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Takanashi JI, Uetani H. Neuroimaging in acute infection-triggered encephalopathy syndromes. Front Neurosci 2023; 17:1235364. [PMID: 37638320 PMCID: PMC10447893 DOI: 10.3389/fnins.2023.1235364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/21/2023] [Indexed: 08/29/2023] Open
Abstract
Acute encephalopathy associated with infectious diseases occurs frequently in Japanese children (400-700 children/year) and is the most common in infants aged 0-3 years. Acute encephalopathy is classified into several clinicoradiological syndromes; acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) is the most common subtype, followed by clinically mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) and acute necrotizing encephalopathy (ANE). Neuroimaging, especially magnetic resonance imaging (MRI), is useful for the diagnosis, assessment of treatment efficacy, and evaluation of the pathophysiology of encephalopathy syndromes. MRI findings essential for diagnosis include delayed subcortical reduced diffusion (bright tree appearance) for AESD, reversible splenial lesions with homogeneously reduced diffusion for MERS, and symmetric hemorrhagic thalamic lesions for ANE. We reviewed several MRI techniques that have been applied in recent years, including diffusion-weighted imaging for the characterization of cerebral edema, arterial spin labeling for evaluating cerebral perfusion, and magnetic resonance spectroscopy for evaluating metabolic abnormality.
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Affiliation(s)
- Jun-ichi Takanashi
- Department of Pediatrics, Tokyo Women’s Medical University Yachiyo Medical Center, Yachiyo, Japan
| | - Hiroyuki Uetani
- Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Choi SW, Duclos S, Camelo-Piragua S, Chaudhary N, Sukovich J, Hall T, Pandey A, Xu Z. Histotripsy Treatment of Murine Brain and Glioma: Temporal Profile of Magnetic Resonance Imaging and Histological Characteristics Post-treatment. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1882-1891. [PMID: 37277304 DOI: 10.1016/j.ultrasmedbio.2023.05.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 04/26/2023] [Accepted: 05/01/2023] [Indexed: 06/07/2023]
Abstract
OBJECTIVE Currently, there is a knowledge gap in our understanding of the magnetic resonance imaging (MRI) characteristics of brain tumors treated with histotripsy to evaluate treatment response as well as treatment-related injuries. Our aim was to bridge this gap by investigating and correlating MRI with histological analysis after histotripsy treatment of mouse brain with and without brain tumors and evaluating the evolution of the histotripsy ablation zone on MRI over time. METHODS An eight-element, 1 MHz histotripsy transducer with a focal distance of 32.5 mm was used to treat orthotopic glioma-bearing mice and normal mice. The tumor burden at the time of treatment was ∼5 mm3. T2, T2*, T1 and T1-gadolinium (Gd) MR images and histology of the brain were acquired on days 0, 2 and 7 for tumor-bearing mice and days 0, 2, 7, 14, 21 and 28 post-histotripsy for normal mice. RESULTS T2 and T2* sequences most accurately correlated with histotripsy treatment zone. The treatment-induced blood products, T1 along with T2, revealed blood product evolution from oxygenated, de-oxygenated blood and methemoglobin to hemosiderin. And T1-Gd revealed the state of the blood-brain barrier arising from the tumor or histotripsy ablation. Histotripsy leads to minor localized bleeding, which resolves within the first 7 d as evident on hematoxylin and eosin staining. By day 14, the ablation zone could be distinguished only by the macrophage-laden hemosiderin, which resides around the ablation zone, rendering the treated zone hypo-intense on all MR sequences. CONCLUSION These results provide a library of radiological features on MRI sequences correlated to histology, thus allowing for non-invasive evaluation of histotripsy treatment effects in in vivo experiments.
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Affiliation(s)
- Sang Won Choi
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
| | - Sarah Duclos
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | | | - Neeraj Chaudhary
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Sukovich
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Timothy Hall
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Aditya Pandey
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA
| | - Zhen Xu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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Abstract
PURPOSE OF REVIEW Imaging is used in the diagnosis of peripheral and axial disease in juvenile spondyloarthritis (JSpA). Imaging of the joints and entheses in children and adolescents can be challenging for those unfamiliar with the appearance of the maturing skeleton. These differences are key for rheumatologists and radiologists to be aware of. RECENT FINDINGS In youth, skeletal variation during maturation makes the identification of arthritis, enthesitis, and sacroiliitis difficult. A great effort has been put forward to define imaging characteristics seen in healthy children in order to more accurately identify disease. Additionally, there are novel imaging modalities on the horizon that are promising to further differentiate normal physiologic changes versus disease. SUMMARY This review describes the current state of imaging, limitations, and future imaging modalities in youth, with key attention to differences in imaging interpretation of the peripheral joints, entheses, and sacroiliac joint in youth and adults.
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Affiliation(s)
- Hallie A Carol
- Division of Rheumatology, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Pamela F Weiss
- Division of Rheumatology, Department of Pediatrics, Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia and Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Gaughan C, Nasa A, Roman E, Cullinane D, Kelly L, Riaz S, Brady C, Browne C, Sooknarine V, Mosley O, Almulla A, Alsehli A, Kelliher A, Murphy C, O'Hanlon E, Cannon M, Roddy DW. A Pilot Study of Adolescents with Psychotic Experiences: Potential Cerebellar Circuitry Disruption Early Along the Psychosis Spectrum. CEREBELLUM (LONDON, ENGLAND) 2023:10.1007/s12311-023-01579-5. [PMID: 37351730 DOI: 10.1007/s12311-023-01579-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Accepted: 06/13/2023] [Indexed: 06/24/2023]
Abstract
A berrant connectivity in the cerebellum has been found in psychotic conditions such as schizophrenia corresponding with cognitive and motor deficits found in these conditions. Diffusion differences in the superior cerebellar peduncles, the white matter connecting the cerebellar circuitry to the rest of the brain, have also been found in schizophrenia and high-risk states. However, white matter diffusivity in the peduncles in individuals with sub-threshold psychotic experiences (PEs) but not reaching the threshold for a definitive diagnosis remains unstudied. This study investigates the cerebellar peduncles in adolescents with PEs but no formal psychiatric diagnosis.Sixteen adolescents with PEs and 17 age-matched controls recruited from schools underwent High-Angular-Resolution-Diffusion neuroimaging. Following constrained spherical deconvolution whole-brain tractography, the superior, inferior and middle peduncles were isolated and virtually dissected out using ExploreDTI. Differences for macroscopic and microscopic tract metrics were calculated using one-way between-group analyses of covariance controlling for age, sex and estimated Total Intracranial Volume (eTIV). Multiple comparisons were corrected using Bonferroni correction.A decrease in fractional anisotropy was identified in the right (p = 0.045) and left (p = 0.058) superior cerebellar peduncle; however, this did not survive strict Bonferroni multiple comparison correction. There were no differences in volumes or other diffusion metrics in either the middle or inferior peduncles.Our trend level changes in the superior cerebellar peduncle in a non-clinical sample exhibiting psychotic experiences complement similar but more profound changes previously found in ultra-high-risk individuals and those with psychotic disorders. This suggests that superior cerebellar peduncle circuitry perturbations may occur early along in the psychosis spectrum.
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Affiliation(s)
- Caoimhe Gaughan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Anurag Nasa
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Elena Roman
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Dearbhla Cullinane
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Linda Kelly
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Sahar Riaz
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Conan Brady
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Ciaran Browne
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Vitallia Sooknarine
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Olivia Mosley
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Ahmad Almulla
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Assael Alsehli
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Allison Kelliher
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Cian Murphy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Erik O'Hanlon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Darren William Roddy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland.
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Mohamed ASR, Abusaif A, He R, Wahid KA, Salama V, Youssef S, McDonald BA, Naser M, Ding Y, Salzillo TC, AboBakr MA, Wang J, Lai SY, Fuller CD. Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study. Radiother Oncol 2023; 183:109641. [PMID: 36990394 PMCID: PMC10848569 DOI: 10.1016/j.radonc.2023.109641] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023]
Abstract
PURPOSE To determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT). METHODS HNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique. RESULTS Eighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10-3 mm2/s vs. (1.37 ± 0.22) × 10-3 mm2/s, p < 0.0001), while patients with non-CR had no significant increase (p > 0.05). RPA identified GTV-P delta (Δ)ADCmean < 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADCmean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADCmean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p < 0.0001 for both). CONCLUSION ΔADCmean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse.
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Affiliation(s)
- Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Abdelrahman Abusaif
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Renjie He
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Kareem A Wahid
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Vivian Salama
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Sara Youssef
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Brigid A McDonald
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Mohamed Naser
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Yao Ding
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Travis C Salzillo
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Moamen A AboBakr
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Jihong Wang
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA
| | - Stephen Y Lai
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; Department of Head and Neck Surgery, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.
