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Coronal FLAIR image in a patient diagnosed with normal pressure hydrocephalus. Pronounced dilatation of the lateral ventricles and dilation of the Sylvian fissure (encircled). Typically, there is no sulcal widening at the vertex (arrow). A decreased corpus callosum angle (<80 • ) can distinguish normal pressure hydrocephalus from ex-vacuo venticulomegaly.
Source publication
Background: Differentiating Parkinson’s disease and atypical parkinsonism on clinical parameters is challenging, especially in early disease courses. This is due to large overlap in symptoms and because the so called red flags, i.e. symptoms indicating atypical parkinsonism, have not (fully) developed. Brain MRI can aid to improve the accuracy and...
Context in source publication
Context 1
... with Lewy bodies (DLB) lacks spe- cific diagnostic markers on brain MRI, although mild general atrophy can be seen in some cases. In case of pronounced ventricular dilatation and enlargement of the Sylvian fissure, normal pressure hydrocephalus should be included in the differential diagnosis (Fig. 5). A decreased angle of the corpus callosum in the coronal plane (<80 • ) can distinguish normal pressure hydrocephalus from ex-vacuo ven- ticulomegaly ...
Citations
... The value of brain MRI in this context lies in ruling out structural abnormalities, secondary causes of parkinsonism (i.e., VP and normal pressure hydrocephalus) and identifying changes often seen in atypical parkinsonism, such as MSA and PSP. 57 In the realm of functional neuroimaging, different radiotracers and imaging techniques can access the dopaminergic pathway. Dopamine transporter (DAT) SPECT has largely been used as a reliable test to demonstrate in vivo dopaminergic dysfunction, by using 99m Tc-TRODAT-1 (SPECT-TRO-DAT) transporter, a tracer that is reasonably costly and available. ...
... As the name implies, this technique traces presynaptic ligands and its measurement is a valuable imaging method to differentiate PD from its mimics like ET, dystonic tremor, or functional parkinsonism. 57,58 However, DAT SPECT is not a reliable test to differentiate PD from atypical parkinsonism, since these conditions usually present with pre-synaptic dopaminergic dysfunction. 59 Attempted to use DAT SPECT to distinguish PD from atypical parkinsonism using measurements of tracers at the putamen and caudate are inconclusive so far. ...
... 59 Attempted to use DAT SPECT to distinguish PD from atypical parkinsonism using measurements of tracers at the putamen and caudate are inconclusive so far. 57,58 SPECT-TRODAT has a higher sensitivity and specificity for measuring the decrement of DAT in PD patients when compared with other imaging techniques. ►Figure 1A shows a normal DAT SPECT from a healthy subject, while ►Figure 1B discloses a marked decrease in dopamine receptor binding in a patient with PD. ...
After more than 200 years since its initial description, the clinical diagnosis of Parkinson's disease (PD) remains an often-challenging endeavor, with broad implications that are fundamental for clinical management. Despite major developments in understanding it's pathogenesis, pathological landmarks, non-motor features and potential paraclinical clues, the most accepted diagnostic criteria remain solidly based on a combination of clinical signs. Here, we review this process, discussing its history, clinical criteria, differential diagnoses, ancillary diagnostic testing, and the role of non-motor and pre-motor signs and symptoms.
Resumo
Passados mais de 200 anos desde a sua descrição inicial, o diagnóstico clínico da doença de Parkinson (DP) continua a ser um processo muitas vezes desafiante, com amplas implicações que são fundamentais para o manejo clínico. Apesar dos grandes desenvolvimentos na compreensão da sua patogénese, marcadores patológicos, características não motoras e potenciais pistas paraclínicas, os critérios diagnósticos mais aceitos permanecem solidamente baseados numa combinação de sinais clínicos motores. Aqui, revisamos esse processo, discutindo sua história, critérios clínicos, diagnósticos diferenciais, testes diagnósticos complementares e o papel dos sinais e sintomas não motores e pré-motores.
