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Diagnostics
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Clinical Advances and Applications in Neuroradiology
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Clinical Advances and Applications in Neuroradiology

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

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 7733

Special Issue Editor

Dr. Houman Sotoudeh

E-Mail Website
Guest Editor
Neuroradiology Section, Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
Interests: neuroradiology; artificial intelligence; radiomics; neuro-oncology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neuroradiology is a rapidly advancing field of medicine that uses imaging techniques to diagnose and treat diseases of the head and neck, brain, spine, and nervous system. Clinical advances in neuroradiology have led to the development of new and improved imaging techniques, such as susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), resting-state and task-based functional MRI (fMRI), perfusion imaging (CT perfusion and MR perfusion), MR spectroscopy, and PET/ molecular imaging. These techniques have allowed neuroradiologists to evaluate the brain and nervous system in greater detail than ever before, which has led to improved diagnosis and treatment of a wide range of neurological disorders. Recently, artificial intelligence (AI) has gained strong momentum in clinical neuroradiology including, workflow optimization, image reconstruction, and AI-assisted diagnosis.

The aim of this Special Issue is to cover these advanced imaging techniques in clinical neuroradiology. We accept original research and reviews in this field.

Dr. Houman Sotoudeh
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

  • neuroradiology
  • advanced imaging
  • susceptibility-weighted imaging (SWI)
  • diffusion tensor imaging (DTI)
  • functional MRI (fMRI)
  • CT perfusion
  • MR perfusion
  • MR spectroscopy
  • PET
  • molecular imaging
  • artificial intelligence

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Published Papers (7 papers)