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Rashidi A, Baratto L, Jayapal P, Theruvath AJ, Greene EB, Lu R, Spunt SL, Daldrup-Link HE. Detection of bone marrow metastases in children and young adults with solid cancers with diffusion-weighted MRI. Skeletal Radiol 2023; 52:1179-1192. [PMID: 36441237 PMCID: PMC10757820 DOI: 10.1007/s00256-022-04240-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/19/2022] [Accepted: 11/20/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE To compare the diagnostic accuracy of diffusion-weighted (DW)-MRI with b-values of 50 s/mm2 and 800 s/mm2 for the detection of bone marrow metastases in children and young adults with solid malignancies. METHODS In an institutional review board-approved prospective study, we performed 51 whole-body DW-MRI scans in 19 children and young adults (14 males, 5 females; age range: 1-25 years) with metastasized cancers before (n = 19 scans) and after (n = 32 scans) chemotherapy. Two readers determined the presence of focal bone marrow lesions in 10 anatomical areas. A third reader measured ADC and SNR of focal lesions and normal marrow. Simultaneously acquired 18F-FDG-PET scans served as the standard of reference. Data of b = 50 s/mm2 and 800 s/mm2 images were compared with the Wilcoxon signed-rank test. Inter-reader agreement was evaluated with weighted kappa statistics. RESULTS The SNR of bone marrow metastases was significantly higher compared to normal bone marrow on b = 50 s/mm2 (mean ± SD: 978.436 ± 1239.436 vs. 108.881 ± 109.813, p < 0.001) and b = 800 s/mm2 DW-MRI (499.638 ± 612.721 vs. 86.280 ± 89.120; p < 0.001). On 30 out of 32 post-treatment DW-MRI scans, reconverted marrow demonstrated low signal with low ADC values (0.385 × 10-3 ± 0.168 × 10-3mm2/s). The same number of metastases (556/588; 94.6%; p > 0.99) was detected on b = 50 s/mm2 and 800 s/mm2 images. However, both normal marrow and metastases exhibited low signals on ADC maps, limiting the ability to delineate metastases. The inter-reader agreement was substantial, with a weighted kappa of 0.783 and 0.778, respectively. CONCLUSION Bone marrow metastases in children and young adults can be equally well detected on b = 50 s/mm2 and 800 s/mm2 images, but ADC values can be misleading.
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Affiliation(s)
- Ali Rashidi
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
| | - Lucia Baratto
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
| | - Praveen Jayapal
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
| | - Ashok Joseph Theruvath
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
| | - Elton Benjamin Greene
- Department of Radiology, Pediatric Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, USA
| | - Rong Lu
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA
| | - Sheri L Spunt
- Department of Pediatrics, Hematology/Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Heike E Daldrup-Link
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Pediatrics, Hematology/Oncology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiology, Lucile Packard Children's Hospital, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA, 94305-5654, USA.
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Andrews L, Keller SS, Osman-Farah J, Macerollo A. A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders. Brain Commun 2023; 5:fcad171. [PMID: 37304793 PMCID: PMC10257440 DOI: 10.1093/braincomms/fcad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/13/2023] Open
Abstract
Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.
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Affiliation(s)
- Luke Andrews
- Correspondence to: Luke Andrews The BRAIN Lab, University of Liverpool Cancer Research Centre 200 London Rd, Liverpool L3 9TA, United Kingdom E-mail:
| | - Simon S Keller
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
| | - Jibril Osman-Farah
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| | - Antonella Macerollo
- Correspondence may also be sent to: Antonella Macerollo. The Walton Centre NHS Trust, Lower Lane Liverpool L9 7LJ, United Kingdom E-mail:
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Alghamdi AJ. The Value of Various Post-Processing Modalities of Diffusion Weighted Imaging in the Detection of Multiple Sclerosis. Brain Sci 2023; 13:brainsci13040622. [PMID: 37190587 DOI: 10.3390/brainsci13040622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease’s progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.
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Affiliation(s)
- Ahmad Joman Alghamdi
- Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia
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60
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Peer S, Gopinath R, Saini J, Kumar P, Srinivas D, Nagaraj C. Evaluation of the Diagnostic Performance of F18-Fluorodeoxyglucose-Positron Emission Tomography, Dynamic Susceptibility Contrast Perfusion, and Apparent Diffusion Coefficient in Differentiation between Recurrence of a High-grade Glioma and Radiation Necrosis. Indian J Nucl Med 2023; 38:115-124. [PMID: 37456178 PMCID: PMC10348492 DOI: 10.4103/ijnm.ijnm_73_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/20/2022] [Accepted: 09/06/2022] [Indexed: 07/18/2023] Open
Abstract
Background Differentiation between recurrence of brain tumor and radiation necrosis remains a challenge in current neuro-oncology practice despite recent advances in both radiological and nuclear medicine techniques. Purpose The purpose of this study was to compare the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging, and F18-fluorodeoxyglucose-positron emission tomography (F18-FDG-PET) in the differentiation between the recurrence of a high-grade glioma and radiation necrosis. Materials and Methods Patients with a diagnosis of high-grade glioma (WHO Grades III and IV) who had undergone surgical resection of the tumor followed by radiotherapy with or without chemotherapy were included in the study. DSC perfusion, diffusion-weighted MRI, and PET scan were acquired on a hybrid PET/MRI scanner. For each lesion, early and delayed tumor-to-brain ratio (TBR), early and delayed maximum standardized uptake value (SUVmax), normalized ADC ratio, and normalized relative cerebral blood volume (rCBV) ratio were calculated and the pattern of lesional enhancement was noted. The diagnosis was finalized with either histopathological examination or the characteristics on follow-up imaging. The statistical analysis using the receiver operator characteristic curves was done to determine the diagnostic performance of DSC perfusion, 18-F FDG-PET, and ADC in differentiation between tumor recurrence and radiation necrosis. Results Fifty patients were included in the final analysis, 32 of them being men (64%). A cutoff value of early TBR >0.8 (sensitivity of 100% and specificity of 80%), delayed TBR >0.93 (sensitivity of 92.3% and specificity of 80%), early SUVmax >10.2 (sensitivity of 76.9% and specificity of 80%), delayed SUVmax >13.2 (sensitivity of 61.54% and specificity of 100%), normalized rCBV ratio >1.21 (sensitivity of 100% and specificity of 60%), normalized ADC ratio >1.66 (sensitivity of 38.5% and specificity of 80%), and Grade 3 enhancement (sensitivity of 100% and specificity of 60%) were found to differentiate recurrence from radiation necrosis. Early TBR had the highest accuracy (94.44%), while ADC ratio had the lowest accuracy (50%). A combination of early TBR (cutoff value of 0.8), late TBR (cutoff value of 0.93), and rCBV ratio (cutoff value of 1.21) showed a sensitivity of 100%, specificity of 92.3%, positive predictive value of 88.9%, negative predictive value of 93.7%, and an accuracy of 96.6% in discrimination between radiation necrosis and recurrence of tumor. Conclusion F18-FDG-PET and DSC perfusion can reliably differentiate tumor recurrence from radiation necrosis, with early TBR showing the highest accuracy. ADC demonstrates a low sensitivity, specificity, and accuracy in differentiating radiation necrosis from recurrence. A combination of early TBR, delayed TBR, and rCBV may be more useful in discrimination between radiation necrosis and recurrence of glioma, with this combination showing a better diagnostic performance than individual parameters or any other combination of parameters.
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Affiliation(s)
- Sameer Peer
- Department of Radiodiagnosis, AIIMS, Bathinda, Punjab, India
| | - R. Gopinath
- Department of Neuro Imaging and Interventional Radiology, Bengaluru, Karnataka, India
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology, Bengaluru, Karnataka, India
| | - Pardeep Kumar
- Department of Neuro Imaging and Interventional Radiology, Bengaluru, Karnataka, India
| | | | - Chandana Nagaraj
- Department of Nuclear Medicine, St. Johns National Academy of Health Sciences, Bengaluru, Karnataka, India
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Warner W, Palombo M, Cruz R, Callaghan R, Shemesh N, Jones DK, Dell'Acqua F, Ianus A, Drobnjak I. Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration. Neuroimage 2023; 269:119930. [PMID: 36750150 PMCID: PMC7615244 DOI: 10.1016/j.neuroimage.2023.119930] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/12/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023] Open
Abstract
Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique (Dell'Acqua et al., proc. ISMRM 2019) which provides contrast between areas with restricted diffusion and areas either without restricted diffusion or with length scales too small for characterisation. Hence, it has a potential for informing on pore sizes, in particular the presence of large axon diameters or other cellular structures. TDR employs the signal from two dMRI acquisitions obtained with the same, large, b-value but with different diffusion gradient waveforms. TDR is advantageous as it employs standard acquisition sequences, does not make any assumptions on the underlying tissue structure and does not require any model fitting, avoiding issues related to model degeneracy. This work for the first time introduces and optimises the TDR method in simulation for a range of different tissues and scanner constraints and validates it in a pre-clinical demonstration. We consider both substrates containing cylinders and spherical structures, representing cell soma in tissue. Our results show that contrasting an acquisition with short gradient duration, short diffusion time and high gradient strength with an acquisition with long gradient duration, long diffusion time and low gradient strength, maximises the TDR contrast for a wide range of pore configurations. Additionally, in the presence of Rician noise, computing TDR from a subset (50% or fewer) of the acquired diffusion gradients rather than the entire shell as proposed originally further improves the contrast. In the last part of the work the results are demonstrated experimentally on rat spinal cord. In line with simulations, the experimental data shows that optimised TDR improves the contrast compared to non-optimised TDR. Furthermore, we find a strong correlation between TDR and histology measurements of axon diameter. In conclusion, we find that TDR has great potential and is a very promising alternative (or potentially complement) to model-based approaches for informing on pore sizes and restricted diffusion in general.
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Affiliation(s)
- William Warner
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | | | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Flavio Dell'Acqua
- NatBrainLab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
| | - Ivana Drobnjak
- Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.
