Advancements in Neuroimaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (16 December 2022) | Viewed by 49792

Special Issue Editor


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Guest Editor
Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
Interests: neuroscience; neuroimaging; artificial intelligence in medicine; EEG, MEG, and fMRI; brain–computer interfaces; neurodegenerative diseases; neurorehabilitation
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Special Issue Information

Dear Colleagues,

The active development of neuroimaging systems, especially non-invasive ones (EEG, MEG, fMRI, PET, fNIRS, and others), makes it possible to obtain various information about the processes occurring in the human brain, both in a normal state and in pathologies. In particular, information on hemodynamics, electrical and magnetic activity, and structural features of the brain is available. The development of computer systems for storing and processing the obtained data has led to the accumulation of a massive amount of biomedical data (Big Data) on the functioning of the human brain. Consequently, the development and application of modern methods for analyzing neuroimaging Big Data to identify patterns in these data (for example, biomarkers of developing neurodegenerative or neurological diseases or patterns associated with a specific cognitive activity of a person) are hot topics in neuroscience and neuroimaging. Here, interdisciplinary approaches based on the principles of information theory, the methods of statistics, machine learning, nonlinear dynamics, and the theory of complex networks seem to be especially promising. Such methods are in demand for the development of brain–computer interfaces, clinical decision support systems, and the diagnostics of various brain diseases and disorders. Thus, the Special Issue on “Advancements in Neuroimaging” aims to attract high-quality research studies from scientists and specialists that advance the application of state-of-the-art neuroimaging techniques and post-processing methods in scientific and clinical contexts.

Potential areas of interest include but are not limited to the following directions:

  • New and substantive developments in neuroimage acquisition techniques of various modalities;
  • Innovative methods for neuroimaging data and biomedical Big Data processing and analysis;
  • Development of brain–computer interfaces and clinical decision support systems.

The speakers' submissions of "Baltic Forum: Neuroscience, Artificial Intelligence and Complex Systems" and other accepted manuscripts contributing to our objective for advancements in neuroimaging are strongly welcomed for the Special Issue.

Dr. Semen A. Kurkin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Neuroscience;
  • Neuroimaging techniques;
  • EEG analysis;
  • MEG analysis;
  • Functional near-infrared spectroscopy (fNIRS);
  • Functional magnetic resonance imaging (fMRI);
  • Brain–computer interfaces;
  • Clinical decision support systems;
  • Brain diseases;
  • Neurorehabilitation;
  • Brain functional networks;
  • Biomedical Big Data;
  • Machine learning methods.

Published Papers (19 papers)

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15 pages, 1927 KiB  
Article
Deep Learning-Based Auto-Segmentation of Spinal Cord Internal Structure of Diffusion Tensor Imaging in Cervical Spondylotic Myelopathy
by Ningbo Fei, Guangsheng Li, Xuxiang Wang, Junpeng Li, Xiaosong Hu and Yong Hu
Diagnostics 2023, 13(5), 817; https://doi.org/10.3390/diagnostics13050817 - 21 Feb 2023
Cited by 2 | Viewed by 1889
Abstract
Cervical spondylotic myelopathy (CSM) is a chronic disorder of the spinal cord. ROI-based features on diffusion tensor imaging (DTI) provide additional information about spinal cord status, which would benefit the diagnosis and prognosis of CSM. However, the manual extraction of the DTI-related features [...] Read more.
