and iron supplementation effectively lower blood manganese levels, halt liver disease progression and improve neurological symptoms.The inherited form of hypermanganesemia can be distinguished from acquired causes of manganese overload including environmental overexposure and acquired hepatocerebral degeneration in cases of end stage liver disease.
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Maksymowych, Walter P., and Robert G. W. Lambert. Imaging: sacroiliac joints. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198734444.003.0013.

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Radiography of the sacroiliac (SI) joints still forms the cornerstone of diagnosis of axial spondyloarthritis (axSpA), although its limitations in early disease preclude early diagnosis. Equivocal radiographic findings of sacroiliitis should be followed by MRI evaluation of the SI joints, especially if clinical suspicion of SpA is high. Routine diagnostic evaluation for SpA by MRI of the SI joints should include simultaneous evaluation of T1-weighted (T1W) and short tau inversion recovery (STIR) or T2 fat-suppressed scans. Bone marrow oedema (BME) in subchondral bone is the primary MRI feature that points to the diagnosis of SpA, although structural lesions such as erosion and fat metaplasia may also be evident in early disease and enhance confidence in the diagnosis. Both inflammatory and structural lesions in the SI joints on MRI can now be quantified in a reliable manner to facilitate therapeutic evaluation in clinical trials and for basic and clinical research.
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Book chapters on the topic "T1-weighted"

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Chen, Ying, Sumayya J. Almarzouqi, Michael L. Morgan, and Andrew G. Lee. "T1-Weighted Image." In Encyclopedia of Ophthalmology. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-35951-4_1228-1.

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Chen, Ying, Sumayya J. Almarzouqi, Michael L. Morgan, and Andrew G. Lee. "T1-Weighted Image." In Encyclopedia of Ophthalmology. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-540-69000-9_1228.

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Xiang, Lei, Yong Chen, Weitang Chang, et al. "Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00928-1_25.

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Shiroishi, Mark S., Jesse G. A. Jones, Naira Muradyan, et al. "MR Perfusion Imaging: ASL, T2*-Weighted DSC, and T1-Weighted DCE Methods." In Functional Brain Tumor Imaging. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-5858-7_1.

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Takahashi, Edwin A. "How to Identify a T1-Weighted Image from a T2-Weighted Image?" In Essential Radiology Review. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26044-6_164.

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Kwon, Junmo, Sang Won Seo, and Hyunjin Park. "Anatomically-Guided Segmentation of Cerebral Microbleeds in T1-Weighted and T2*-Weighted MRI." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72069-7_3.

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Teixeira, João F., and Hélder P. Oliveira. "Spacial Aliasing Artefact Detection on T1-Weighted MRI Images." In Pattern Recognition and Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_51.

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Mardal, Kent-André, Marie E. Rognes, Travis B. Thompson, and Lars Magnus Valnes. "Getting started: from T1 images to simulation." In Mathematical Modeling of the Human Brain. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95136-8_3.

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AbstractThe goal of this chapter is to outline how to perform a numerical simulation of a brain region defined from structural MR images. To address this challenge, we first demonstrate how to generate a high quality mesh of a brain hemisphere from T1-weighted MR images using the tools introduced in Chapter 2. Next, we show how to define a finite element discretization of the diffusion equation (1.1) over this mesh to simulate the influx of an injected tracer.
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Triulzi, F., G. Scotti, L. Beccaria, E. Bianchini, E. Corbella, and C. Bianchi. "Anterior pituitary hyperintensity on T1-weighted MRI in the newborn." In Proceedings of the XIV Symposium Neuroradiologicum. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-49329-4_88.

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Pölsterl, Sebastian, Benjamín Gutiérrez-Becker, Ignacio Sarasua, Abhijit Guha Roy, and Christian Wachinger. "Prediction of Fluid Intelligence from T1-Weighted Magnetic Resonance Images." In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31901-4_5.

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Conference papers on the topic "T1-weighted"

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Tran, Nicole, Anisa V. Prasad, Yan Zhuang, et al. "Benchmarking multiorgan segmentation tools for multiparametric T1-weighted abdominal MRI." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3048938.

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Sun, Hang, Xueying Wang, and Hong Li. "Use of T1-weighted MRI in the Quantitative Feature Analysis of Breast Lesions." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691510.

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Yoon, Jongyeon, Elyssa M. McMaster, Chloe Cho, Kurt G. Schilling, Bennett A. Landman, and Daniel Moyer. "Tractography enhancement in clinically-feasible diffusion MRI using T1-weighted MRI and anatomical context." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047112.

