High-resolution Magnetic Resonance Imaging Research Articles

Brain MR image simulation for deep learning based medical image analysis networks

Background and ObjectiveAs large sets of annotated MRI data are needed for training and validating deep learning based medical image analysis algorithms, the lack of sufficient annotated data is a critical problem. A possible solution is the generation of artificial data by means of physics-based simulations. Existing brain simulation data is limited in terms of anatomical models, tissue classes, fixed tissue characteristics, MR sequences and overall realism. MethodsWe propose a realistic simulation framework by incorporating patient-specific phantoms and Bloch equations-based analytical solutions for fast and accurate MRI simulations. A large number of labels are derived from open-source high-resolution T1w MRI data using a fully automated brain classification tool. The brain labels are taken as ground truth (GT) on which MR images are simulated using our framework. Moreover, we demonstrate that the T1w MR images generated from our framework along with GT annotations can be utilized directly to train a 3D brain segmentation network. To evaluate our model further on larger set of real multi-source MRI data without GT, we compared our model to existing brain segmentation tools, FSL-FAST and SynthSeg. ResultsOur framework generates 3D brain MRI for variable anatomy, sequence, contrast, SNR and resolution. The brain segmentation network for WM/GM/CSF trained only on T1w simulated data shows promising results on real MRI data from MRBrainS18 challenge dataset with a Dice scores of 0.818/0.832/0.828. On OASIS data, our model exhibits a close performance to FSL, both qualitatively and quantitatively with a Dice scores of 0.901/0.939/0.937. ConclusionsOur proposed simulation framework is the initial step towards achieving truly physics-based MRI image generation, providing flexibility to generate large sets of variable MRI data for desired anatomy, sequence, contrast, SNR, and resolution. Furthermore, the generated images can effectively train 3D brain segmentation networks, mitigating the reliance on real 3D annotated data.

A multimodal submillimeter MRI atlas of the human cerebellum

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.

Evaluation of the structural integrity of different spinal cord tracts with magnetization transfer ratio in degenerative cervical myelopathy.

Degenerative cervical myelopathy (DCM) is a common cause of spinal cord dysfunction. In this study, we explored the potential of magnetization transfer ratio (MTR) for evaluating the structural integrity of spinal cord tracts in patients with clinically significant DCM. Fifty-three patients with DCM and 41 patients with cervical radiculopathy were evaluated using high-resolution cervical spinal cord magnetic resonance imaging (MRI), which included the magnetization transfer technique. MRI data were analyzed with the Spinal Cord Toolbox (v5.5); MTR values in each spinal tract were calculated and compared between groups after correction for patient age and sex. Correlations between MTR values and patients' clinical disability rate were also evaluated. A statistically significant reduction in the average MTR of the spinal cord white matter, as well as the MTR of the ventral columns and lateral funiculi, was revealed in the DCM group (adjusted p < 0.01 for all comparisons). Furthermore, reductions in MTR values in the fasciculus cuneatus, spinocerebellar, rubrospinal, and reticulospinal tracts were found in patients with DCM (adjusted p < 0.01 for all comparisons). Positive correlations between the JOA score and the MTR within the ventral columns of the spinal cord (R = 0.38, adjusted p < 0.05) and the ventral spinocerebellar tract (R = 0.41, adjusted p < 0.05) were revealed. The findings of our study indicate that demyelination in patients with DCM primarily affects the spinal tracts of the extrapyramidal system, and the extent of these changes is related to the severity of the condition.

Assessment of arterial pulsatility of cerebral perforating arteries using 7T high-resolution dual-VENC phase-contrast MRI.