Cervical spondylotic myelopathy (CSM) is a chronic disorder of the spinal cord. ROI-based features on diffusion tensor imaging (DTI) provide additional information about spinal cord status, which would benefit the diagnosis and prognosis of CSM. However, the manual extraction of the DTI-related features on multiple ROIs is time-consuming and laborious. In total, 1159 slices at cervical levels from 89 CSM patients were analyzed, and corresponding fractional anisotropy (FA) maps were calculated. Eight ROIs were drawn, covering both sides of lateral, dorsal, ventral, and gray matter. The UNet model was trained with the proposed heatmap distance loss for auto-segmentation. Mean Dice coefficients on the test dataset for dorsal, lateral, and ventral column and gray matter were 0.69, 0.67, 0.57, 0.54 on the left side and 0.68, 0.67, 0.59, 0.55 on the right side. The ROI-based mean FA value based on segmentation model strongly correlated with the value based on manual drawing. The percentages of the mean absolute error between the two values of multiple ROIs were 0.07, 0.07, 0.11, and 0.08 on the left side and 0.07, 0.1, 0.1, 0.11, and 0.07 on the right side. The proposed segmentation model has the potential to offer a more detailed spinal cord segmentation and would be beneficial for quantifying a more detailed status of the cervical spinal cord. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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11 pages, 1017 KiB  
Article
Accuracy of Dose-Saving Artificial-Intelligence-Based 3D Angiography (3DA) for Grading of Intracranial Artery Stenoses: Preliminary Findings
by Stefan Lang, Philip Hoelter, Manuel Alexander Schmidt, Anne Mrochen, Joji Kuramatsu, Christian Kaethner, Philipp Roser, Markus Kowarschik and Arnd Doerfler
Diagnostics 2023, 13(4), 712; https://doi.org/10.3390/diagnostics13040712 - 14 Feb 2023
Cited by 2 | Viewed by 2192
Abstract
Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to [...] Read more.
Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to cut the patient dose by 50%. The object was to evaluate 3DA’s diagnostic value for visualization of intracranial artery stenoses (IAS) compared to 3D-DSA. Materials and methods: 3D-DSA datasets of IAS (nIAS = 10) were postprocessed using conventional and prototype software (Siemens Healthineers AG, Erlangen, Germany). Matching reconstructions were assessed by two experienced neuroradiologists in consensus reading, considering image quality (IQ), vessel diameters (VD1/2), vessel-geometry index (VGI = VD1/VD2), and specific qualitative/quantitative parameters of IAS (e.g., location, visual IAS grading [low-/medium-/high-grade] and intra-/poststenotic diameters [dintra-/poststenotic in mm]). Using the NASCET criteria, the percentual degree of luminal restriction was calculated. Results: In total, 20 angiographic 3D volumes (n3DA = 10; n3D-DSA = 10) were successfully reconstructed with equivalent IQ. Assessment of the vessel geometry in 3DA datasets did not differ significantly from 3D-DSA (VD1: r = 0.994, p = 0.0001; VD2:r = 0.994, p = 0.0001; VGI: r = 0.899, p = 0.0001). Qualitative analysis of IAS location (3DA/3D-DSA:nICA/C4 = 1, nICA/C7 = 1, nMCA/M1 = 4, nVA/V4 = 2, nBA = 2) and the visual IAS grading (3DA/3D-DSA:nlow-grade = 3, nmedium-grade = 5, nhigh-grade = 2) revealed identical results for 3DA and 3D-DSA, respectively. Quantitative IAS assessment showed a strong correlation regarding intra-/poststenotic diameters (rdintrastenotic = 0.995, pdintrastenotic = 0.0001; rdpoststenotic = 0.995, pdpoststenotic = 0.0001) and the percentual degree of luminal restriction (rNASCET 3DA = 0.981; pNASCET 3DA = 0.0001). Conclusions: The AI-based 3DA is a resilient algorithm for the visualization of IAS and shows comparable results to 3D-DSA. Hence, 3DA is a promising new method that allows a considerable patient-dose reduction, and its clinical implementation would be highly desirable. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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15 pages, 831 KiB  
Article
Utilisation of 3D Printing in the Manufacturing of an Anthropomorphic Paediatric Head Phantom for the Optimisation of Scanning Parameters in CT
by Merim Jusufbegović, Adi Pandžić, Mustafa Busuladžić, Lejla M. Čiva, Azra Gazibegović-Busuladžić, Adnan Šehić, Sandra Vegar-Zubović, Rahima Jašić and Adnan Beganović
Diagnostics 2023, 13(2), 328; https://doi.org/10.3390/diagnostics13020328 - 16 Jan 2023
Cited by 2 | Viewed by 2289
Abstract
Computed tomography (CT) is a diagnostic imaging process that uses ionising radiation to obtain information about the interior anatomic structure of the human body. Considering that the medical use of ionising radiation implies exposing patients to radiation that may lead to unwanted stochastic [...] Read more.