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12 pages, 1490 KiB  
Article
Compressed SENSitivity Encoding (SENSE): Qualitative and Quantitative Analysis
by Eliseo Picchi, Silvia Minosse, Noemi Pucci, Francesca Di Pietro, Maria Lina Serio, Valentina Ferrazzoli, Valerio Da Ros, Raffaella Giocondo, Francesco Garaci and Francesca Di Giuliano
Diagnostics 2024, 14(15), 1693; https://doi.org/10.3390/diagnostics14151693 - 5 Aug 2024
Viewed by 1103
Abstract
Background. This study aimed to qualitatively and quantitatively evaluate T1-TSE, T2-TSE and 3D FLAIR sequences obtained with and without Compressed-SENSE technique by assessing the contrast (C), the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR). Methods. A total of 142 MRI images were [...] Read more.
Background. This study aimed to qualitatively and quantitatively evaluate T1-TSE, T2-TSE and 3D FLAIR sequences obtained with and without Compressed-SENSE technique by assessing the contrast (C), the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR). Methods. A total of 142 MRI images were acquired: 69 with Compressed-SENSE and 73 without Compressed-SENSE. All the MRI images were contoured, spatially aligned and co-registered using 3D Slicer Software. Two radiologists manually drew 12 regions of interests on three different structures of CNS: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Results. C values were significantly higher in Compressed-SENSE T1-TSE compared to No Compressed-SENSE T1-TSE for three different structures of the CNS. C values were also significantly lower for Compressed-SENSE 3D FLAIR and Compressed-SENSE T2-TSE compared to the corresponding No Compressed-SENSE scans. While CNR values did not significantly differ in GM-WM between Compressed-SENSE and No Compressed-SENSE for the 3D FLAIR and T1-TSE sequences, the differences in GM-CSF and WM-CSF were always statistically significant. Conclusion. Compressed-SENSE for 3D T2 FLAIR, T1w and T2w sequences enables faster MRI acquisition, reducing scan time and maintaining equivalent image quality. Compressed-SENSE is very useful in specific medical conditions where lower SAR levels are required without sacrificing the acquisition of helpful diagnostic sequences. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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12 pages, 9413 KiB  
Communication
High-Resolution Magnetic Resonance Neurography at 7T: A Pilot Study of Hand Innervation
by Pauline C. Guillemin, David Ferreira Branco, Yacine M’Rad, Loan Mattera, Orane Lorton, Gian Franco Piredda, Antoine Klauser, Roberto Martuzzi, Pierre-Alexandre Poletti, Rares Salomir and Sana Boudabbous
Diagnostics 2024, 14(12), 1230; https://doi.org/10.3390/diagnostics14121230 - 12 Jun 2024
Viewed by 1106
Abstract
The emergence of 7T clinical MRI technology has sparked our interest in its ability to discern the complex structures of the hand. Our primary objective was to assess the sensory and motor nerve structures of the hand, specifically nerves and Pacinian corpuscles, with [...] Read more.
The emergence of 7T clinical MRI technology has sparked our interest in its ability to discern the complex structures of the hand. Our primary objective was to assess the sensory and motor nerve structures of the hand, specifically nerves and Pacinian corpuscles, with the dual purpose of aiding diagnostic endeavors and supporting reconstructive surgical procedures. Ethical approval was obtained to carry out 7T MRI scans on a cohort of volunteers. Four volunteers assumed a prone position, with their hands (N = 8) positioned in a “superman” posture. To immobilize and maintain the hand in a strictly horizontal position, it was affixed to a plastic plate. Passive B0 shimming was implemented. Once high-resolution 3D images had been acquired using a multi-transmit head coil, advanced post-processing techniques were used to meticulously delineate the nerve fiber networks and mechanoreceptors. Across all participants, digital nerves were consistently located on the phalanges area, on average, between 2.5 and 3.5 mm beneath the skin, except within flexion folds where the nerve was approximately 1.8 mm from the surface. On the phalanges area, the mean distance from digital nerves to joints was approximately 1.5 mm. The nerves of the fingers were closer to the bone than to the surface of the skin. Furthermore, Pacinian corpuscles exhibited a notable clustering primarily within the metacarpal zone, situated on the palmar aspect. Our study yielded promising results, successfully reconstructing and meticulously describing the anatomy of nerve fibers spanning from the carpus to the digital nerve division, alongside the identification of Pacinian corpuscles, in four healthy volunteers (eight hands). Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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11 pages, 2757 KiB  
Article
Deep Learning-Based High-Resolution Magnetic Resonance Angiography (MRA) Generation Model for 4D Time-Resolved Angiography with Interleaved Stochastic Trajectories (TWIST) MRA in Fast Stroke Imaging
by Bo Kyu Kim, Sung-Hye You, Byungjun Kim and Jae Ho Shin
Diagnostics 2024, 14(11), 1199; https://doi.org/10.3390/diagnostics14111199 - 6 Jun 2024
Viewed by 1208
Abstract