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Abele N, Langner S, Felbor U, Lode H, Hosten N. Quantitative Diffusion-Weighted MRI of Neuroblastoma. Cancers (Basel) 2023; 15:cancers15071940. [PMID: 37046600 PMCID: PMC10092990 DOI: 10.3390/cancers15071940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/06/2023] [Accepted: 03/08/2023] [Indexed: 04/14/2023] Open
Abstract
Neuroblastoma is the most common extracranial, malignant, solid tumor found in children. In more than one-third of cases, the tumor is in an advanced stage, with limited resectability. The treatment options include resection, with or without (neo-/) adjuvant therapy, and conservative therapy, the latter even with curative intent. Contrast-enhanced MRI is used for staging and therapy monitoring. Diffusion-weighted imaging (DWI) is often included. DWI allows for a calculation of the apparent diffusion coefficient (ADC) for quantitative assessment. Histological tumor characteristics can be derived from ADC maps. Monitoring the response to treatment is possible using ADC maps, with an increase in ADC values in cases of a response to therapy. Changes in the ADC value precede volume reduction. The usual criteria for determining the response to therapy can therefore be supplemented by ADC values. While these changes have been observed in neuroblastoma, early changes in the ADC value in response to therapy are less well described. In this study, we evaluated whether there is an early change in the ADC values in neuroblastoma under therapy; if this change depends on the form of therapy; and whether this change may serve as a prognostic marker. We retrospectively evaluated neuroblastoma cases treated in our institution between June 2007 and August 2014. The examinations were grouped as 'prestaging'; 'intermediate staging'; 'final staging'; and 'follow-up'. A classification of "progress", "stable disease", or "regress" was made. For the determination of ADC values, regions of interest were drawn along the borders of all tumor manifestations. To calculate ADC changes (∆ADC), the respective MRI of the prestaging was used as a reference point or, in the case of therapies that took place directly after previous therapies, the associated previous staging. In the follow-up examinations, the previous examination was used as a reference point. The ∆ADC were grouped into ∆ADCregress for regressive disease, ∆ADCstable for stable disease, and ∆ADC for progressive disease. In addition, examinations at 60 to 120 days from the baseline were grouped as er∆ADCregress, er∆ADCstable, and er∆ADCprogress. Any differences were tested for significance using the Mann-Whitney test (level of significance: p < 0.05). In total, 34 patients with 40 evaluable tumor manifestations and 121 diffusion-weighted MRI examinations were finally included. Twenty-seven patients had INSS stage IV neuroblastoma, and seven had INSS stage III neuroblastoma. A positive N-Myc expression was found in 11 tumor diseases, and 17 patients tested negative for N-Myc (with six cases having no information). 26 patients were assigned to the high-risk group according to INRG and eight patients to the intermediate-risk group. There was a significant difference in mean ADC values from the high-risk group compared to those from the intermediate-risk group, according to INRG. The differences between the mean ∆ADC values (absolute and percentage) according to the course of the disease were significant: between ∆ADCregress and ∆ADCstable, between ∆ADCprogress and ∆ADCstable, as well as between ∆ADCregress and ∆ADCprogress. The differences between the mean er∆ADC values (absolute and percentage) according to the course of the disease were significant: between er∆ADCregress and er∆ADCstable, as well as between er∆ADCregress and er∆ADCprogress. Forms of therapy, N-Myc status, and risk groups showed no further significant differences in mean ADC values and ∆ADC/er∆ADC. A clear connection between the ADC changes and the response to therapy could be demonstrated. This held true even within the first 120 days after the start of therapy: an increase in the ADC value corresponds to a probable response to therapy, while a decrease predicts progression. Minimal or no changes were seen in cases of stable disease.
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Affiliation(s)
- Niklas Abele
- Department of Radiology, Germany University of Greifswald, 17475 Greifswald, Germany
- Institute of Pathology, University of Erlangen, 91054 Erlangen, Germany
| | - Soenke Langner
- Department of Radiology, Germany University of Greifswald, 17475 Greifswald, Germany
- Department of Radiology, University of Rostock, 18057 Rostock, Germany
| | - Ute Felbor
- Department of Human Genetics, University of Greifswald, 17475 Greifswald, Germany
- Interfaculty Institute of Genetics and Functional Genetics, University of Greifswald, 17475 Greifswald, Germany
| | - Holger Lode
- Department of Pediatric Hematology and Oncology, University of Greifswald, 17475 Greifswald, Germany
| | - Norbert Hosten
- Department of Radiology, Germany University of Greifswald, 17475 Greifswald, Germany
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Guirguis M, Gupta A, Thakur U, Pezeshk P, Weatherall P, Sharan G, Xi Y, Chhabra A. Osseous-Tissue Tumor Reporting and Data System With Diffusion-Weighted Imaging of Bone Tumors-An Interreader Analysis and Whether It Adds Incremental Value on Tumor Grading Over Conventional Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:255-263. [PMID: 36877760 DOI: 10.1097/rct.0000000000001415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
OBJECTIVE The aim of the study is to determine whether the use of diffusion-weighted imaging (DWI) provides incremental increase in performance in the osseous-tissue tumor reporting and data system (OT-RADS) with the hypothesis that use of DWI improves interreader agreement and diagnostic accuracy. METHODS In this multireader cross-sectional validation study, multiple musculoskeletal radiologists reviewed osseous tumors with DW images and apparent diffusion coefficient maps. Four blinded readers categorized each lesion using the OT-RADS categorizations. Intraclass correlation (ICC) and Conger κ were used. Diagnostic performance measures including area under the receiver operating curve were reported. These measures were then compared with the previously published work that validated OT-RADS but did not include incremental value assessment of DWI. RESULTS One hundred thirty-three osseous tumors of the upper and lower extremities (76 benign, 57 malignant) were tested. Interreader agreement for OT-RADS with DWI (ICC = 0.69) was slightly lower (not statistically different) from the previously published work that did not incorporate DWI (ICC = 0.78, P > 0.05). The mean sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating curve including DWI of the 4 readers were 0.80, 0.95, 0.96, 0.79, and 0.91, respectively. In the previously published work without DWI, the mean values of the readers were 0.96, 0.79, 0.78, 0.96, and 0.94, respectively. CONCLUSIONS The addition of DWI to the OT-RADS system does not allow significantly improved area under the curve diagnostic performance measure. Conventional magnetic resonance imaging can be prudently used for OT-RADS for reliable and accurate characterization of bone tumors.
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Affiliation(s)
| | | | | | | | | | | | - Yin Xi
- From the Departments of Radiology
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Thorley N, Jones A, Ciurtin C, Castelino M, Bainbridge A, Abbasi M, Taylor S, Zhang H, Hall-Craggs MA, Bray TJP. Quantitative magnetic resonance imaging (qMRI) in axial spondyloarthritis. Br J Radiol 2023; 96:20220675. [PMID: 36607267 PMCID: PMC10078871 DOI: 10.1259/bjr.20220675] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Imaging, and particularly MRI, plays a crucial role in the assessment of inflammation in rheumatic disease, and forms a core component of the diagnostic pathway in axial spondyloarthritis. However, conventional imaging techniques are limited by image contrast being non-specific to inflammation and a reliance on subjective, qualitative reader interpretation. Quantitative MRI methods offer scope to address these limitations and improve our ability to accurately and precisely detect and characterise inflammation, potentially facilitating a more personalised approach to management. Here, we review quantitative MRI methods and emerging quantitative imaging biomarkers for imaging inflammation in axial spondyloarthritis. We discuss the potential benefits as well as the practical considerations that must be addressed in the movement toward clinical translation of quantitative imaging biomarkers.
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Affiliation(s)
- Natasha Thorley
- Imaging Department, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Alexis Jones
- Department of Rheumatology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Coziana Ciurtin
- Department of Rheumatology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Madhura Castelino
- Department of Rheumatology, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Alan Bainbridge
- Department of Medical Physics, University College London Hospitals, London, United Kingdom
| | - Maaz Abbasi
- Imaging Department, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Stuart Taylor
- Centre for Medical Imaging (CMI), University College London, London, United Kingdom
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom
| | | | - Timothy J P Bray
- Centre for Medical Imaging (CMI), University College London, London, United Kingdom
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65
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Hardy SJ, Finkelstein AJ, Tivarus M, Culakova E, Mohile N, Weber M, Lin E, Zhong J, Usuki K, Schifitto G, Milano M, Janelsins-Benton MC. Cognitive and neuroimaging outcomes in individuals with benign and low-grade brain tumours receiving radiotherapy: a protocol for a prospective cohort study. BMJ Open 2023; 13:e066458. [PMID: 36792323 PMCID: PMC9933762 DOI: 10.1136/bmjopen-2022-066458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 01/27/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION Radiation-induced cognitive decline (RICD) occurs in 50%-90% of adult patients 6 months post-treatment. In patients with low-grade and benign tumours with long expected survival, this is of paramount importance. Despite advances in radiation therapy (RT) treatment delivery, better understanding of structures important for RICD is necessary to improve cognitive outcomes. We hypothesise that RT may affect network topology and microstructural integrity on MRI prior to any gross anatomical or apparent cognitive changes. In this longitudinal cohort study, we aim to determine the effects of RT on brain structural and functional integrity and cognition. METHODS AND ANALYSIS This study will enroll patients with benign and low-grade brain tumours receiving partial brain radiotherapy. Patients will receive either hypofractionated (>2 Gy/fraction) or conventionally fractionated (1.8-2 Gy/fraction) RT. All participants will be followed for 12 months, with MRIs conducted pre-RT and 6-month and 12 month post-RT, along with a battery of neurocognitive tests and questionnaires. The study was initiated in late 2018 and will continue enrolling through 2024 with final follow-ups completing in 2025. The neurocognitive battery assesses visual and verbal memory, attention, executive function, processing speed and emotional cognition. MRI protocols incorporate diffusion tensor imaging and resting state fMRI to assess structural connectivity and functional connectivity, respectively. We will estimate the association between radiation dose, imaging metrics and cognitive outcomes. ETHICS AND DISSEMINATION This study has been approved by the Research Subjects Review Board at the University of Rochester (STUDY00001512: Cognitive changes in patients receiving partial brain radiation). All results will be published in peer-reviewed journals and at scientific conferences. TRIAL REGISTRATION NUMBER ClinicalTrials.gov NCT04390906.