Directly imaging the function of cerebral perforating arteries could provide valuable insight into the pathology of cerebral small vessel diseases (cSVD). Arterial pulsatility has been identified as a useful biomarker for assessing vascular dysfunction. In this study, we investigate the feasibility and reliability of using dual velocity encoding (VENC) phase-contrast MRI (PC-MRI) to measure the pulsatility of cerebral perforating arteries at 7 T. Twenty participants, including 12 young volunteers and 8 elder adults, underwent high-resolution 2D PC-MRI scans with VENCs of 20 cm/s and 40 cm/s at 7T. The sensitivity of perforator detection and the reliability of pulsatility measurement of cerebral perforating arteries using dual-VENC PC-MRI were evaluated by comparison with the single-VENC data. The effects of temporal resolution in the PC-MRI acquisition and aging on the pulsatility measurements were investigated. Compared to the single VENCs, dual-VENC PC-MRI provided improved sensitivity of perforator detection and more reliable pulsatility measurements. Temporal resolution impacted the pulsatility measurements, as decreasing temporal resolution led to an underestimation of pulsatility. Elderly adults had elevated pulsatility in cerebral perforating arteries compared to young adults, but there was no difference in the number of detected perforators between the two age groups. Dual-VENC PC-MRI is a reliable imaging method for the assessment of pulsatility of cerebral perforating arteries, which could be useful as a potential imaging biomarker of aging and cSVD.

Evaluation of high temporal resolution magnetic resonance imaging of the liver with gadoxetate disodium in combination with compressed sensing and parallel imaging under single breath-holding using a 1.5-T magnetic resonance system

BackgroundThis study aimed to determine the optimal scan time for high temporal resolution magnetic resonance (MR) imaging of the liver with gadoxetate disodium injection in combination with compressed sensing (CS) and parallel imaging (PI) techniques under single breath-holding using a 1.5-T MR system.MethodsSixty-two participants underwent multiple arterial phases of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the liver with gadoxetate disodium using fat-suppressed GRE T1-weighted imaging—liver acquisition with volume acceleration (LAVA)—in combination with CS and PI using a 1.5-T MR system. Forty-six and 22 participants underwent 6-s and 10-s scans, respectively. Pre-contrast, multiple arterial, portal venous, and hepatobiliary phase images were acquired. Two radiologists evaluated the visual scores for the outline of the liver, inferior right hepatic vein (IRHV), right portal vein, right hepatic artery, appropriateness of the arterial phase, and overall image quality using a 4- or 5-point scale.ResultsThe overall image quality and the image quality of the outline of the liver in the pre-contrast and arterial phases and IRHV in the pre-contrast phase were significantly better (P < 0.05) in the 10-s scan group than those in the 6-s scan group. No significant difference was observed between the two groups in terms of the appropriateness of the arterial phase (obtaining the optimal arterial phase) (P = 0.731).ConclusionsA 10-s scan protocol is recommended for high temporal resolution DCE-MRI of the liver with gadoxetate disodium injection in combination with CS and PI under single breath-holding using a 1.5-T MR system.

Feasibility and efficiency of a new classification based on high-resolution MRI for carotid artery pseudo-occlusion and occlusion: Hybrid revascularization pilot study

Background and purposeStudies on changes in the distal internal carotid artery based on high resolution magnetic resonance imaging (HRMRI) are scarce. Herein, we propose a histological classification system for patients with carotid artery pseudo-occlusion or occlusion based on preoperative HRMRI, for which we evaluated the feasibility and clinical implications. Materials and methodsFrom January 2017 to June 2021, 40 patients with Doppler ultrasound, CTA or MRA suggesting carotid artery occlusion were enrolled in this study. A new classification system based on HRMRI was established and subsequently verified by postoperative specimens. We recorded and analyzed patient characteristics, HRMRI data, recanalization rate, requirements of additional endovascular procedures, complications, and outcomes. ResultsFour histological classifications (type Ⅰ–Ⅳ) were identified. According to our classification system, 20 patients (50.00%) were type I, nine (22.50%) were type II, 7 (17.50%) were type III, and four (10.00%) were type Ⅳ. The success rate of recanalization was 88.89% (32/36) in type I–III patients. Four (44.44%) type Ⅱ patients and five (71.43%) type Ⅲ patients suffered from intraoperative dissection. ConclusionPatients identified as types I (pseudo-occlusion) and II (thrombotic-occlusion) were able to be treated via hybrid revascularization with relatively low risk, while patients identified as type III (fibrous-occlusion) required more careful treatment. Recanalization is not suitable for patients identified as type Ⅳ. Our proposed classification system based on HRMRI data can be used as an adjunctive guide to predict the technical feasibility and success of revascularization via a hybrid technique.