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Aghdam, Maryam Akhavan, Serdar Bozdag, and Fahad Saeed. "Pvtad: Alzheimer’s Disease Diagnosis Using Pyramid Vision Transformer Applied to White Matter of T1-Weighted Structural Mri Data." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635541.

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Fu, Guanghui, Lucia Nichelli, Dario Herran, et al. "Comparing foundation models and nnU-Net for segmentation of primary brain lymphoma on clinical routine post-contrast T1-weighted MRI." In Clinical and Biomedical Imaging, edited by Barjor S. Gimi and Andrzej Krol. SPIE, 2025. https://doi.org/10.1117/12.3044679.

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Li, Xiaoxing, and Christopher Wyatt. "Brain segmentation performance using T1-weighted images versus T1 maps." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.844278.

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Pan, Kai, Pujin Cheng, Ziqi Huang, Li Lin, and Xiaoying Tang. "Transformer-Based T2-weighted MRI Synthesis from T1-weighted Images." In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871183.

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Ping-Feng Chen, R. Grant Steen, Anthony Yezzi, and Hamid Krim. "Brain MRI T1-Map and T1-weighted image segmentation in a variational framework." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959609.

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Liu, Jian-lei. "The approach of T1 weighted brain MRI image segmentation." In 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895763.

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Somasundaram, K., and T. Kalaiselvi. "Brain extraction method for T1-weighted magnetic resonance scans." In 2010 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2010. http://dx.doi.org/10.1109/spcom.2010.5560513.

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Reports on the topic "T1-weighted"

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โควาวิสารัช, นงลักษณ์. MRI brain segmentation : โครงการวิจัยโครงการย่อยที่ 4 : รายงานฉบับสมบูรณ์. คณะวิศวกรรมศาสตร์ จุฬาลงกรณ์มหาวิทยาลัย, 2003. https://doi.org/10.58837/chula.res.2003.50.

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Abstract:
ศึกษา ออกแบบเทคนิควิธีการแบ่งส่วนภาพของสมองและพัฒนาโปรแกรมเพื่อแบ่งส่วนภาพของสมองจากชุดภาพถ่าย MRI ของสมองโดยใช้วิธีใช้ค่าขีดแบ่งและวิธี 3D morphological watershed เป็นวิธีแบ่งส่วนแบบอัตโนมัติ โดยวิธีการแบ่งส่วนโดยอัตโนมัติที่ใช้ค่าขีดแบ่งที่พัฒนาขึ้น พัฒนาจากพื้นฐานความรู้ที่ว่าระดับความสว่างของสมองแบบ White matter, gray matter และ CSF ในภาพ T1-weighted MRI ของสมองจะมีความสว่างจากมากไปน้อย ดังนั้นวิธีใช้ค่าขีดแบ่งที่พัฒนานี้จึงให้ผลการแบ่งส่วนสมองที่ดีกับภาพที่มีข้อมูลในลักษณะดังกล่าว ส่วนวิธี 3D morphological watershed เป็นวิธีที่เหมาะจะใช้ภาพที่บริเวณสมองมีความสว่างที่กลมกลืนกันและไม่แตกต่างกันมาก ไม่ควรมีสัญญาณรบกวนและควรเป็นบริเวณที่แยกจากส่วนอื่นๆ อย่างค่อนข้างชัดเจน แต่ข้อมูลภาพ MRI ส่วนมากมีความหลากหลายเนื่องจากปัจจัยต่างๆ เช่น การเลือกวิธีการจัดทำข้อมูลภาพมีหลากหลาย เช่น ทำเป็น T1-weighted หรือ T2-weighted ด้วยเทคนิคย่อยต่างๆ การเลือกความหนาของสไลซ์ รวมถึงลักษณะของเครื่อง MRI เอง ทำให้ผลการแบ่งส่วนสมองจากภาพ MRI แบบอัตโนมัติมีความหลากหลายทั้งดีและไม่ดี ดังนั้น ในงานวิจัยนี้จึงได้จัดทำโปรแกรมเครื่องมือซอฟต์แวร์ให้ผู้ใช้สามารถเลือกแบ่งส่วนภาพด้วยตนเองรวมทั้งมีตัวกระทำการ Preprocessing เช่น การทำ Normalization การทำ Diffusion และ Post processing เช่นตัวกระทำการทางสัณฐานวิทยาต่างๆ เพื่อให้ผู้ใช้สามารถปรับปรุงข้อมูลก่อนและหลังจากการแบ่งส่วนโดยอัตโนมัติได้ด้วย
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