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Affiliation(s)
- Sara J Hardy
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Alan J Finkelstein
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
- Center for Advanced Brain Imaging and Neurophysiology, University of Rochester Medical Center, Rochester, New York, USA
| | - Madalina Tivarus
- Center for Advanced Brain Imaging and Neurophysiology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Eva Culakova
- Department of Surgery, University of Rochester Medical Center, Rochester, New York, USA
| | - Nimish Mohile
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Miriam Weber
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York, USA
| | - Edward Lin
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Jianhui Zhong
- Department of Biomedical Engineering, University of Rochester, Rochester, New York, USA
- Center for Advanced Brain Imaging and Neurophysiology, University of Rochester Medical Center, Rochester, New York, USA
| | - Kenneth Usuki
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
| | - Giovanni Schifitto
- Department of Neurology, Department of Imaging Sciences, University of Rochester Medical Center, Rochester, New York, USA
| | - Michael Milano
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
| | - M C Janelsins-Benton
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York, USA
- Department of Surgery, University of Rochester Medical Center, Rochester, New York, USA
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Cole KL, Findlay MC, Kundu M, Johansen C, Rawanduzy C, Lucke-Wold B. The Role of Advanced Imaging in Neurosurgical Diagnosis. JOURNAL OF MODERN MEDICAL IMAGING 2023; 1:2. [PMID: 36908971 PMCID: PMC10003679 DOI: 10.53964/jmmi.2023002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Neurosurgery as a specialty has developed at a rapid pace as a result of the continual advancements in neuroimaging modalities. With more sophisticated imaging options available to the modern neurosurgeon, diagnoses become more accurate and at a faster rate, allowing for greater surgical planning and precision. Herein, the authors review the current heavily used imaging modalities within neurosurgery, weighing their strengths and weaknesses, and provide a look into new advances and imaging options within the field. Of the many imaging modalities currently available to the practicing neurosurgeon, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasonography (US) are used most heavily within the field for appropriate diagnosis of neuropathologies in question. For each, their strengths are weighed regarding appropriate capabilities in accurate diagnosis of cranial or spinal lesions. Reasoning for choosing one over the other for various pathologies is also reviewed. Current limitations of each is also assessed, providing insight for possible improvement for each. New advancements in imaging options are subsequently reviewed for best uses within neurosurgery, including the new utilization of FIESTA sequencing, glymphatic mapping, black-blood MRI, and functional MRI. The specialty of neurosurgery will continue to heavily rely on improvements within imaging options available for improved diagnosis and greater surgical outcomes for the patients treated. The synthesis of techniques provided herein may provide meaningful guidance for neurosurgeons in effectively diagnosing neurological pathologies while also helping guide future efforts in neuroimaging developments.
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Affiliation(s)
- Kyril L Cole
- School of Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Mrinmoy Kundu
- Institute of Medical Sciences & Sum Hospital, Bhubaneswar, India
| | | | - Cameron Rawanduzy
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
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Lyu J, Li Y, Yan F, Chen W, Wang C, Li R. Multi-channel GAN-based calibration-free diffusion-weighted liver imaging with simultaneous coil sensitivity estimation and reconstruction. Front Oncol 2023; 13:1095637. [PMID: 36845688 PMCID: PMC9945270 DOI: 10.3389/fonc.2023.1095637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/09/2023] [Indexed: 02/10/2023] Open
Abstract
Introduction Diffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging. Methods This study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors. Results The performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model. Discussion The current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.
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Affiliation(s)
- Jun Lyu
- School of Computer and Control Engineering, Yantai University, Yantai, Shandong, China
| | - Yan Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weibo Chen
- Philips Healthcare (China), Shanghai, China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China,*Correspondence: Chengyan Wang, ; Ruokun Li,
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China,*Correspondence: Chengyan Wang, ; Ruokun Li,
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Hnilicova P, Kantorova E, Sutovsky S, Grofik M, Zelenak K, Kurca E, Zilka N, Parvanovova P, Kolisek M. Imaging Methods Applicable in the Diagnostics of Alzheimer's Disease, Considering the Involvement of Insulin Resistance. Int J Mol Sci 2023; 24:ijms24043325. [PMID: 36834741 PMCID: PMC9958721 DOI: 10.3390/ijms24043325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Alzheimer's disease (AD) is an incurable neurodegenerative disease and the most frequently diagnosed type of dementia, characterized by (1) perturbed cerebral perfusion, vasculature, and cortical metabolism; (2) induced proinflammatory processes; and (3) the aggregation of amyloid beta and hyperphosphorylated Tau proteins. Subclinical AD changes are commonly detectable by using radiological and nuclear neuroimaging methods such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). Furthermore, other valuable modalities exist (in particular, structural volumetric, diffusion, perfusion, functional, and metabolic magnetic resonance methods) that can advance the diagnostic algorithm of AD and our understanding of its pathogenesis. Recently, new insights into AD pathoetiology revealed that deranged insulin homeostasis in the brain may play a role in the onset and progression of the disease. AD-related brain insulin resistance is closely linked to systemic insulin homeostasis disorders caused by pancreas and/or liver dysfunction. Indeed, in recent studies, linkages between the development and onset of AD and the liver and/or pancreas have been established. Aside from standard radiological and nuclear neuroimaging methods and clinically fewer common methods of magnetic resonance, this article also discusses the use of new suggestive non-neuronal imaging modalities to assess AD-associated structural changes in the liver and pancreas. Studying these changes might be of great clinical importance because of their possible involvement in AD pathogenesis during the prodromal phase of the disease.
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Affiliation(s)
- Petra Hnilicova
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
- Correspondence: (P.H.); (M.K.)
| | - Ema Kantorova
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Stanislav Sutovsky
- 1st Department of Neurology, Faculty of Medicine, Comenius University in Bratislava and University Hospital, 813 67 Bratislava, Slovakia
| | - Milan Grofik
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Kamil Zelenak
- Clinic of Radiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Egon Kurca
- Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Norbert Zilka
- Institute of Neuroimmunology, Slovak Academy of Sciences, 845 10 Bratislava, Slovakia
| | - Petra Parvanovova
- Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
| | - Martin Kolisek
- Biomedical Center Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 036 01 Martin, Slovakia
- Correspondence: (P.H.); (M.K.)
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Molecular MRI-Based Monitoring of Cancer Immunotherapy Treatment Response. Int J Mol Sci 2023; 24:ijms24043151. [PMID: 36834563 PMCID: PMC9959624 DOI: 10.3390/ijms24043151] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 01/29/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Immunotherapy constitutes a paradigm shift in cancer treatment. Its FDA approval for several indications has yielded improved prognosis for cases where traditional therapy has shown limited efficiency. However, many patients still fail to benefit from this treatment modality, and the exact mechanisms responsible for tumor response are unknown. Noninvasive treatment monitoring is crucial for longitudinal tumor characterization and the early detection of non-responders. While various medical imaging techniques can provide a morphological picture of the lesion and its surrounding tissue, a molecular-oriented imaging approach holds the key to unraveling biological effects that occur much earlier in the immunotherapy timeline. Magnetic resonance imaging (MRI) is a highly versatile imaging modality, where the image contrast can be tailored to emphasize a particular biophysical property of interest using advanced engineering of the imaging pipeline. In this review, recent advances in molecular-MRI based cancer immunotherapy monitoring are described. Next, the presentation of the underlying physics, computational, and biological features are complemented by a critical analysis of the results obtained in preclinical and clinical studies. Finally, emerging artificial intelligence (AI)-based strategies to further distill, quantify, and interpret the image-based molecular MRI information are discussed in terms of perspectives for the future.
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Accelerated Diffusion-Weighted MR Image Reconstruction Using Deep Neural Networks. J Digit Imaging 2023; 36:276-288. [PMID: 36333593 PMCID: PMC9984585 DOI: 10.1007/s10278-022-00709-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/06/2022] Open
Abstract
Under-sampling in diffusion-weighted imaging (DWI) decreases the scan time that helps to reduce off-resonance effects, geometric distortions, and susceptibility artifacts; however, it leads to under-sampling artifacts. In this paper, diffusion-weighted MR image (DWI-MR) reconstruction using deep learning (DWI U-Net) is proposed to recover artifact-free DW images from variable density highly under-sampled k-space data. Additionally, different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, have been investigated to choose the best optimizers for DWI U-Net. The reconstruction results are compared with the conventional Compressed Sensing (CS) reconstruction. The quality of the recovered images is assessed using mean artifact power (AP), mean root mean square error (RMSE), mean structural similarity index measure (SSIM), and mean apparent diffusion coefficient (ADC). The proposed method provides up to 61.1%, 60.0%, 30.4%, and 28.7% improvements in the mean AP value of the reconstructed images in our experiments with different optimizers, i.e., RMSProp, Adam, Adagrad, and Adadelta, respectively, as compared to the conventional CS at an acceleration factor of 6 (i.e., AF = 6). The results of DWI U-Net with the RMSProp, Adam, Adagrad, and Adadelta optimizers show 13.6%, 10.0%, 8.7%, and 8.74% improvements, respectively, in terms of mean SSIM with respect to the conventional CS at AF = 6. Also, the proposed technique shows 51.4%, 29.5%, 24.04%, and 18.0% improvements in terms of mean RMSE using the RMSProp, Adam, Adagrad, and Adadelta optimizers, respectively, with reference to the conventional CS at AF = 6. The results confirm that DWI U-Net performs better than the conventional CS reconstruction. Also, when comparing the different optimizers in DWI U-Net, RMSProp provides better results than the other optimizers.