An MR-compatible fiber-optic probe for measuring focused ultrasound-induced temperature rises without viscous heating artifacts

Focused ultrasound (FUS) beam interactions with thermocouples and fiber optic (FO) probes cause a “viscous heating artifact” (VHA), which prevent accurate temperature measurements during acoustic sonication. This work demonstrates a novel fiber-optic temperature probe which is insensitive to VHA, validated in an MR-guided FUS treatment setting. The FO probe (OSENSA Innovations; PRB-140, 0.14 mm diameter) was inserted with an 18G catheter into a tissue-mimicking gelatin phantom. The FO probe tip was located with high-resolution MR images (0.25 × 0.25 × 0.5 mm3) for targeting with FUS. Continuous-wave FUS sonications (50 W, 20 s) were delivered at a distance of ∼1.5 mm from the FO probe tip using a 256 phased-array transducer (Imasonic, France; 1 MHz, 2.1 × 2.3 × 9.8 FWHM spot size) and FUS-induced heating was measured with 3D MR temperature imaging (MRTI; 0.5 × 0.5 × 1 mm3 resolution, 3.9 s acquisition). The VHA effect was not observed in the PRB-140 FO probe measurement data. The FO probe heating and cooling curves closely matched those measured by MRTI, with a root mean-squared error (RMSE) of 0.61 °C. In contrast, the RMSE of VHA-sensitive FO probe was 6.00 °C for a similar acoustic power output. This ultrasound artifact-immune FO temperature probe is highly advantageous for MR temperature sequence development and precise temperature monitoring in FUS treatment applications.

Communicative impairment and its neural correlates in Alzheimer's disease and frontotemporal dementia.

Communication skills can deteriorate in neurodegenerative diseases such as Alzheimer's disease (AD) and frontotemporal dementia (FTD); however, their clinical assessment and treatment in patient care can be challenging. In the present study, we aimed to quantify the distinctive communication resources and barriers reported by patients and their relatives in AD and FTD and associated these communicative characteristics with clinical parameters, such as the degree of cognitive impairment and atrophy in language-associated brain areas. We assessed self-reported communication barriers and resources in 33 individuals with AD and FTD through an interview on daily-life communication, using the Aachener KOMPASS questionnaire. We correlated reported communication barriers and resources with atrophy from high-resolution 3T brain magnetic resonance imaging, neuropsychological assessment, and neurodegenerative markers from cerebrospinal fluid. Communicative impairment was higher in FTD compared to AD. Increased reported communication barriers in our whole sample were associated with the atrophy rate in the left middle temporal lobe, a critical site within the neuronal language network, and with depressive symptoms as well as the semantic word fluency from neuropsychological assessment. The best model for prediction of communicative impairment included the diagnosis (AD or FTD), semantic word fluency, and depressive symptoms. Our study demonstrates that communication barriers and resources can be successfully assessed via a structured interview based on self-report and report of patients' relatives in practice and are reflected in neuroimaging specific for AD and FTD as well as in further clinical parameters specific for these neurodegenerative diseases. This can potentially open new treatment options for clinical practice and patient care.

Does CISS MRI Reliably Depict the Endolymphatic Duct in Children with and without Vestibular Aqueduct Enlargement?

