Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Objective: : Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging (MRI) and two analytical methods : receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study. Methods: : This study enrolled 60 patients who underwent MVD for HFS. They were divided into two groups : group A consisted of 32 patients who had early recurrence and group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters. Results: : Group A had significantly lower relative cerebral blood flow than group B in most of the selected brain regions, as shown by the region-of-interest-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve value of 0.845. Conclusion : By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmorfrequency) without applying an external current. In MREIT, the subject’s safety should be ensuredbecause electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential ofconductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humansrather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmorfrequency) without applying an external current. In MREIT, the subject’s safety should be ensuredbecause electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential ofconductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humansrather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmorfrequency) without applying an external current. In MREIT, the subject’s safety should be ensuredbecause electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential ofconductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humansrather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmorfrequency) without applying an external current. In MREIT, the subject’s safety should be ensuredbecause electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential ofconductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humansrather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Magnetic resonance imaging (MRI)-related techniques can provide information related to the electrical properties of the body. Understanding the electrical properties of human tissues is crucial for developing diagnostic tools and therapeutic approaches for various medical conditions. This study reviewed the principles, development, and application of electrical conductivity mapping using MRI. To review the magnetic resonance electrical properties tomography (MREPT)-based conductivity mapping technique and its application to brain imaging, first, we explain the definition and fundamental principles of electrical conductivity, some factors that influence changes in ionic conductivity, and the background of mapping cellular conductivities. Second, we explain the concepts and applications of magnetic resonance electrical impedance tomography (MREIT) and MREPT. Third, we describe our recent technical developments and their clinical applications. Finally, we explain the benefits, impacts, and challenges of MRI-based conductivity in clinical practice. MRI techniques, such as MREIT and MREPT, enabled the measurement of conductivity-related properties within the body. MREIT assessed low-frequency conductivity by applying a lowfrequency external current, whereas MREPT captured high-frequency conductivity (at the Larmorfrequency) without applying an external current. In MREIT, the subject’s safety should be ensuredbecause electrical current is applied, particularly around sensitive areas, such as the brain, or in subjects with implanted electronic devices. Our previous studies have highlighted the potential ofconductivity indices as biomarkers for Alzheimer’s disease. MREPT is usually applied to humansrather than MREIT. MREPT holds promise as a noninvasive tool for characterizing tissue properties and understanding pathological conditions.

Objective: Chemical exchange-dependent saturation transfer (CEST) MRI is sensitive for detecting solid-like proteins and may detect changes in the levels of mobile proteins and peptides in tissues. The objective of this study was to evaluate the characteristics of chemical exchange proton pools using the CEST MRI technique in patients with dementia. Materials and Methods: Our institutional review board approved this cross-sectional prospective study and informed consent was obtained from all participants. This study included 41 subjects (19 with dementia and 22 without dementia). Complete CEST data of the brain were obtained using a three-dimensional gradient and spin-echo sequence to map CEST indices, such as amide, amine, hydroxyl, and magnetization transfer ratio asymmetry (MTR asym) values, using six-pool Lorentzian fitting. Statistical analyses of CEST indices were performed to evaluate group comparisons, their correlations with gray matter volume (GMV) and Mini-Mental State Examination (MMSE) scores, and receiver operating characteristic (ROC) curves. Results: Amine signals (0.029 for non-dementia, 0.046 for dementia, p = 0.011 at hippocampus) and MTR asym values at 3 ppm (0.748 for non-dementia, 1.138 for dementia, p = 0.022 at hippocampus), and 3.5 ppm (0.463 for non-dementia, 0.875 for dementia, p = 0.029 at hippocampus) were significantly higher in the dementia group than in the non-dementia group. Most CEST indices were not significantly correlated with GMV; however, except amide, most indices were significantly correlated with the MMSE scores. The classification power of most CEST indices was lower than that of GMV but adding one of the CEST indices in GMV improved the classification between the subject groups. The largest improvement was seen in the MTR asym values at 2 ppm in the anterior cingulate (area under the ROC curve = 0.981), with a sensitivity of 100 and a specificity of 90.91. Conclusion CEST MRI potentially allows noninvasive image alterations in the Alzheimer’s disease brain without injecting isotopes for monitoring different disease states and may provide a new imaging biomarker in the future.