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Iatrogenic Strokes and Covert Brain Infarcts After Percutaneous Cardiac Procedures: An Update. Can J Cardiol 2023; 39:200-209. [PMID: 36435326 DOI: 10.1016/j.cjca.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
Millions of cardiac procedures are performed worldwide each year, making the potential complication of periprocedural iatrogenic stroke an important concern. These strokes can occur intraoperatively or within 30 days of a procedure and can be categorised as either overt or covert, occurring without obvious acute neurologic symptoms. Understanding the prevalence, risk factors, and strategies for preventing overt and covert strokes associated with cardiac procedures is imperative for reducing periprocedural morbidity and mortality. In this narrative review, we focus on the impacts of perioperative ischemic strokes for several of the most common interventional cardiac procedures, their relevance from a neurologic standpoint, and future directions for the care and research on perioperative strokes. Depending on the percutaneous procedure, the rates of periprocedural overt strokes can range from as little as 0.01% to as high as 2.9%. Meanwhile, covert brain infarctions (CBIs) occur much more frequently, with rates for different procedures ranging from 10%-84%. Risk factors include previous stroke, atherosclerotic disease, carotid stenosis, female sex, and African race, as well as other patient- and procedure-level factors. While the impact of covert brain infarctions is still a developing field, overt strokes for cardiac procedures lead to longer stays in hospital and increased costs. Potential preventative measures include screening and vascular risk factor control, premedicating, and procedural considerations such as the use of cerebral embolic protection devices. In addition, emerging treatments from the neurologic field, including neuroprotective drugs and remote ischemic conditioning, present promising avenues for preventing these strokes and merit investigation in cardiac procedures.
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El-Abtah ME, Murayi R, Lee J, Recinos PF, Kshettry VR. Radiological Differentiation Between Intracranial Meningioma and Solitary Fibrous Tumor/Hemangiopericytoma: A Systematic Literature Review. World Neurosurg 2023; 170:68-83. [PMID: 36403933 DOI: 10.1016/j.wneu.2022.11.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Intracranial solitary fibrous tumor (SFT) is characterized by aggressive local behavior and high post-resection recurrence rates. It is difficult to distinguish between SFT and meningiomas, which are typically benign. The goal of this study was to systematically review radiological features that differentiate meningioma and SFT. METHODS We performed a systematic review in accordance with PRISMA guidelines to identify studies that used imaging techniques to identify radiological differentiators of SFT and meningioma. RESULTS Eighteen studies with 1565 patients (SFT: 662; meningiomas: 903) were included. The most commonly used imaging modality was diffusion weighted imaging, which was reported in 11 studies. Eight studies used a combination of diffusion weighted imaging and T1- and T2-weighted sequences to distinguish between SFT and meningioma. Compared to all grades/subtypes of meningioma, SFT is associated with higher apparent diffusion coefficient, presence of narrow-based dural attachments, lack of dural tail, less peritumoral brain edema, extensive serpentine flow voids, and younger age at initial diagnosis. Tumor volume was a poor differentiator of SFT and meningioma, and overall, there were less consensus findings in studies exclusively comparing angiomatous meningiomas and SFT. CONCLUSIONS Clinicians can differentiate SFT from meningiomas on preoperative imaging by looking for higher apparent diffusion coefficient, lack of dural tail/narrow-based dural attachment, less peritumoral brain edema, and vascular flow voids on neuroimaging, in addition to younger age at diagnosis. Distinguishing between angiomatous meningioma and SFT is much more challenging, as both are highly vascular pathologies. Tumor volume has limited utility in differentiating between SFT and various grades/subtypes of meningioma.
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Affiliation(s)
- Mohamed E El-Abtah
- Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Roger Murayi
- Department of Neurological Surgery, Cleveland Clinic, Cleveland, Ohio, USA
| | - Jonathan Lee
- Department of Radiology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Pablo F Recinos
- Department of Neurological Surgery and Rosa Ella Burkhardt Brain Tumor & Neuro-Oncology Center, Cleveland Clinic, Cleveland, Ohio, USA; Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA
| | - Varun R Kshettry
- Department of Neurological Surgery and Rosa Ella Burkhardt Brain Tumor & Neuro-Oncology Center, Cleveland Clinic, Cleveland, Ohio, USA; Cleveland Clinic Lerner College of Medicine, Cleveland, Ohio, USA.
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A study of the diagnostic efficacy of diffusion-weighted magnetic resonance imaging in the diagnosis of perianal fistula and its complications. Pol J Radiol 2023; 88:e113-e118. [PMID: 36910887 PMCID: PMC9995243 DOI: 10.5114/pjr.2023.125220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 11/21/2022] [Indexed: 02/24/2023] Open
Abstract
Purpose To determine the diagnostic efficacy of diffusion-weighted magnetic resonance imaging (DWI) in the diagnosis of perianal fistula and its complications. Material and methods This is a retrospective study based on the data of 47 patients with a clinical diagnosis of perianal fistula, who had an MRI study performed on a 1.5-T GE Signa MR scanner. DWI sequences were done using 3 different b-values. Other routine MR sequences were included. The MR images were studied to compare the diagnostic efficacy of the DW MRI sequence and other sequences in diagnosing perianal fistula and its complications. Apparent diffusion coefficient (ADC) values of abscesses and inflammatory soft tissue lesions were measured using ADC maps. The standard reference to obtain diagnostic efficacy was post-surgical data. Results Seventy-nine perianal fistulas were diagnosed in 47 patients who had undergone an MRI study. The sensitivity and specificity of different MR sequences in diagnosing perianal fistulas are T2 FSFSE: 92% sensitivity; DWI: 96% sensitivity; combined T2+DWI: 100% sensitivity; and post-gadolinium T1 FS has 100% sensitivity in diagnosing perianal fistulas. The mean apparent diffusion coefficient for the abscess in our study was 0.990 ± 0.05 × 10-3, and the mean apparent diffusion coefficient for an inflammatory soft tissue lesion was 1.440 ± 0.05 × 10-3. The optimal ADC cut-off for the abscess was 1.098 × 10-3 mm2/s showing 100% sensitivity and 93.8% specificity. Conclusions DW imaging is a reliable sequence to diagnose perianal fistula and its complications. Measurement of ADC values is reliable in diagnosing perianal abscess collection. DWI sequence helps patients with renal impairment in whom IV gadolinium is contraindicated.
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Ahmad A, Parker D, Dheer S, Samani ZR, Verma R. 3D-QCNet - A pipeline for automated artifact detection in diffusion MRI images. Comput Med Imaging Graph 2023; 103:102151. [PMID: 36502764 PMCID: PMC10494975 DOI: 10.1016/j.compmedimag.2022.102151] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/27/2022] [Accepted: 11/27/2022] [Indexed: 12/03/2022]
Abstract
Artifacts are a common occurrence in Diffusion MRI (dMRI) scans. Identifying and removing them is essential to ensure the accuracy and viability of any post-processing carried out on these scans. This makes quality control (QC) a crucial first step prior to any analysis of dMRI data. Several QC methods for artifact detection exist, however they suffer from problems like requiring manual intervention and the inability to generalize across different artifacts and datasets. In this paper, we propose an automated deep learning (DL) pipeline that utilizes a 3D-Densenet architecture to train a model on diffusion volumes for automatic artifact detection. Our method is validated on 9000 volumes sourced from 7 large clinical datasets spanning different acquisition protocols (with different gradient directions, high and low b-values, single-shell and multi-shell acquisitions) from multiple scanners. Additionally, they represent diverse subject demographics including age, sex and the presence or absence of pathologies. Our QC method is found to accurately generalize across this heterogenous data by correctly detecting 92% artifacts on average across our test set. This consistent performance over diverse datasets underlines the generalizability of our method, which currently is a significant barrier hindering the widespread adoption of automated QC techniques. Thus, 3D-QCNet can be integrated into diffusion pipelines to effectively automate the arduous and time-intensive process of artifact detection.
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Affiliation(s)
- Adnan Ahmad
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Suhani Dheer
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zahra Riahi Samani
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Diffusion and Connectomics in Precision Healthcare Research Lab (DiCIPHR), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
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75
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Shah D, Gehani A, Mahajan A, Chakrabarty N. Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management. Crit Rev Oncog 2023; 28:45-62. [PMID: 37830215 DOI: 10.1615/critrevoncog.2023047799] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.