High-resolution CT is the mainstay for diagnosing an enlarged vestibular aqueduct (EVA), but MR imaging may be an appealing alternative, given its lack of ionizing radiation exposure. The purpose of this study was to determine how reliably MR imaging demonstrates the endolymphatic duct and endolymphatic duct enlargement in hearing-impaired children. We performed a retrospective review of temporal bone high-resolution CT and MR imaging of hearing-impaired children evaluated between 2017 and 2020. Vestibular aqueduct diameter was measured on high-resolution CT. The vestibular aqueducts were categorized as being enlarged (EVA+) or nonenlarged (EVA-) using the Cincinnati criteria. The endolymphatic ducts were assessed on axial high-resolution CISS MR imaging. We categorized endolymphatic duct visibility into the following: type 1 (not visible), type 2 (faintly visible), and type 3 (easily visible). Mixed-effect logistic regression was used to identify associations between endolymphatic duct visibility and EVA. Interreader agreement for the endolymphatic duct among 3 independent readers was assessed using the Fleiss κ statistic. In 196 ears from 98 children, endolymphatic duct visibility on MR imaging was type 1 in 74.0%, type 2 in 14.8%, and type 3 in 11.2%; 20.4% of ears were EVA+ on high-resolution CT. There was a significant association between EVA+ status and endolymphatic duct visibility (P < .01). Endolymphatic duct visibility was type 1 in 87.1%, type 2 in 12.8%, and type 3 in 0% of EVA- ears and type 1 in 22.5%, type 2 in 22.5%, and type 3 in 55.0% of EVA+ ears. The predicted probability of a type 3 endolymphatic duct being EVA+ was 0.997. There was almost perfect agreement among the 3 readers for distinguishing type 3 from type 1 or 2 endolymphatic ducts. CISS MR imaging substantially underdiagnoses EVA; however, when a type 3 endolymphatic duct is evident, there is a >99% likelihood of an EVA.

Detection and classification of progressive supranuclear palsy from MRI images using deep learning

A timely and reliable computer aided MR image-based evaluations is required for the detection and classification of Progressive supranuclear palsy (PSP). It is a neurodegenerative ailment that is clinically very difficult to identify due to a high degree of overlap in characterized symptoms. Unlike other ailments, the primary constraint regarding PSP is the limited research work in this area. The main aim of our study is to establish a generalized model by comparing traditional custom CNN and transfer learning models such as DenseNet121, ResNet50, InceptionV3, VGG16, EfficientNetB0, Xception, MobileNet and InceptionResNetV2 which are fine tuned for the detection and classification of PSP with higher accuracy rates. Existing research has primarily focused on reducing the time complexity of neural networks and has only had success with low-level features. Furthermore, obtaining a significant volume of distributed labelled data is difficult. In our research, 125 T1 protocol based high resolution MRI images of 65 PSP and 60 normal control patients were considered. The image dataset is pre-processed, normalized and augmented before deploying them to the respective networks. The results propose that ‘InceptionResNetV2’ model can be considered as a generalised model for the detection and classification of PSP. The network offered a classification accuracy of 95%, Precision of 100%, Sensitivity of 92.8%, F1 score of 96.2% and a Specificity of 100% which is significantly higher compared to other models considered in the study and also the existing conventional ML models, thereby providing a prerequisite for significant diagnostic implementation.

High-Q metasurface signal isolator for 1.5T surface coil magnetic resonance imaging on the go.

The combination of surface coils and metamaterials remarkably enhance magnetic resonance imaging (MRI) performance for significant local staging flexibility. However, due to the coupling in between, impeded signal-to-noise ratio (SNR) and low-contrast resolution, further hamper the future growth in clinical MRI. In this paper, we propose a high-Q metasurface decoupling isolator fueled by topological LC loops for 1.5T surface coil MRI system, increasing the magnetic field up to fivefold at 63.8 MHz. We have employed a polarization conversion mechanism to effectively eliminate the coupling between the MRI metamaterial and the radio frequency (RF) surface transmitter-receiver coils. Furthermore, a high-Q metasurface isolator was achieved by taking advantage of bound states in the continuum (BIC) for extremely high-resolution MRI and spectroscopy. An equivalent physical model of the miniaturized metasurface design was put forward through LC circuit analysis. This study opens up a promising route for the easy-to-use and portable surface coil MRI scanners.

Discrete residual diffusion model for high-resolution prostate MRI synthesis

Objective. High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM). Approach. First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images. Main results. Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods. Significance. Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.