Computed tomography (CT) is a diagnostic imaging process that uses ionising radiation to obtain information about the interior anatomic structure of the human body. Considering that the medical use of ionising radiation implies exposing patients to radiation that may lead to unwanted stochastic effects and that those effects are less probable at lower doses, optimising imaging protocols is of great importance. In this paper, we used an assembled 3D-printed infant head phantom and matched its image quality parameters with those obtained for a commercially available adult head phantom using the imaging protocol dedicated for adult patients. In accordance with the results, an optimised scanning protocol was designed which resulted in dose reductions for paediatric patients while keeping image quality at an adequate level. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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12 pages, 7901 KiB  
Article
Reduction in Radiation Exposure of CT Perfusion by Optimized Imaging Timing Using Temporal Information of the Preceding CT Angiography of the Carotid Artery in the Stroke Protocol
by Zsuzsanna Deak, Lara Schuettoff, Ann-Kathrin Lohse, Matthias Fabritius, Paul Reidler, Robert Forbrig, Wolfgang Kunz, Konstantin Dimitriadis, Jens Ricke and Bastian Sabel
Diagnostics 2022, 12(11), 2853; https://doi.org/10.3390/diagnostics12112853 - 18 Nov 2022
Viewed by 2912
Abstract
(1) Background: CT perfusion (CTP) is a fast, robust and widely available but dose-exposing imaging technique for infarct core and penumbra detection. Carotid CT angiography (CTA) can precede CTP in the stroke protocol. Temporal information of the bolus tracking series of CTA could [...] Read more.
(1) Background: CT perfusion (CTP) is a fast, robust and widely available but dose-exposing imaging technique for infarct core and penumbra detection. Carotid CT angiography (CTA) can precede CTP in the stroke protocol. Temporal information of the bolus tracking series of CTA could allow for better timing and a decreased number of scans in CTP, resulting in less radiation exposure, if the shortening of CTP does not alter the calculated infarct core and penumbra or the resulting perfusion maps, which are essential for further treatment decisions. (2) Methods: 66 consecutive patients with ischemic stroke proven by follow-up imaging or endovascular intervention were included in this retrospective study approved by the local ethics committee. In each case, six simulated, stepwise shortened CTP examinations were compared with the original data regarding the perfusion maps, infarct core, penumbra and endovascular treatment decision. (3) Results: In simulated CTPs with 26, 28 and 30 scans, the infarct core, penumbra and PRR values were equivalent, and the resulting clinical decision was identical to the original CTP. (4) Conclusions: The temporal information of the bolus tracking series of the carotid CTA can allow for better timing and a lower radiation exposure by eliminating unnecessary scans in CTP. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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10 pages, 1007 KiB  
Article
Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography
by Jan Borggrefe, Max Philipp Gebest, Myriam Hauger, Daniel Ruess, Anastasios Mpotsaris, Christoph Kabbasch, Lenhard Pennig, Kai Roman Laukamp, Lukas Goertz, Jan Robert Kroeger and Jonas Doerner
Diagnostics 2022, 12(10), 2494; https://doi.org/10.3390/diagnostics12102494 - 15 Oct 2022
Cited by 1 | Viewed by 1660
Abstract
Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans [...] Read more.
Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans conducted prior to the stereotactic needle biopsy of untreated primary brain tumor lesions. Attenuation of contrast-enhancing BT was derived from polyenergetic images as well as spectral iodine density maps (IDM) and their contrast-to-noise-ratios (CNR) were determined using ROI measures in contrast-enhancing BT and healthy contralateral white matter. The measures were correlated to histopathology regarding tumor entity, isocitrate dehydrogenase (IDH) and MGMT mutation status. Results: The cohort included 52 female and 76 male patients, mean age of 59.4 (±17.1) years. Brain lymphomas showed the highest attenuation (IDM CNR 3.28 ± 1,23), significantly higher than glioblastoma (2.37 ± 1.55, p < 0.005) and metastases (1.95 ± 1.14, p < 0.02), while the differences between glioblastomas and metastases were not significant. These strongly enhancing lesions differed from oligodendroglioma and astrocytoma (Grade II and III) that showed IDM CNR in the range of 1.22–1.27 (±0.45–0.82). Conventional attenuation measurements in DLCT data performed equally or slightly superior to iodine density measurements. Conclusion: Quantitative attenuation and iodine density measurements of contrast-enhancing brain tumors are feasible imaging biomarkers for the discrimination of cerebral tumor lesions but not specifically for single tumor entities. CNR based on simple HU measurements performed equally or slightly superior to iodine quantification. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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19 pages, 8853 KiB  
Article
Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis
by Amir Hosein Riazi, Hossein Rabbani and Rahele Kafieh
Diagnostics 2022, 12(9), 2263; https://doi.org/10.3390/diagnostics12092263 - 19 Sep 2022
Viewed by 2282
Abstract
Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between [...] Read more.
Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between different brain regions. In recent years, statistical methods, including independent component analysis (ICA) and graph-based analysis, have been widely used in fMRI studies. Furthermore, topological properties of the brain have been appeared as significant features of neuroscience studies. Most studies are focused on graph analysis and ICA methods, rather than considering spectral approaches. Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and healthy controls (HCs). For this purpose, a spectral ICA method is proposed to extract the nodes of the brain graph. Spectral ICA extracts more reliable components and decreases the processing time in calculation of the static brain connectivity. Compared to Infomax ICA, dynamic range and low-frequency to high-frequency power ratio (fALFF) show better results using the proposed ICA. It is also helpful in selection of the states for dynamic connectivity. Furthermore, the dynamic connectivity-based extracted components from spectral ICA are estimated using a mutual information method and based on correlation of sliding time-windowed on selected IC time courses. First-level and second-level connectivity states are calculated using correlations of connectivity strength between graph nodes (spectral ICA components). Finally, static and dynamic connectivity are analyzed based on correlation nodes percolated by an anatomical automatic labeling (AAL) atlas. Despite static and dynamic connectivity results of AAL correlations not showing any significant changes between MS and HC, our results based on spectral ICA in static and dynamic connectivity showed significantly decreased connectivity in MS patients in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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17 pages, 5075 KiB  
Article
Utility of Diffusion and Magnetization Transfer MRI in Cervical Spondylotic Myelopathy: A Pilot Study
by Hea-Eun Yang, Wan-Tae Kim, Dae-Hyun Kim, Seok-Woo Kim and Woo-Kyoung Yoo
Diagnostics 2022, 12(9), 2090; https://doi.org/10.3390/diagnostics12092090 - 29 Aug 2022
Cited by 6 | Viewed by 2218
Abstract
Diffusion tensor imaging (DTI) and magnetization transfer (MT) magnetic resonance imaging (MRI) can help detect spinal cord pathology, and tract-specific analysis of their parameters, such as fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), radial diffusivity (RD) and MT ratio (MTR), can give [...] Read more.
Diffusion tensor imaging (DTI) and magnetization transfer (MT) magnetic resonance imaging (MRI) can help detect spinal cord pathology, and tract-specific analysis of their parameters, such as fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), radial diffusivity (RD) and MT ratio (MTR), can give microstructural information. We performed the tract-based acquisition of MR parameters of three major motor tracts: the lateral corticospinal (CS), rubrospinal (RuS) tract, and lateral reticulospinal (RS) tract as well as two major sensory tracts, i.e., the fasciculus cuneatus (FC) and spinal lemniscus, to detect pathologic change and find correlations with clinical items. MR parameters were extracted for each tract at three levels: the most compressed lesion level and above and below the lesion. We compared the MR parameters of eight cervical spondylotic myelopathy patients and 12 normal controls and analyzed the correlation between clinical evaluation items and MR parameters in patients. RuS and lateral RS showed worse DTI parameters at the lesion level in patients compared to the controls. Worse DTI parameters in those tracts were correlated with weaker power grasp at the lesion level. FC and lateral CS showed a correlation between higher RD and lower FA and MTR with a weaker lateral pinch below the lesion level. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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18 pages, 1049 KiB  
Article
Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion
by Zhongyang Wang, Junchang Xin, Huixian Shen, Qi Chen, Zhiqiong Wang and Xinlei Wang
Diagnostics 2022, 12(8), 2013; https://doi.org/10.3390/diagnostics12082013 - 20 Aug 2022
Viewed by 1367
Abstract
As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to [...] Read more.