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Affiliation(s)
- Diva Shah
- Senior Consultant Radiologist, Department of Radiodiagnosis, HCG Cancer Centre, Ahmedabad, 380060, Gujarat, India
| | - Anisha Gehani
- Department of Radiology and Imaging Sciences, Tata Medical Centre, New Town, WB 700160, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, L7 8YA, United Kingdom
| | - Nivedita Chakrabarty
- Department of Radiodiagnosis, Tata Memorial Hospital, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), 400012, Mumbai, India
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Farhadi F, Rajagopal JR, Veziroglu EM, Abdollahi H, Shiri I, Nikpanah M, Morris MA, Zaidi H, Rahmim A, Saboury B. Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine. PET Clin 2023; 18:135-148. [DOI: 10.1016/j.cpet.2022.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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77
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Kuo H, Liu TW, Huang YP, Chin SC, Ro LS, Kuo HC. Differential Diagnostic Value of Machine Learning-Based Models for Embolic Stroke. Clin Appl Thromb Hemost 2023; 29:10760296231203663. [PMID: 37728185 PMCID: PMC10515586 DOI: 10.1177/10760296231203663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/10/2023] [Accepted: 09/11/2023] [Indexed: 09/21/2023] Open
Abstract
Cancer-associated thrombosis (CAT) and atrial fibrillation (AF)-related stroke are two subtypes of acute embolic stroke with distinct lesion patterns on diffusion weighted imaging (DWI). This pilot study aimed to evaluate the feasibility and performance of DWI-based machine learning models for differentiating between CAT and AF-related stroke. Patients with CAT and AF-related stroke were enrolled. In this pilot study with a small sample size, DWI images were augmented by flipping and/or contrast shifting to build convolutional neural network (CNN) predicative models. DWI images from 29 patients, including 9 patients with CAT and 20 with AF-related stroke, were analyzed. Training and testing accuracies of the DWI-based CNN model were 87.1% and 78.6%, respectively. Training and testing accuracies were 95.2% and 85.7%, respectively, for the second CNN model that combined DWI images with demographic/clinical characteristics. There were no significant differences in sensitivity, specificity, accuracy, and AUC between two CNN models (all P = n.s.).The DWI-based CNN model using data augmentation may be useful for differentiating CAT from AF-related stroke.
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Affiliation(s)
- HsunYu Kuo
- Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Tsai-Wei Liu
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Yo-Ping Huang
- Department of Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taipei University, New Taipei City, Taiwan
- Department of Electrical Engineering, National Penghu University of Science and Technology, Penghu, Taiwan
- Fellow of the Institute of Electrical and Electronics Engineers, Taipei, Taiwan
- Fellow of the Institution of Engineering and Technology, Taipei, Taiwan
- Fellow of Chinese Automatic Control Society, Taipei, Taiwan
| | - Shy-Chyi Chin
- Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Long-Sun Ro
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
| | - Hung-Chou Kuo
- Department of Neurology, Chang Gung Memorial Hospital at Linkou Medical Center and Chang Gung University College of Medicine, Taoyuan, Taiwan
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78
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Khalighinejad P, Suh EH, Sherry AD. MRI Methods for Imaging Beta-Cell Function in the Rodent Pancreas. Methods Mol Biol 2023; 2592:101-111. [PMID: 36507988 PMCID: PMC10008468 DOI: 10.1007/978-1-0716-2807-2_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The role of Zn2+ ions in proper storage of insulin in β-cell granules is well-established so when insulin is secreted from β-cells stimulated by an increase in plasma glucose, free Zn2+ ions are also released. This local increase in Zn2+ can be detected in the pancreas of rodents in real time by the use of a zinc-responsive MR contrast agent. This method offers the opportunity to monitor β-cell function longitudinally in live rodents. The methods used in our lab are fully described in this short report and some MR images of a rat pancreas showing clearly enhanced hot spots in the tail are presented.
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Affiliation(s)
- Pooyan Khalighinejad
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Eul Hyun Suh
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - A Dean Sherry
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Department of Chemistry & Biochemistry, University of Texas at Dallas, Richardson, TX, USA.
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Updates on Quantitative MRI of Diffuse Liver Disease: A Narrative Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1147111. [PMID: 36619303 PMCID: PMC9812615 DOI: 10.1155/2022/1147111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/10/2022] [Accepted: 12/12/2022] [Indexed: 12/29/2022]
Abstract
Diffuse liver diseases are highly prevalent conditions around the world, including pathological liver changes that occur when hepatocytes are damaged and liver function declines, often leading to a chronic condition. In the last years, Magnetic Resonance Imaging (MRI) is reaching an important role in the study of diffuse liver diseases moving from qualitative to quantitative assessment of liver parenchyma. In fact, this can allow noninvasive accurate and standardized assessment of diffuse liver diseases and can represent a concrete alternative to biopsy which represents the current reference standard. MRI approach already tested for other pathologies include diffusion-weighted imaging (DWI) and radiomics, able to quantify different aspects of diffuse liver disease. New emerging MRI quantitative methods include MR elastography (MRE) for the quantification of the hepatic stiffness in cirrhotic patients, dedicated gradient multiecho sequences for the assessment of hepatic fat storage, and iron overload. Thus, the aim of this review is to give an overview of the technical principles and clinical application of new quantitative MRI techniques for the evaluation of diffuse liver disease.
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80
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Azor AM, Sharp DJ, Jolly AE, Bourke NJ, Hellyer PJ. Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations. PLoS One 2022; 17:e0268233. [PMID: 36480567 PMCID: PMC9731501 DOI: 10.1371/journal.pone.0268233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 04/25/2022] [Indexed: 12/13/2022] Open
Abstract
Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS.
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Affiliation(s)
- Adriana M. Azor
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Hammersmith Hospital, London, United Kingdom
- Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London, United Kingdom
- The Royal British Legion, Centre for Blast Injury Studies, Imperial College London, South Kensington Campus, London, United Kingdom
- * E-mail: (AMA); (DJS)
| | - David J. Sharp
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Hammersmith Hospital, London, United Kingdom
- Dyson School of Design Engineering, Imperial College London, South Kensington Campus, London, United Kingdom
- Care Research & Technology Centre, UK Dementia Research Institute, Imperial College London, London, United Kingdom
- * E-mail: (AMA); (DJS)
| | - Amy E. Jolly
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Hammersmith Hospital, London, United Kingdom
- Care Research & Technology Centre, UK Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Niall J. Bourke
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Hammersmith Hospital, London, United Kingdom
| | - Peter J. Hellyer
- Centre for Neuroimaging Sciences, King’s College London, London, United Kingdom
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81
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Barch DM, Hua X, Kandala S, Harms MP, Sanders A, Brady R, Tillman R, Luby JL. White matter alterations associated with lifetime and current depression in adolescents: Evidence for cingulum disruptions. Depress Anxiety 2022; 39:881-890. [PMID: 36321433 PMCID: PMC10848013 DOI: 10.1002/da.23294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/15/2022] [Accepted: 10/22/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Compared to research on adults with depression, relatively little work has examined white matter microstructure differences in depression arising earlier in life. Here we tested hypotheses about disruptions to white matter structure in adolescents with current and past depression, with an a priori focus on the cingulum bundles, uncinate fasciculi, corpus collosum, and superior longitudinal fasciculus. METHODS One hundred thirty-one children from the Preschool Depression Study were assessed using a Human Connectome Project style diffusion imaging sequence which was processed with HCP pipelines and TRACULA to generate estimates of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). RESULTS We found that reduced FA, reduced AD, and increased RD in the dorsal cingulum bundle were associated with a lifetime diagnosis of major depression and greater cumulative and current depression severity. Reduced FA, reduced AD, and increased RD in the ventral cingulum were associated with greater cumulative depression severity. CONCLUSION These findings support the emergence of white matter differences detected in adolescence associated with earlier life and concurrent depression. They also highlight the importance of connections of the cingulate to other brain regions in association with depression, potentially relevant to understanding emotion dysregulation and functional connectivity differences in depression.
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Affiliation(s)
- Deanna M. Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Xiao Hua
- Department of Psychological & Brain Sciences, Imaging Sciences Program, Washington University in St. Louis, Missouri, St. Louis, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ashley Sanders
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Rebecca Brady
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Rebecca Tillman
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Joan L. Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
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Zhi R, Zhang XD, Hou Y, Jiang KW, Li Q, Zhang J, Zhang YD. RtNet: a deep hybrid neural network for the identification of acute rejection and chronic allograft nephropathy after renal transplantation using multiparametric MRI. Nephrol Dial Transplant 2022; 37:2581-2590. [PMID: 35020923 DOI: 10.1093/ndt/gfac005] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Reliable diagnosis of the cause of renal allograft dysfunction is of clinical importance. The aim of this study is to develop a hybrid deep-learning approach for determining acute rejection (AR), chronic allograft nephropathy (CAN) and renal function in kidney-allografted patients by multimodality integration. METHODS Clinical and magnetic resonance imaging (MRI) data of 252 kidney-allografted patients who underwent post-transplantation MRI between December 2014 and November 2019 were retrospectively collected. An end-to-end convolutional neural network, namely RtNet, was designed to discriminate between AR, CAN and stable renal allograft recipient (SR), and secondarily, to predict the impaired renal graft function [estimated glomerular filtration rate (eGFR) ≤50 mL/min/1.73 m2]. Specially, clinical variables and MRI radiomics features were integrated into the RtNet, resulting in a hybrid network (RtNet+). The performance of the conventional radiomics model RtRad, RtNet and RtNet+ was compared to test the effect of multimodality interaction. RESULTS Out of 252 patients, AR, CAN and SR was diagnosed in 20/252 (7.9%), 92/252 (36.5%) and 140/252 (55.6%) patients, respectively. Of all MRI sequences, T2-weighted imaging and diffusion-weighted imaging with stretched exponential analysis showed better performance than other sequences. On pairwise comparison of resulting prediction models, RtNet+ produced significantly higher macro-area-under-curve (macro-AUC) (0.733 versus 0.745; P = 0.047) than RtNet in discriminating between AR, CAN and SR. RtNet+ performed similarly to the RtNet (macro-AUC, 0.762 versus 0.756; P > 0.05) in discriminating between eGFR ≤50 mL/min/1.73 m2 and >50 mL/min/1.73 m2. With decision curve analysis, adding RtRad and RtNet to clinical variables resulted in more net benefits in diagnostic performance. CONCLUSIONS Our study revealed that the proposed RtNet+ model owned a stable performance in revealing the cause of renal allograft dysfunction, and thus might offer important references for individualized diagnostics and treatment strategy.