A detunable wireless resonator insert for high-resolution TMJ MRI at 1.5 T

MRI is essential for evaluating and diagnosing various conditions affecting the temporomandibular joint (TMJ) and surrounding structures, as it provides highly detailed images that enable healthcare professionals to assess the joints and surroundings in great detail. While commercial MRI scanners typically come equipped with basic receive coils, such as the head receive array, RF coils tailored for specialized applications like TMJ MRI must be obtained separately. Consequently, TMJ MRI scans are often conducted using the head receive array, yet this configuration proves suboptimal due to the lack of specialized coils. In this study, we introduce a simple, low-cost, and easy-to-reproduce wireless resonator insert to enhance the quality of TMJ MRI at 1.5 T. The wireless resonator shows a significant improvement in signal-to-noise ratio (SNR) and noticeably better imaging quality than the head array alone configuration in both phantom and in vivo images.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Diffusion Magnetic Resonance Imaging Tractography Guides Investigation of the Zona Incerta: A Novel Target for Deep Brain Stimulation

BackgroundThe zona incerta (ZI) is a subcortical structure primarily investigated in rodents that is implicated in various behaviors, ranging from motor control to survival-associated activities, partly due to its integration in multiple neural circuits. In the current study, we used diffusion magnetic resonance imaging tractography to segment the ZI and gain insight into its connectivity in various circuits in humans. MethodsWe performed probabilistic tractography in 7T diffusion MRI on 178 participants from the Human Connectome Project to validate the ZI’s anatomical subdivisions and their respective tracts. K-means clustering segmented the ZI based on each voxel’s connectivity profile. We further characterized the connections of each ZI subregion using probabilistic tractography with each subregion as a seed. ResultsWe identified 2 dominant clusters that delineated the whole ZI into rostral and caudal subregions. The caudal ZI primarily connected with motor regions, while the rostral ZI received a topographic distribution of projections from prefrontal areas, notably the anterior cingulate and medial prefrontal cortices. We generated a probabilistic ZI atlas that was registered to a patient-participant’s magnetic resonance imaging scan for placement of stereoencephalographic leads for electrophysiology-guided deep brain stimulation to treat their obsessive-compulsive disorder. Rostral ZI stimulation improved the patient’s core symptoms (mean improvement 21%). ConclusionsWe present a tractography-based atlas of the rostral and caudal ZI subregions constructed using high-resolution diffusion magnetic resonance imaging from 178 healthy participants. Our work provides an anatomical foundation to explore the rostral ZI as a novel target for deep brain stimulation to treat refractory obsessive-compulsive disorder and other disorders associated with dysfunctional reward circuitry.

Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI

ObjectivesSeverity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients.Materials and methodsVertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC).ResultsThe ICCs between the readers ranged from 0.90 (95% CI 0.83–0.94) to 0.93 (95% CI 0.88–0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71–0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87–0.96) to 0.97 (95% CI 0.94–0.98).ConclusionThe Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured.Clinical relevance statementOur AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care.Key Points• While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis.• The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis.• Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available.Graphical

Radiologic Classification of Hippocampal Sclerosis in Epilepsy.

Temporal lobe epilepsy is a common form of epilepsy that is often associated with hippocampal sclerosis (HS). Although HS is commonly considered a binary assessment in radiologic evaluation, it is known that histopathologic changes occur in distinct clusters. Some subtypes of HS only affect certain subfields, resulting in minimal changes to the overall volume of the hippocampus. This is likely a major reason why whole hippocampal volumetrics have underperformed versus expert readers in the diagnosis of HS. With recent advancements in MRI technology, it is now possible to characterize the substructure of the hippocampus more accurately. However, this is not consistently addressed in radiographic evaluations. The histologic subtype of HS is critical for prognosis and treatment decision-making, necessitating improved radiologic classification of HS. The International League Against Epilepsy (ILAE) has issued a consensus classification scheme for subtyping HS histopathologic changes. This review aims to explore how the ILAE subtypes of HS correlate with radiographic findings, introduce a grading system that integrates radiologic and pathologic reporting in HS, and outline an approach to detecting HS subtypes by using MRI. This framework will not only benefit current clinical evaluations, but also enhance future studies involving high-resolution MRI in temporal lobe epilepsy.

Cochlear measurement in computed tomography and magnetic resonance imaging data sets by the Otoplan measurement tool: a retrospective comparative study.