... In terms of the objective of sophisticated methods, MRI is utilized to evaluate cerebrovascular damage, excluding other common causes of neurological problems. 7 Furthermore, the MRI of the brain provides for the supplementation of the probable diagnosis of the specific AP form. 8 The improvement of MRI has increased our knowledge of the diverse neurobiological alterations, which is predicted to lead to the development of novel neuroimaging technologies. ...
Objectives
To observe the accuracy of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans in evaluating neurological disorders.
Methods
This retrospective research used CT or MRI to diagnose and characterize brain disorders. Patients’ records suffering from neurological disorders were considered eligible for inclusion, regardless of the time of appearance of symptoms, the severity of their symptoms, or their final clinical diagnosis. The exclusion criteria for this study involved patients who did not undergo either a CT or MRI scan. A chi-square test was performed to observe the association between the study variables. A total of 3155 cases were analyzed.
Results
The most prevalent comorbid was dyslipidemia 670 (21.6%) followed by hypertension 548 (17.6%). Overall brain disorders were confirmed in 2426 (77%) patients. It was observed that half of the patients 1543 (48.9%) were diagnosed with stroke. It was found that the accuracy of CT and MRI was 78% and 74% respectively. The association of modalities, patient type, and gender with the confirmation of diseases was not found significant (p=>0.05).
Conclusion
Our study revealed that CT and MRI were accurate by more than 75% and no difference was between both techniques to detect neurological disorders.
... However, for atypical symptoms called red flags [1], brain magnetic resonance imaging (MRI) is essential for diagnosing Parkinsonplus syndromes (P-plus), such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). MRI improves the diagnostic accuracy and can be used for monitoring disease progression [2]. Brain MRI can reveal various features that appear in P-plus but not in PD [2,3,4]. ...
... MRI improves the diagnostic accuracy and can be used for monitoring disease progression [2]. Brain MRI can reveal various features that appear in P-plus but not in PD [2,3,4]. For instance, patients with PSP show marked midbrain atrophy [5], known as the hummingbird sign. ...
Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (n=105) and patients with PD (n=105), multiple systemic atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotating data for DL models, the representative V-Net and UNETR. The Dice scores and area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated. The segmentation times of V-Net and UNETR for the six brain structures per patient were 3.48 +- 0.17 and 48.14 +- 0.97 s, respectively, being at least 300 times faster than FS (15,735 +- 1.07 s). Dice scores of both DL models were sufficiently high (>0.85), and their AUCs for disease classification were superior to that of FS. For classification of normal vs. P-plus and PD vs. multiple systemic atrophy (cerebellar type), the DL models and FS showed AUCs above 0.8. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.
... MRI is a highly flexible technology that enables the measure of very fine details of soft tissues, oblique orientation imaging with two-dimensional (2D) and threedimensional (3D) orientations, and aids in revealing the structural and functional information. Due to its wide clinical applications MRI tool usage has been extended to multiple disciplines of medicines such as cardiovascular, neurology, oncology, gastroenterology, and musculoskeletal structures (Galbán et al., 2017;Meijer et al., 2017;Otero-García et al., 2019;Sotoudeh et al., 2016). This chapter will provide an insight into the basic principles of MRI, as well as components of the equipment, applications, and advancements in the technology, limitations, and future prospects. ...
... So, the brain MRI is applicable to assess cerebrovascular damage to improve the diagnosis with more accuracy and confidentiality. Simultaneously, brain MRI helps in finding the possible or probable diagnosis of a specific form of atypical parkinsonism (Meijer et al., 2017). ...