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Affiliation(s)
- Rui Zhi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Xiao-Dong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Ying Hou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Ke-Wen Jiang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Qiao Li
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Jing Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Yu-Dong Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
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83
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Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst. Sci Rep 2022; 12:20047. [PMID: 36414657 PMCID: PMC9681845 DOI: 10.1038/s41598-022-20802-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/19/2022] [Indexed: 11/23/2022] Open
Abstract
The differentiation between ameloblastoma (AB) and odontogenic keratocyst (OKC) is essential for the formulation of the surgical plan, especially considering the biological behavior of these two pathological entities. Therefore, developing means to increase the accuracy of the diagnostic process is extremely important for a safe treatment. The aim of this study was to use magnetic resonance imaging (MRI) based on texture analysis (TA) as an aid in differentiating AB from OKC. This study comprised 18 patients; eight patients with AB and ten with OKC. All diagnoses were determined through incisional biopsy and later through histological examination of the surgical specimen. MRI was performed using a 3 T scanner with a neurovascular coil according to a specific protocol. All images were exported to segmentation software in which the volume of interest (VOI) was determined by a radiologist, who was blind to the histopathological results. Next, the textural parameters were computed by using the MATLAB software. Spearman's correlation coefficient was used to assess the correlation between texture parameters and the selected variables. Differences in TA parameters were compared between AB and OKC by using the Mann-Whitney test. Mann-Whitney test showed a statistically significant difference between AB and OKC for the parameters entropy (P = 0.033) and sum average (P = 0.033). MRI texture analysis has the potential to discriminate between AB and OKC as a noninvasive method. MRI texture analysis can be an additional tool to differentiate ameloblastoma from odontogenic keratocyst.
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84
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Matani H, Patel AK, Horne ZD, Beriwal S. Utilization of functional MRI in the diagnosis and management of cervical cancer. Front Oncol 2022; 12:1030967. [PMID: 36439416 PMCID: PMC9691646 DOI: 10.3389/fonc.2022.1030967] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/13/2022] [Indexed: 09/15/2023] Open
Abstract
Introduction Imaging is integral part of cervical cancer management. Currently, MRI is used for staging, follow up and image guided adaptive brachytherapy. The ongoing IQ-EMBRACE sub-study is evaluating the use of MRI for functional imaging to aid in the assessment of hypoxia, metabolism, hemodynamics and tissue structure. This study reviews the current and potential future utilization of functional MRI imaging in diagnosis and management of cervical cancer. Methods We searched PubMed for articles characterizing the uses of functional MRI (fMRI) for cervical cancer. The current literature regarding these techniques in diagnosis and outcomes for cervical cancer were then reviewed. Results The most used fMRI techniques identified for use in cervical cancer include diffusion weighted imaging (DWI) and dynamic contrast enhancement (DCE). DCE-MRI indirectly reflects tumor perfusion and hypoxia. This has been utilized to either characterize a functional risk volume of tumor with low perfusion or to characterize at-risk tumor voxels by analyzing signal intensity both pre-treatment and during treatment. DCE imaging in these situations has been associated with local control and disease-free survival and may have predictive/prognostic significance, however this has not yet been clinically validated. DWI allows for creation of ADC maps, that assists with diagnosis of local malignancy or nodal disease with high sensitivity and specificity. DWI findings have also been correlated with local control and overall survival in patients with an incomplete response after definitive chemoradiotherapy and thus may assist with post-treatment follow up. Other imaging techniques used in some instances are MR-spectroscopy and perfusion weighted imaging. T2-weighted imaging remains the standard technique used for diagnosis and radiation treatment planning. In many instances, it is unclear what additional information functional-MRI techniques provide compared to standard MRI imaging. Conclusions Functional MRI provides potential for improved diagnosis, prediction of treatment response and prognostication in cervical cancer. Specific sequences such as DCE, DWI and ADC need to be validated in a large prospective setting prior to widespread use. The ongoing IQ-EMBRACE study will provide important clinical information regarding these imaging modalities.
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Affiliation(s)
- Hirsch Matani
- Division of Radiation Oncology, Allegheny Health Network Cancer Institute, Pittsburgh, PA, United States
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85
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Shahbazi-Gahrouei D, Aminolroayaei F, Nematollahi H, Ghaderian M, Gahrouei SS. Advanced Magnetic Resonance Imaging Modalities for Breast Cancer Diagnosis: An Overview of Recent Findings and Perspectives. Diagnostics (Basel) 2022; 12:2741. [PMID: 36359584 PMCID: PMC9689118 DOI: 10.3390/diagnostics12112741] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/26/2022] [Accepted: 11/07/2022] [Indexed: 08/28/2023] Open
Abstract
Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.
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Affiliation(s)
- Daryoush Shahbazi-Gahrouei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Fahimeh Aminolroayaei
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Hamide Nematollahi
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Mohammad Ghaderian
- Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 8174673461, Iran
| | - Sogand Shahbazi Gahrouei
- Department of Management, School of Humanities, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
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Boudreau E, Kerwin SC, DuPont EB, Levine JM, Griffin JF. Temporal and sequence-related variability in diffusion-weighted imaging of presumed cerebrovascular accidents in the dog brain. Front Vet Sci 2022; 9:1008447. [PMID: 36419725 PMCID: PMC9676236 DOI: 10.3389/fvets.2022.1008447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022] Open
Abstract
Diffusion-weighted MRI (DWI) is often used to guide clinical interpretation of intraparenchymal brain lesions when there is suspicion for a cerebrovascular accident (CVA). Despite widespread evidence that imaging and patient parameters can influence diffusion-weighted measurements, such as apparent diffusion coefficient (ADC), there is little published data on such measurements for naturally occurring CVA in clinical cases in dogs. We describe a series of 22 presumed and confirmed spontaneous canine CVA with known time of clinical onset imaged on a single 3T magnet between 2011 and 2021. Median ADC values of < 1.0x10−3 mm2/s were seen in normal control tissues as well as within CVAs. Absolute and relative ADC values in CVAs were well-correlated (R2 = 0.82). Absolute ADC values < 1.0x10−3 mm2/s prevailed within ischemic CVAs, though there were exceptions, including some lesions of < 5 days age. Some lesions showed reduced absolute but not relative ADC values when compared to matched normal contralateral tissue. CVAs with large hemorrhagic components did not show restricted diffusion. Variation in the DWI sequence used impacted the ADC values obtained. Failure to identify a region of ADC < 1.0x10−3 mm2/s should not exclude CVA from the differential list when clinical suspicion is high.
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Affiliation(s)
- Elizabeth Boudreau
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Elizabeth Boudreau
| | - Sharon C. Kerwin
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Emily B. DuPont
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - Jonathan M. Levine
- Department of Small Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
| | - John F. Griffin
- Department of Large Animal Clinical Sciences, School of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, United States
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Marija C, Kresimir D, Ognjen B, Iva P, Nenad K, Matija B. Estimation of colon cancer grade and metastatic lymph node involvement using DWI/ADC sequences. Acta Radiol 2022; 64:1341-1346. [PMID: 36197524 DOI: 10.1177/02841851221130008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The potential benefit of neoadjuvant chemotherapy (NAC) in colon cancer is under evaluation. There is a need to improve preoperative non-invasive diagnostics using techniques that provide more accurate staging information in assessing patient eligibility for NAC. PURPOSE To investigate the link between the tumor grade (pathohistological confirmed) and the N status (corresponding to lymph node involvement) with apparent diffusion coefficient (ADC) values. MATERIAL AND METHODS A total of 17 patients planned for surgical resection had a biopsy confirming colon carcinoma and participated in the study. Abdominal magnetic resonance imaging with diffusion-weighted imaging/ADC sequence was recorded before surgery. The tumor and all visible lymph nodes were manually delineated directly on a grayscale ADC map for every single slice and detected to access the total tumor and summarized lymph node volume. The mean ADC value was further calculated for the mean tumor and mean lymph node values. RESULTS Low-grade tumors had a mean ADC equivalent to 1225 ± 170×10-6 mm2/s, and the coefficient of high-grade tumors was 1444 ± 69×10-6 mm2/s. The group of patients with positive lymph nodes in operative tissue samples (N+) exhibited lower mean ADC values (1023 ± 142×10-6 mm2/s) as opposed to the group without metastatic lymph nodes (N-) with ADC values of 1260 ± 231×10-6 mm2/s. CONCLUSION The mean whole-tumor ADC is associated with the histological tumor grade, and the mean ADC value of whole-volume abdominal lymph nodes could assume real nodal infiltration.