Using Otoplan software, it is possible to measure the cochlea before cochlear implant surgery. Until now, computed tomography (CT) of the cochlea has been necessary for this purpose. The aim of this study was to find out whether measuring the cochlea with magnetic resonance imaging (MRI) using Otoplan is possible with the same accuracy. The cochlea of 44 patients of the local cochlear implant centre was measured by Otoplan using high-resolution CT-bone and MRI images, and the determined lengths were compared. No significant difference was found between the cochlear lengths measured, regardless of whether the length measurement was based on a CT or an MRI data set. For the determination of cochlear length prior to cochlear implant surgery, MRI images are just as suitable as CT images, therefore CT is not mandatory for length measurement by Otoplan, which could reduce the patient's radiation exposure.

Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas.

To evaluate the diagnostic value of T1-weighted 3D fast spin-echo sequence (CUBE) with deep learning-based reconstruction (DLR) for depiction of pituitary adenoma and parasellar regions on contrast-enhanced MRI. We evaluated 24 patients with pituitary adenoma or residual tumor using CUBE with and without DLR, 1-mm slice thickness 2D T1WI (1-mm 2D T1WI) with DLR, and 3D spoiled gradient echo sequence (SPGR) as contrast-enhanced MRI. Depiction scores of pituitary adenoma and parasellar regions were assigned by two neuroradiologists, and contrast-to-noise ratio (CNR) was calculated. CUBE with DLR showed significantly higher scores for depicting pituitary adenoma or residual tumor compared to CUBE without DLR, 1-mm 2D T1WI with DLR, and SPGR (p < 0.01). The depiction score for delineation of the boundary between adenoma and the cavernous sinus was higher for CUBE with DLR than for 1-mm 2D T1WI with DLR (p = 0.01), but the difference was not significant when compared to SPGR (p = 0.20). CUBE with DLR had better interobserver agreement for evaluating adenomas than 1-mm 2D T1WI with DLR (Kappa values, 0.75 vs. 0.41). The CNR of the adenoma to the brain parenchyma increased to a ratio of 3.6 (obtained by dividing 13.7, CNR of CUBE with DLR, by 3.8, that without DLR, p < 0.01). CUBE with DLR had a significantly higher CNR than SPGR, but not 1-mm 2D T1WI with DLR. On the contrast-enhanced MRI, compared to CUBE without DLR, 1-mm 2D T1WI with DLR and SPGR, CUBE with DLR improves the depiction of pituitary adenoma and parasellar regions.

Are there any differences at gray matter sites between severe obstructive sleep apnea patients and healthy controls?

ObjectivesObstructive sleep apnea (OSA) is a disease that may cause many medical conditions. Neurocognitive disorders may be triggered by OSA. In recent studies, selectively decreased gray matter tissue was observed in patients with OSA. We aimed to determine if there was a substantial difference in patients with extreme OSA by comparing the microstructural changes in different gray matter sub-areas with healthy controls using diffusion-weighted imaging methods. MethodsWe studied 15 diagnosed severe OSA subjects before any treatment and 32 healthy control subjects. High resolution Magnetic Resonance Imaging (MRI) T1 and T2-weighted scans were visually examined to assess any major brain lesions. ResultsThere were no statistically significant differences of age and gender between the groups.The left and right globus pallidus, putamen and thalamus values did not differ significantly between OSA and control subjects. Right putamen values was negatively correlated with Apnea Hypopnea Index (AHI), supine AHI and non-REM AHI in OSA subjects, but no correlations appeared with left putamen values. The other gray matter parameters did not show any correlations with PSG parameters. AHI, Supine AHI, Non-Supine AHI, REM and NON-REM AHI values was not show any correlation with Right and Left Putamen volume sizes. ConclusionsWe made a morphological comparison of various gray matter areas of OSA patients and healthy volunteers in our study. We observed a significant decrease in right putamen gray matter volumes in patients with higher AHI values. Decreased cognitive functions are found in patients with OSA. In order to demonstrate this cognitive loss in patients with morphologically there is a need for further prospective studies with larger sample sizes.