As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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11 pages, 2075 KiB  
Article
Diagnostic Performance for Differential Diagnosis of Atypical Parkinsonian Syndromes from Parkinson’s Disease Using Quantitative Indices of 18F-FP-CIT PET/CT
by Miju Cheon, Seung Min Kim, Sang-Won Ha, Min Ju Kang, Hea-Eun Yang and Jang Yoo
Diagnostics 2022, 12(6), 1402; https://doi.org/10.3390/diagnostics12061402 - 6 Jun 2022
Cited by 5 | Viewed by 2346
Abstract
We are aimed to evaluate the diagnostic performances of quantitative indices obtained from dual-phase 18F-FP-CIT PET/CT for differential diagnosis of atypical parkinsonian syndromes (APS) from Parkinson’s disease (PD). We analyzed 172 subjects, including 105 non-Parkinsonism, 26 PD, 8 PSP, 1 CBD, 8 [...] Read more.
We are aimed to evaluate the diagnostic performances of quantitative indices obtained from dual-phase 18F-FP-CIT PET/CT for differential diagnosis of atypical parkinsonian syndromes (APS) from Parkinson’s disease (PD). We analyzed 172 subjects, including 105 non-Parkinsonism, 26 PD, 8 PSP, 1 CBD, 8 MSA-P, 9 MSA-C, and 15 DLB retrospectively. Two sequential PET/CT scans were acquired at 5 min and 3 h. We compared subregional binding potentials, putamen-to-caudate nucleus ratio of the binding potential, asymmetry index, and degree of washout. To differentiate APS, all BPs in both early and late phases (except late BPbrainstem) and all factors of the percent change except for putamen in APS significantly differed from PD. When a cut-off for early BPcerebellum was set as 0.79, the sensitivity, specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy for differentiating APS 73.2%, 91.7%, 93.8%, 66.7%, and 80.0%. The early BPcerebellum showed significantly greater SP and PPV than the late quantitative indices. Combined criteria regarding both early and late indices exhibited only greater NPV. The quantitative indices showed high diagnostic performances in differentiating APS from PD. Our findings provide the dual-phase 18F-FP-CIT PET/CT would be useful for differentiating APS from PD. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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8 pages, 1605 KiB  
Article
Cut-Out Towne-View Whole-Brain 320-Row Four-Dimensional Computed Tomography Angiography for Assessing the Anterior Intracranial Collateral Status: A Retrospective Study
by Takahisa Mori, Toshimitsu Shimizu, Hirobumi Sato and Natsuki Mashikawa
Diagnostics 2022, 12(6), 1336; https://doi.org/10.3390/diagnostics12061336 - 27 May 2022
Viewed by 1908
Abstract
Whole-brain four-dimensional computed tomography angiography (W4D-CTA) using a 320-row area detector CT (320r-ADCT) has been applied before thrombectomy. Endovascular physicians require images with high interrater reliability (IRR) for making appropriate decisions. However, the 320r-ADCT gantry cannot be tilted, and the patient’s head position [...] Read more.
Whole-brain four-dimensional computed tomography angiography (W4D-CTA) using a 320-row area detector CT (320r-ADCT) has been applied before thrombectomy. Endovascular physicians require images with high interrater reliability (IRR) for making appropriate decisions. However, the 320r-ADCT gantry cannot be tilted, and the patient’s head position influences the anteroposterior (AP)-view W4D-CTA images. This study aimed to determine which W4D-CTA images are appropriate pre-thrombectomy, whether the unedited AP view or cut-out Towne view. This study included the W4D-CTA images of acute stroke patients with occlusion of the internal carotid artery or the middle cerebral artery (MCA) from April to July 2021. Images produced by 320r-ADCT were transferred to a workstation. Unedited AP-view images were automatically generated. Towne-view images were cut out for this study. Collateral status was evaluated as poor, intermediate, or good based on the visualization of the MCA peripheral branches. In addition, the IRR was assessed using intraclass correlation coefficients (ICC) (2,1). Fifteen patients were analyzed. In the unedited AP-view and cut-out Towne-view W4D-CTA images, the ICC (2,1) were 0.147 and 0.796, respectively. Cut-out Towne-view W4D-CTA images with substantial IRR are superior to the unedited AP-view images for assessing the anterior intracranial collateral status. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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14 pages, 4357 KiB  
Article
Clinical Evaluation of an Innovative Metal-Artifact-Reduction Algorithm in FD-CT Angiography in Cerebral Aneurysms Treated by Endovascular Coiling or Surgical Clipping
by Felix Eisenhut, Manuel Alexander Schmidt, Alexander Kalik, Tobias Struffert, Julian Feulner, Sven-Martin Schlaffer, Michael Manhart, Arnd Doerfler and Stefan Lang
Diagnostics 2022, 12(5), 1140; https://doi.org/10.3390/diagnostics12051140 - 4 May 2022
Cited by 4 | Viewed by 1817
Abstract
Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to [...] Read more.
Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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14 pages, 1985 KiB  
Article
Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression
by Rositsa Paunova, Sevdalina Kandilarova, Anna Todeva-Radneva, Adeliya Latypova, Ferath Kherif and Drozdstoy Stoyanov
Diagnostics 2022, 12(2), 469; https://doi.org/10.3390/diagnostics12020469 - 12 Feb 2022
Cited by 5 | Viewed by 2451
Abstract
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left [...] Read more.
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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29 pages, 9182 KiB  
Article
Experimental Diagnostics of the Emotional State of Individuals Using External Stimuli and a Model of Neurocognitive Brain Activity
by Alexandr Y. Petukhov, Sofia A. Polevaya and Anna V. Polevaya
Diagnostics 2022, 12(1), 125; https://doi.org/10.3390/diagnostics12010125 - 6 Jan 2022
Cited by 4 | Viewed by 1707
Abstract
In this paper, we study ways and methods to diagnose the emotional state of individuals using external audiovisual stimuli and heart telemetry tools. We apply a mathematical model of neurocognitive brain activity developed specifically for this study to interpret the experimental scheme and [...] Read more.
In this paper, we study ways and methods to diagnose the emotional state of individuals using external audiovisual stimuli and heart telemetry tools. We apply a mathematical model of neurocognitive brain activity developed specifically for this study to interpret the experimental scheme and its results. This experimental technique is based on monitoring and analyzing the dynamics of heart rate variability (HRV), taking into account the particular context and events occurring around the subject of the study. In addition, we provide a brief description of the theory of information images/representations used for the paradigm and interpretation of the experiment. For this study, we viewed the human mind as a one-dimensional potential hole with finite walls of different sizes and an internal potential barrier modeling the border between consciousness and subconsciousness. We also provided the foundations of the mathematical apparatus for this particular view. This experiment allowed us to identify the characteristic markers of influencing external stimuli, which form a foundation for diagnosing the emotional state of an individual. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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23 pages, 1515 KiB  
Article
MEG-Derived Symptom-Sensitive Biomarkers with Long-Term Test-Retest Reliability
by Don Krieger, Paul Shepard, Ryan Soose, Ava Puccio, Sue Beers, Walter Schneider, Anthony P. Kontos, Michael W. Collins and David O. Okonkwo
Diagnostics 2022, 12(1), 84; https://doi.org/10.3390/diagnostics12010084 - 30 Dec 2021
Cited by 3 | Viewed by 2506
Abstract
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability [...] Read more.
Neuroelectric measures derived from human magnetoencephalographic (MEG) recordings hold promise as aides to diagnosis and treatment monitoring and targeting for chronic sequelae of traumatic brain injury (TBI). This study tests novel MEG-derived regional brain measures of tonic neuroelectric activation for long-term test-retest reliability and sensitivity to symptoms. Resting state MEG recordings were obtained from a normative cohort (CamCAN, baseline: n = 613; mean 16-month follow-up: n = 245) and a chronic symptomatic TBI cohort (TEAM-TBI, baseline: n = 62; mean 6-month follow-up: n = 40). The MEG-derived neuroelectric measures were corrected for the empty-room contribution using a random forest classifier. The mean 16-month correlation between baseline and 16-month follow-up CamCAN measures was 0.67; test-retest reliability was markedly improved in this study compared with previous work. The TEAM-TBI cohort was screened for depression, somatization, and anxiety with the Brief Symptom Inventory and for insomnia with the Insomnia Severity Index and was assessed via adjudication for six clinical syndromes: chronic pain, psychological health, and oculomotor, vestibular, cognitive, and sleep dysfunction. Linear classifiers constructed from the 136 regional measures from each TEAM-TBI cohort member distinguished those with and without each symptom, p < 0.0003 for each, i.e., the tonic regional neuroelectric measures of activation are sensitive to the presence/absence of these symptoms and clinical syndromes. The novel regional MEG-derived neuroelectric measures obtained and tested in this study demonstrate the necessary and sufficient properties to be clinically useful, i.e., good test-retest reliability, sensitivity to symptoms in each individual, and obtainable using automatic processing without human judgement or intervention. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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12 pages, 1632 KiB  
Article
Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm
by Jakub Jamárik, Lubomír Vojtíšek, Vendula Churová, Tomáš Kašpárek and Daniel Schwarz
Diagnostics 2022, 12(1), 24; https://doi.org/10.3390/diagnostics12010024 - 23 Dec 2021
Cited by 1 | Viewed by 3208
Abstract
Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component [...] Read more.