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Affiliation(s)
- Cavar Marija
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Split, Split, Croatia
| | - Dolic Kresimir
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Split, Split, Croatia
| | - Barcot Ognjen
- Department of Abdominal Surgery, 162037University Hospital Split, Split, Croatia
| | - Peric Iva
- Clinical Department of Diagnostic and Interventional Radiology, University Hospital Split, Split, Croatia
| | - Kunac Nenad
- Clinical Department for Pathology, Forensic Medicine and Cytology, University Hospital Split, Split, Croatia
| | - Boric Matija
- Department of Abdominal Surgery, 162037University Hospital Split, Split, Croatia
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Shetty AS, Fraum TJ, Ludwig DR, Hoegger MJ, Zulfiqar M, Ballard DH, Strnad BS, Rajput MZ, Itani M, Salari R, Lanier MH, Mellnick VM. Body MRI: Imaging Protocols, Techniques, and Lessons Learned. Radiographics 2022; 42:2054-2074. [DOI: 10.1148/rg.220025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Anup S. Shetty
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Tyler J. Fraum
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Daniel R. Ludwig
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mark J. Hoegger
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Maria Zulfiqar
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - David H. Ballard
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Benjamin S. Strnad
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Mohamed Z. Rajput
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Malak Itani
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Reza Salari
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Michael H. Lanier
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
| | - Vincent M. Mellnick
- From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, Campus Box 8131, St Louis, MO 63110
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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90
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Safri AA, Nassir CMNCM, Iman IN, Mohd Taib NH, Achuthan A, Mustapha M. Diffusion tensor imaging pipeline measures of cerebral white matter integrity: An overview of recent advances and prospects. World J Clin Cases 2022; 10:8450-8462. [PMID: 36157806 PMCID: PMC9453345 DOI: 10.12998/wjcc.v10.i24.8450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/20/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
Cerebral small vessel disease (CSVD) is a leading cause of age-related microvascular cognitive decline, resulting in significant morbidity and decreased quality of life. Despite a progress on its key pathophysiological bases and general acceptance of key terms from neuroimaging findings as observed on the magnetic resonance imaging (MRI), key questions on CSVD remain elusive. Enhanced relationships and reliable lesion studies, such as white matter tractography using diffusion-based MRI (dMRI) are necessary in order to improve the assessment of white matter architecture and connectivity in CSVD. Diffusion tensor imaging (DTI) and tractography is an application of dMRI that provides data that can be used to non-invasively appraise the brain white matter connections via fiber tracking and enable visualization of individual patient-specific white matter fiber tracts to reflect the extent of CSVD-associated white matter damage. However, due to a lack of standardization on various sets of software or image pipeline processing utilized in this technique that driven mostly from research setting, interpreting the findings remain contentious, especially to inform an improved diagnosis and/or prognosis of CSVD for routine clinical use. In this minireview, we highlight the advances in DTI pipeline processing and the prospect of this DTI metrics as potential imaging biomarker for CSVD, even for subclinical CSVD in at-risk individuals.
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Affiliation(s)
- Amanina Ahmad Safri
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Che Mohd Nasril Che Mohd Nassir
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Ismail Nurul Iman
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Nur Hartini Mohd Taib
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
| | - Anusha Achuthan
- School of Computer Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Muzaimi Mustapha
- Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Health Campus, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Neurosciences, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
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91
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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92
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Ahmed MIB, Alotaibi S, Atta-ur-Rahman, Dash S, Nabil M, AlTurki AO. A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy. SN COMPUTER SCIENCE 2022; 3:437. [PMID: 35965953 PMCID: PMC9364307 DOI: 10.1007/s42979-022-01358-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/26/2022] [Indexed: 10/26/2022]
Abstract
Epilepsy is the second most common neurological disease after Alzheimer. It is a disorder of the brain which results in recurrent seizures. Though the epilepsy in general is considered as a serious disorder, its effects in children are rather dangerous. It is mainly because it reasons a slower rate of development and a failure to improve certain skills among such children. Seizures are the most common symptom of epilepsy. As a regular medical procedure, the specialists record brain activity using an electroencephalogram (EEG) to observe epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate and depend on the specialist's experience; therefore, automated detection of epileptic pediatric seizures might be an optimal solution. In this regard, several techniques have been investigated in the literature. This research aims to review the approaches to pediatric epilepsy seizures' identification especially those based on machine learning, in addition to the techniques applied on the CHB-MIT scalp EEG database of epileptic pediatric signals.
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Affiliation(s)
- Mohammed Imran Basheer Ahmed
- Department of Computer Engineering, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Shamsah Alotaibi
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Atta-ur-Rahman
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Sujata Dash
- Department of Computer Application, Maharaja Srirama Chandra Bhanj Deo University, Baripada, India
| | - Majed Nabil
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
| | - Abdullah Omar AlTurki
- Department of Computer Science, College of Computer Science and Information Technology (CCSIT), Imam Abdulrahman Bin Faisal University (IAU), P.O. Box 1982, Dammam, 31441 Saudi Arabia
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93
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Altabella L, Benetti G, Camera L, Cardano G, Montemezzi S, Cavedon C. Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7d8f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 06/30/2022] [Indexed: 11/11/2022]
Abstract
Abstract
In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.
Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.
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94
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Vijithananda SM, Jayatilake ML, Hewavithana B, Gonçalves T, Rato LM, Weerakoon BS, Kalupahana TD, Silva AD, Dissanayake KD. Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques. Biomed Eng Online 2022; 21:52. [PMID: 35915448 PMCID: PMC9344709 DOI: 10.1186/s12938-022-01022-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 07/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.
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Affiliation(s)
- Sahan M Vijithananda
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | - Mohan L Jayatilake
- Department of Radiography and Radiotherapy, University of Peradeniya, Peradeniya, Sri Lanka.
| | - Badra Hewavithana
- Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | | | - Luis M Rato
- Department of Informatics, University of Évora, Évora, Portugal
| | - Bimali S Weerakoon
- Department of Radiography and Radiotherapy, University of Peradeniya, Peradeniya, Sri Lanka
| | - Tharindu D Kalupahana
- Department of Computer Engineering, University of Sri Jayawardhanapura, Dehiwala-Mount Lavinia, Sri Lanka
| | - Anil D Silva
- Epilepsy Unit, National Hospital of Sri Lanka, Colombo 10, Sri Lanka
| | - Karuna D Dissanayake
- Department of Histopathology, National Hospital of Sri Lanka, Colombo 10, Sri Lanka
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95
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Monitoring the Impact of Spaceflight on the Human Brain. LIFE (BASEL, SWITZERLAND) 2022; 12:life12071060. [PMID: 35888147 PMCID: PMC9323314 DOI: 10.3390/life12071060] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/04/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022]
Abstract
Extended exposure to radiation, microgravity, and isolation during space exploration has significant physiological, structural, and psychosocial effects on astronauts, and particularly their central nervous system. To date, the use of brain monitoring techniques adopted on Earth in pre/post-spaceflight experimental protocols has proven to be valuable for investigating the effects of space travel on the brain. However, future (longer) deep space travel would require some brain function monitoring equipment to be also available for evaluating and monitoring brain health during spaceflight. Here, we describe the impact of spaceflight on the brain, the basic principles behind six brain function analysis technologies, their current use associated with spaceflight, and their potential for utilization during deep space exploration. We suggest that, while the use of magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) is limited to analog and pre/post-spaceflight studies on Earth, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and ultrasound are good candidates to be adapted for utilization in the context of deep space exploration.
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Cerebral White Matter Connectivity in Adolescent Idiopathic Scoliosis: A Diffusion Magnetic Resonance Imaging Study. CHILDREN 2022; 9:children9071023. [PMID: 35884007 PMCID: PMC9320696 DOI: 10.3390/children9071023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/06/2022] [Accepted: 07/08/2022] [Indexed: 11/16/2022]
Abstract
Adolescent idiopathic scoliosis (AIS) is characterized by the radiographic presence of a frontal plane curve, with a magnitude greater than 10° (Cobb technique). Diffusion MRI can be employed to assess the cerebral white matter. The aim of this study was to analyze, by means of MRI, the presence of any alteration in the connectivity of cerebral white matter in AIS patients. In this study, 22 patients with AIS participated. The imaging protocol consisted in T1 and diffusion-weighted acquisitions. Based on the information from one of the diffusion acquisitions, a whole brain tractography was performed with the MRtrix tool. Tractography is a method to deduce the trajectory of fiber bundles through the white matter based on the diffusion MRI data. By combining cortical segmentation with tractography, a connectivity matrix of size 84 × 84 was constructed using FA (fractional anisotropy), and the number of streamlines as connectomics metrics. The results obtained support the hypothesis that alterations in cerebral white matter connectivity in patients with adolescent idiopathic scoliosis (AIS) exist. We consider that the application of diffusion MRI, together with transcranial magnetic stimulation neurophysiologically, is useful to search the etiology of AIS.
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Li W. Non-Gaussian Diffusion MRI for Evaluating Hepatic Fibrosis. Acad Radiol 2022; 29:964-966. [PMID: 35597754 DOI: 10.1016/j.acra.2022.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 11/01/2022]
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Arthur A, Johnston EW, Winfield JM, Blackledge MD, Jones RL, Huang PH, Messiou C. Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We? Front Oncol 2022; 12:892620. [PMID: 35847882 PMCID: PMC9286756 DOI: 10.3389/fonc.2022.892620] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/31/2022] [Indexed: 12/13/2022] Open
Abstract
A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.
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Affiliation(s)
- Amani Arthur
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Edward W. Johnston
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
| | - Robin L. Jones
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- Division of Clinical Studies, The Institute of Cancer Research, London, United Kingdom
| | - Paul H. Huang
- Division of Molecular Pathology, The Institute of Cancer Research, Sutton, United Kingdom
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, Sutton, United Kingdom
- Sarcoma Unit, The Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
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Zhang H, Zhang XY, Wang Y. Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions. World J Clin Cases 2022; 10:6021-6031. [PMID: 35949832 PMCID: PMC9254209 DOI: 10.12998/wjcc.v10.i18.6021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/23/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
AIM To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.
METHODS This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.
RESULTS The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10-3 mm2/s and 1.24 ± 0.16 × 10-3 mm2/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10-3 mm2/s, which was lower than that of the benign group (1.42 ± 0.68 × 10-3 mm2/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (Ve) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.
CONCLUSION DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.
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Affiliation(s)
- Hui Zhang
- Department of Radiology, Hebei General Hospital, Shijiazhuang 050000, Hebei Province, China
| | - Xin-Yi Zhang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
| | - Yong Wang
- Department of Radiology, the First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
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