Considering the global burden of human diseases, it has increasingly become important to have advanced diagnostic tools for the early diagnosis of debilitating and deadly human diseases such as cancer, cardiovascular, neurologic, developmental diseases, and other abnormalities of the human body. Magnetic resonance imaging (MRI) is one of the powerful noninvasive diagnostic imaging tools that have gained wide attention in the era of modern medicine, and it is routinely being used in clinical practice to diagnose myriad of diseases. MRI provides multiparametric information in context with physiologic, anatomic, and functional aspects of the body. MRI basically relies on four fundamental components—primary magnet, gradient system, radiofrequency system, and computer system—that work in tandem to generate the output signals in the form of images to be clinically interpreted. MRI relies on computing the radiofrequency signals that arise from magnetic moments of hydrogen atom (nuclei) that are profusely found in the water or body fluids. However, the human body’s complex architecture involving bones, tendons, and vasculature has limited the scope of MRI due to the decay of resonance signals. To overcome these limitations rapid advancement has been done to evolve the conventional MRI into advanced MRI coupled with other imaging tools, and introduction of contrast agents has added impeccable advantage in improving the resolution. This chapter will describe the basic principles of MRI, contrast agents, and their applications in human health care.
... Note that ADNI-CN and Control can be considered medically equivalent. Furthermore, PD is known to show little or no difference in MRI from healthy cases [34] [35]. The ADNI and PPMI are longitudinal studies that include multiple time points, and the datasets contain multiple scans for each participant. ...
To build a robust and practical content-based image retrieval (CBIR) system applicable to clinical brain MRI databases, we propose a new framework, disease-oriented image embedding with pseudo-scanner standardization (DI-PSS). It consists of two core techniques: data harmonization to absorb differences caused by different scanning environments and an algorithm to generate low-dimensional embeddings suitable for disease classification. Until now, there have been very few studies aimed at CBIR of brain MRI. Even in the harmonization of scanners, which is an important prerequisite technique for CBIR, only a limited number of studies have been conducted on T1-weighted MRI, which has collected a vast amount of clinical data. Recently proposed methods need to correctly estimate the domain (i.e., dataset, scanner) of each data in advance to remove environment-dependent information from low-dimensional embedding, which is not an easy task. With DI-PSS, each brain image is pseudo-transformed into a brain image taken with a given reference scanner. Then, 3D convolutioinal autoencoders (3D-CAE) trained with deep metric learning generate low-dimensional embeddings that better reflect the characteristics of the disease. In this study, DI-PSS reduced the variability of distance in low-dimensional embedding between Alzheimer’s disease (AD) and clinically normal (CN) patients, caused by differences in scanners and datasets, by 15.8-22.6% and 18.0-29.9%, respectively, compared to the baseline. This improved the ability of spectral clustering to classify AD and CN by 6.2% in average accuracy and 10.7% in macro-F1. Our method has the advantage of not requiring difficult domain prediction tasks in advance, and can effectively utilize the big data of T1-weighted MR images. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, it is well suited for application to a large number of legacy MRIs captured in heterogeneous environments.
... If neuroimaging is necessary, MRI is generally preferred over computer tomography (CT) due to the better tissue contrast resolution and sensitivity unless contraindicated [19] (e.g. cardiac implantable electronic devices, metallic intraocular foreign bodies, neurostimulation systems, metallic implants depending on their type and the strength of magnetic field). ...
... The role of structural 1.5T MR imaging in patients with idiopathic PD is so far questionable because of the lack of disease-specific signs [4] and according to the National Institute for Health and Care Excellence (NICE) guidelines should not be used to diagnose PD [23]. However, in clinical practice, brain MRI is usually performed at least once in the course of the disease, although some experts suggest performing a brain MRI only in patients with atypical features that suggest atypical or symptomatic parkinsonism [19]. Additionally, brain MRI plays an important role in excluding treatable causes of parkinsonism (as in clinical vignette 1), which should never be missed. ...
... Based on recent advances in MRI technology, new techniques using high-field (3T) and ultra-high-field (7T) MRI (nigrosome-1 imaging, neuromelanin sensitive MRI) [28] can add value to the diagnosis of PD and APS. Techniques such as diffusion-weighted imaging [DWI], diffusion tensor imaging [DTI], arterial spin labelling perfusion [ASL], functional MRI, which can also be performed on 1.5T MRI scanners, are based on group-wise comparisons, however cannot be used in the diagnostic work-up of individual patients, because of a lack of validated diagnostic criteria [19]. ...