Purpose: The purpose of this study is to improve the qualitative and quantitative image quality of the time-resolved angiography with interleaved stochastic trajectories technique (4D-TWIST-MRA) using deep neural network (DNN)-based MR image reconstruction software. Materials and Methods: A total of 520 consecutive patients [...] Read more.
Purpose: The purpose of this study is to improve the qualitative and quantitative image quality of the time-resolved angiography with interleaved stochastic trajectories technique (4D-TWIST-MRA) using deep neural network (DNN)-based MR image reconstruction software. Materials and Methods: A total of 520 consecutive patients underwent 4D-TWIST-MRA for ischemic stroke or intracranial vessel stenosis evaluation. Four-dimensional DNN-reconstructed MRA (4D-DNR) was generated using commercially available software (SwiftMR v.3.0.0.0, AIRS Medical, Seoul, Republic of Korea). Among those evaluated, 397 (76.3%) patients received concurrent time-of-flight MRA (TOF-MRA) to compare the signal-to-noise ratio (SNR), image quality, noise, sharpness, vascular conspicuity, and degree of venous contamination with a 5-point Likert scale. Two radiologists independently evaluated the detection rate of intracranial aneurysm in TOF-MRA, 4D-TWIST-MRA, and 4D-DNR in separate sessions. The other 123 (23.7%) patients received 4D-TWIST-MRA due to a suspicion of acute ischemic stroke. The confidence level and decision time for large vessel occlusion were evaluated in these patients. Results: In qualitative analysis, 4D-DNR demonstrated better overall image quality, sharpness, vascular conspicuity, and noise reduction compared to 4D-TWIST-MRA. Moreover, 4D-DNR exhibited a higher SNR than 4D-TWIST-MRA. The venous contamination and aneurysm detection rates were not significantly different between the two MRA images. When compared to TOF-MRA, 4D-CE-MRA underestimated the aneurysm size (2.66 ± 0.51 vs. 1.75 ± 0.62, p = 0.029); however, 4D-DNR showed no significant difference in size compared to TOF-MRA (2.66 ± 0.51 vs. 2.10 ± 0.41, p = 0.327). In the diagnosis of large vessel occlusion, 4D-DNR showed a better confidence level and shorter decision time than 4D-TWIST-MRA. Conclusion: DNN reconstruction may improve the qualitative and quantitative image quality of 4D-TWIST-MRA, and also enhance diagnostic performance for intracranial aneurysm and large vessel occlusion. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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13 pages, 3278 KiB  
Article
Prediction of Seropositivity in Suspected Autoimmune Encephalitis by Use of Radiomics: A Radiological Proof-of-Concept Study
by Jacob Stake, Christine Spiekers, Burak Han Akkurt, Walter Heindel, Tobias Brix, Manoj Mannil and Manfred Musigmann
Diagnostics 2024, 14(11), 1070; https://doi.org/10.3390/diagnostics14111070 - 21 May 2024
Viewed by 1216
Abstract
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral [...] Read more.
In this study, we sought to evaluate the capabilities of radiomics and machine learning in predicting seropositivity in patients with suspected autoimmune encephalitis (AE) from MR images obtained at symptom onset. In 83 patients diagnosed with AE between 2011 and 2022, manual bilateral segmentation of the amygdala was performed on pre-contrast T2 images using 3D Slicer open-source software. Our sample of 83 patients contained 43 seropositive and 40 seronegative AE cases. Images were obtained at our tertiary care center and at various secondary care centers in North Rhine-Westphalia, Germany. The sample was randomly split into training data and independent test data. A total of 107 radiomic features were extracted from bilateral regions of interest (ROIs). Automated machine learning (AutoML) was used to identify the most promising machine learning algorithms. Feature selection was performed using recursive feature elimination (RFE) and based on the determination of the most important features. Selected features were used to train various machine learning algorithms on 100 different data partitions. Performance was subsequently evaluated on independent test data. Our radiomics approach was able to predict the presence of autoantibodies in the independent test samples with a mean AUC of 0.90, a mean accuracy of 0.83, a mean sensitivity of 0.84 and a mean specificity of 0.82, with Lasso regression models yielding the most promising results. These results indicate that radiomics-based machine learning could be a promising tool in predicting the presence of autoantibodies in suspected AE patients. Given the implications of seropositivity for definitive diagnosis of suspected AE cases, this may expedite diagnostic workup even before results from specialized laboratory testing can be obtained. Furthermore, in conjunction with recent publications, our results indicate that characterization of AE subtypes by use of radiomics may become possible in the future, potentially allowing physicians to tailor treatment in the spirit of personalized medicine even before laboratory workup is completed. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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19 pages, 2295 KiB  
Article
Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform
by Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine and Houman Sotoudeh
Diagnostics 2024, 14(11), 1066; https://doi.org/10.3390/diagnostics14111066 - 21 May 2024
Viewed by 1105
Abstract
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and [...] Read more.
Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive T1c images, AI segmentation achieved a mean DSC of 81% compared to radiologists’ ranging from 80% to 86%. Sensitivity and specificity for T1c AI were 91.6% and 97.5%, respectively. The FLAIR AI exhibited a mean DSC of 90% with a 95% CI interval of 87% to 92%, comparable to the radiologists’ scores. It also achieved a mean DSC of 78% for true positive FLAIR slices versus radiologists’ scores of 75% to 83% and recorded a median sensitivity and specificity of 92.1% and 96.1%, respectively. The T1C and FLAIR AI models produced mean Hausdorff distances (<5 mm), volume measurements, kappa scores, and Bland–Altman differences that align closely with those measured by radiologists. Moreover, the inter-user variability between radiologists using the smart manual contouring platform was under 5% for T1c and under 10% for FLAIR images. These results underscore the MRIMath© platform’s low inter-user variability and the high accuracy of its T1c and FLAIR AI models. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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4 pages, 3217 KiB  
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Symptomatic Pneumorrhachis from Bronchial-Subarachnoid Fistula
by Alexander W. Lipinski, Mathew V. Smith, Eric J. Wannamaker and Vincent M. Timpone
Diagnostics 2024, 14(19), 2170; https://doi.org/10.3390/diagnostics14192170 - 29 Sep 2024
Viewed by 583
Abstract
Bronchial-subarachnoid fistulas are rare occurrences, which are not well defined in the literature. This uncommon clinical phenomenon may result in symptomatic pneumorrhachis and presents unique clinical challenges. This report details a case of a 53-year-old female whose treatment for recurrent chondrosarcoma of the [...] Read more.
Bronchial-subarachnoid fistulas are rare occurrences, which are not well defined in the literature. This uncommon clinical phenomenon may result in symptomatic pneumorrhachis and presents unique clinical challenges. This report details a case of a 53-year-old female whose treatment for recurrent chondrosarcoma of the thoracic spine included multiple surgeries and radiotherapy. Two weeks after her most recent debulking surgery, she experienced a rapid onset of unusual symptoms, including headache, back and neck spasms, bladder incontinence, and confusion. The source of her symptoms was found to be secondary to pneumorrhachis from a pre-existing bronchial-pleural fistula that had fistulized to the subarachnoid space discovered on computed tomography (CT) and confirmed intraoperatively. The patient was treated successfully with high-flow oxygen therapy and bed rest, followed by surgical correction of both a pleural air leak and a dural defect with muscular flaps. The patient was discharged home in stable condition and remained clinically free of recurrent bronchial-subarachnoid fistula six months after surgical repair. This case contributes to the existing literature by providing detailed clinical insights into the diagnosis and successful management of a bronchial-subarachnoid fistula leading to pneumorrhachis, thereby highlighting the importance of early recognition and intervention and underscoring the need for further research in this area. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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7 pages, 1646 KiB  
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Rare Complications of CSF Diversion: Paradoxical Neuroimaging Findings in a Double, Chiasmic Case Report
by Gianfranco Di Salle, Gianmichele Migaleddu, Silvia Canovetti, Gaetano Liberti, Paolo Perrini and Mirco Cosottini
Diagnostics 2024, 14(11), 1141; https://doi.org/10.3390/diagnostics14111141 - 30 May 2024
Viewed by 569
Abstract
Two patients with CSF shunting systems exhibited symptoms of altered intracranial pressure. Initial neuroimaging led to misinterpretation, but integrating clinical history and follow-up imaging revealed the true diagnosis. In the first case, reduced ventricular size was mistaken for CSF overdrainage, while the actual [...] Read more.
Two patients with CSF shunting systems exhibited symptoms of altered intracranial pressure. Initial neuroimaging led to misinterpretation, but integrating clinical history and follow-up imaging revealed the true diagnosis. In the first case, reduced ventricular size was mistaken for CSF overdrainage, while the actual problem was increased intracranial pressure, as seen in slit ventricle syndrome. In the second case, symptoms attributed to intracranial hypertension were due to CSF overdrainage causing tonsillar displacement and hydrocephalus. Adjusting the spinoperitoneal shunt pressure resolved symptoms and imaging abnormalities. These cases highlight the necessity of correlating clinical presentation with a deep understanding of CSF dynamics in shunt assessments. Full article
(This article belongs to the Special Issue Clinical Advances and Applications in Neuroradiology)
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