Pathological changes in the cortical lamina can cause several mental disorders. Visualization of these changes in vivo would enhance their diagnostics. Recently a framework for visualizing cortical structures by magnetic resonance imaging (MRI) has emerged. This is based on mathematical modeling of multi-component T1 relaxation at the sub-voxel level. This work proposes a new approach for their estimation. The approach is validated using simulated data. Sixteen MRI experiments were carried out on healthy volunteers. A modified echo-planar imaging (EPI) sequence was used to acquire 105 individual volumes. Data simulating the images were created, serving as the ground truth. The model was fitted to the data using a modified Trust Region algorithm. In single voxel experiments, the estimation accuracy of the T1 relaxation times depended on the number of optimization starting points and the level of noise. A single starting point resulted in a mean percentage error (MPE) of 6.1%, while 100 starting points resulted in a perfect fit. The MPE was <5% for the signal-to-noise ratio (SNR) ≥ 38 dB. Concerning multiple voxel experiments, the MPE was <5% for all components. Estimation of T1 relaxation times can be achieved using the modified algorithm with MPE < 5%. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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16 pages, 3241 KiB  
Article
A Comparison of Single- and Multiparametric MRI Models for Differentiation of Recurrent Glioblastoma from Treatment-Related Change
by Felix Eisenhut, Tobias Engelhorn, Soheil Arinrad, Sebastian Brandner, Roland Coras, Florian Putz, Rainer Fietkau, Arnd Doerfler and Manuel A. Schmidt
Diagnostics 2021, 11(12), 2281; https://doi.org/10.3390/diagnostics11122281 - 6 Dec 2021
Viewed by 2246
Abstract
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, [...] Read more.
To evaluate single- and multiparametric MRI models to differentiate recurrent glioblastoma (GBM) and treatment-related changes (TRC) in clinical routine imaging. Selective and unselective apparent diffusion coefficient (ADC) and minimum, mean, and maximum cerebral blood volume (CBV) measurements in the lesion were performed. Minimum, mean, and maximum ratiosCBV (CBVlesion to CBVhealthy white matter) were computed. All data were tested for lesion discrimination. A multiparametric model was compiled via multiple logistic regression using data demonstrating significant difference between GBM and TRC and tested for its diagnostic strength in an independent patient cohort. A total of 34 patients (17 patients with recurrent GBM and 17 patients with TRC) were included. ADC measurements showed no significant difference between both entities. All CBV and ratiosCBV measurements were significantly higher in patients with recurrent GBM than TRC. A minimum CBV of 8.5, mean CBV of 116.5, maximum CBV of 327 and ratioCBV minimum of 0.17, ratioCBV mean of 2.26 and ratioCBV maximum of 3.82 were computed as optimal cut-off values. By integrating these parameters in a multiparametric model and testing it in an independent patient cohort, 9 of 10 patients, i.e., 90%, were classified correctly. The multiparametric model further improves radiological discrimination of GBM from TRC in comparison to single-parameter approaches and enables reliable identification of recurrent tumors. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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15 pages, 2452 KiB  
Article
Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
by Gopi Battineni, Mohmmad Amran Hossain, Nalini Chintalapudi, Enea Traini, Venkata Rao Dhulipalla, Mariappan Ramasamy and Francesco Amenta
Diagnostics 2021, 11(11), 2103; https://doi.org/10.3390/diagnostics11112103 - 13 Nov 2021
Cited by 28 | Viewed by 4111
Abstract
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not [...] Read more.
Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Review

Jump to: Research, Other

20 pages, 7531 KiB  
Review
Vessel Wall Magnetic Resonance Imaging in Cerebrovascular Diseases
by Federico Mazzacane, Valentina Mazzoleni, Elisa Scola, Sara Mancini, Ivano Lombardo, Giorgio Busto, Elisa Rognone, Anna Pichiecchio, Alessandro Padovani, Andrea Morotti and Enrico Fainardi
Diagnostics 2022, 12(2), 258; https://doi.org/10.3390/diagnostics12020258 - 20 Jan 2022
Cited by 16 | Viewed by 5036
Abstract
Cerebrovascular diseases are a leading cause of disability and death worldwide. The definition of stroke etiology is mandatory to predict outcome and guide therapeutic decisions. The diagnosis of pathological processes involving intracranial arteries is especially challenging, and the visualization of intracranial arteries’ vessel [...] Read more.
Cerebrovascular diseases are a leading cause of disability and death worldwide. The definition of stroke etiology is mandatory to predict outcome and guide therapeutic decisions. The diagnosis of pathological processes involving intracranial arteries is especially challenging, and the visualization of intracranial arteries’ vessel walls is not possible with routine imaging techniques. Vessel wall magnetic resonance imaging (VW-MRI) uses high-resolution, multiparametric MRI sequences to directly visualize intracranial arteries walls and their pathological alterations, allowing a better characterization of their pathology. VW-MRI demonstrated a wide range of clinical applications in acute cerebrovascular disease. Above all, it can be of great utility in the differential diagnosis of atherosclerotic and non-atherosclerotic intracranial vasculopathies. Additionally, it can be useful in the risk stratification of intracranial atherosclerotic lesions and to assess the risk of rupture of intracranial aneurysms. Recent advances in MRI technology made it more available, but larger studies are still needed to maximize its use in daily clinical practice. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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Other

Jump to: Research, Review

11 pages, 415 KiB  
Protocol
Functional PET Neuroimaging in Consciousness Evaluation: Study Protocol
by Tom Paunet, Denis Mariano-Goulart, Jeremy Deverdun, Emmanuelle Le Bars, Marjolaine Fourcade and Florentin Kucharczak
Diagnostics 2023, 13(12), 2026; https://doi.org/10.3390/diagnostics13122026 - 10 Jun 2023
Viewed by 1225
Abstract
Ensuring a robust and reliable evaluation of coma deepness and prognostication of neurological outcome is challenging. We propose to develop PET neuroimaging as a new diagnostic and prognosis tool for comatose patients using a recently published methodology to perform functional PET (fPET). This [...] Read more.
Ensuring a robust and reliable evaluation of coma deepness and prognostication of neurological outcome is challenging. We propose to develop PET neuroimaging as a new diagnostic and prognosis tool for comatose patients using a recently published methodology to perform functional PET (fPET). This exam permits the quantification of task-specific changes in neuronal metabolism in a single session. The aim of this protocol is to determine whether task-specific changes in glucose metabolism during the acute phase of coma are able to predict recovery at 18 months. Participation will be proposed for all patients coming for a standard PET-CT in our center in order to evaluate global cerebral metabolism during the comatose state. Legally appointed representative consent will be obtained to slightly modify the exam protocol: (1) 18F-fluorodeoxyglucose (18F-FDG) bolus plus continuous infusion instead of a simple bolus and (2) more time under camera to perform dynamic acquisition. Participants will undergo a 55-min fPET session with a 20% bolus + 80% infusion protocol. Two occurrences of three block (5-min rest, 10-min auditory stimulation and 10-min emotional auditory stimulation) will be performed after reaching equilibrium of FDG arterial concentration. We will compare the regional brain metabolism at rest and during the sessions of auditory and emotional auditory stimulation to search for a determinant of coma recovery (18 months of follow-up after the exam). Emotional auditory stimulation should induce an activation of: the auditory cortex, the consciousness areas and the neural circuitry for emotion (function to coma deepness). An activation analysis will be carried out to highlight regional brain activation using dedicated custom-made software based on Python statistical and image processing toolboxes. The association between activation levels and the Coma Recovery Scale-Revisited (CRS-R) will be assessed using multivariate analysis. If successful, the results from this study will help improve coma prognosis evaluation based on the pattern of neuronal metabolism at the onset of the pathology. The study protocol, rationale and methods are described in this paper. Full article
(This article belongs to the Special Issue Advancements in Neuroimaging)
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