Introduction:
Neuroimaging play an increasingly important role in the diagnosis of parkinsonian syndromes.
Aim of the study:
In this paper, the authors elaborate on the necessity of using magnetic resonance imaging (MRI) in Parkinson's Disease (PD) and its potential role in differential diagnosis versus other neurodegenerative parkinsonian syndromes such as dementia with Lewy bodies, multiple system atrophy, progressive supranuclear palsy and corticobasal syndrome.
State of the art:
The currently known characteristic abnormalities are listed and tabulated, current recommendations are summarised and sample images are provided. As routine MRI scanning in PD remains controversial, the authors' aim is to show the pros and cons in clinical practice. Additionally, the rationale for functional imaging examination, including [123I]-FP-CIT SPECT (DaTSCAN) and [99mTc]- HMPAO-SPECT, [18F]-FDG-PET, [123I]-mIBG-SPECT is discussed.
Clinical vignette:
This paper is accompanied by two illustrative clinical cases in which neuroimaging studies played a key role in diagnosis and further management.
Conclusions:
Neuroimaging can be helpful in differentiating PD from both atypical and symptomatic parkinsonism. Nevertheless, extensive neurological assessment in a majority of PD cases is sufficient to make a diagnosis. A network of specialists in movement disorders should be established in order to enable better, faster and more precise diagnosis of parkinsonism.
... Note that ADNI-CN and Control can be considered medically equivalent. Furthermore, PD is known to show little or no difference in MRI from healthy cases [32] [33]. ...
To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reduction algorithm. Our DI-PSS uses skull stripping and CycleGAN-based image transformations that map to a standard brain followed by transformation into a brain image taken with a given reference scanner. Then, our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acquires a low-dimensional embedding that better reflects the characteristics of the disease. The effectiveness of our proposed framework was tested on the T1-weighted MRIs selected from the Alzheimer's Disease Neuroimaging Initiative and the Parkinson's Progression Markers Initiative. We confirmed that our PSS greatly reduced the variability of low-dimensional embeddings caused by different scanner and datasets. Compared with the baseline condition, our PSS reduced the variability in the distance from Alzheimer's disease (AD) to clinically normal (CN) and Parkinson disease (PD) cases by 15.8-22.6% and 18.0-29.9%, respectively. These properties allow DI-PSS to generate lower dimensional representations that are more amenable to disease classification. In AD and CN classification experiments based on spectral clustering, PSS improved the average accuracy and macro-F1 by 6.2% and 10.7%, respectively. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, we expect that the DI-PSS is suitable for application to a large number of legacy MRIs scanned in heterogeneous environments.
... Brain MRI scanning protocols employed [13]. ...
Progressive supranuclear palsy (PSP), is an atypical parkinsonian condition characterised by neurodegenerative disorders. Due to a high degree of overlap in characterised symptoms, clinically distinguishing PSP and atypical Parkinsonism traits at the onset is difficult. The review illustrates existing statistical measures and techniques that aid in the detection of certain brain atrophies that constitute to PSP and other atypical parkinsonian conditions using MRI image acquisition protocols. Finally, the review proposes machine learning methods with new models for programmed automated diagnosis as well as progressive methodology for identifying sensitive medical image biomarkers, in distinguishing PSP patients from other atypical parkinsonian ailments.
... MRI findings are used to assess cerebrovascular impairment as well as the possible cause of a neurological disorder [9]. An understanding of the multifaceted neurobiological changes causing neurological disorders is likely to expand on account of current advancements in MRI and other imaging modalities. ...
... Moreover, advanced MRI technique, machine learning framework, including extreme learning machine (ELM) along with the genetic algorithm, also had better and accurate diagnosis ability [140][141][142]. An essential review investigates the new MRI techniques, applications, and frequently used biomarkers in Parkinson's disease. ...
