3D Magnetic Resonance Imaging Research Articles

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.

Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI.

In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM is necessary and holds significant clinical importance. We develop a ranking attention multiple instance learning (RA-MIL) model that integrates convolutional neural networks (CNNs) and ranking attention pooling to diagnose LNM from T2 MRI. Our RA-MIL model applies the CNNs to derive imaging features from 2D MRI slices and employs ranking attention pooling to create patient-level feature representation for diagnostic classification. Based on the MIL and attention theory, informative regions of top-ranking MRI slices from LNM-positive patients are visualized to enhance the interpretability of automatic LNM prediction. This retrospective study collected 300 female patients with cervical cancer who underwent T2-weighted magnetic resonance imaging (MRI) scanning and histopathological diagnosis from one hospital (289 patients) and one open-source dataset (11 patients). Our RA-MIL model delivers promising LNM prediction performance, achieving the area under the receiver operating characteristic curve (AUC) of 0.809 on the internal test set and 0.833 on the public dataset. Experiments show significant improvements in LNM status prediction using the proposed RA-MIL model compared with other state-of-the-art (SOTA) comparative deep learning models. The developed RA-MIL model has the potential to serve as a non-invasive auxiliary tool for preoperative LNM prediction, offering visual interpretability regarding informative MRI slices and regions in LNM-positive patients.

7594 Diagnostic utility of 11C-Methionine PET-CT imaging in persistent or recurrent Cushing’s disease after transsphenoidal surgery

Abstract Disclosure: R. Furnica: None. F. Devuyst: None. C. Mathey: None. S. Constantinescu: None. C. De Herdt: None. L. Renaud: None. D.M. Maiter: None. Introduction: Equivocal or negative pituitary magnetic resonance imaging (MRI) findings pose a significant challenge in the management of persistent or recurrent Cushing’s disease (CD), compromising the chances of success of a further transsphenoidal surgery (TSS). The aim of our study was to determine the diagnostic utility of [1][1]C-methionine (MET) positron emission tomography coupled with computerized tomography (PET-CT) in localizing the residual corticotroph adenoma. Methods: We retrospectively analyzed the results of all MET-PET-CT performed between May 2002 and November 2023 in patients with a CD persisting or relapsing after TSS, and equivocal (n=14) or negative pituitary MRI findings (n=8). In 15 cases, images obtained from MET-PET were also co-registered with a high-resolution 3D pituitary MRI. All patients had a pathologically confirmed corticotroph tumor, and/or biochemical evidence of early postoperative remission after the first TSS, and/or proven ACTH-dependent Cushing’s syndrome with positive bilateral inferior petrosal sinus sampling. Results: Twenty-two patients were included (18 females and 4 males), with a mean age of 35.5 years at diagnosis. Initial tumors were mostly microadenomas with a mean tumor volume of 15 mm3. Thirteen out of the 22 patients had a suspect anomaly on conventional MRI that could however not be clearly distinguished from postsurgical changes, while the remaining 9 had a negative MRI. A significant MET standardized uptake value (SUV) in the suspected area of the tumor residue was defined as a ratio ≥ 1.80 relative to cerebellum SUV and was found in 12/22 patients (55%; 4 patients with negative pituitary MRI and 8 with equivocal findings). On the basis of positive imaging, repeat TSS was performed in 10 patients and was successful in 6, while Gamma Knife radiosurgery (GKRS) was performed in 2 patients allowing remission in both (total remission rate: 8/12). Among the 4 patients not cured by TSS, the presence of corticotroph adenoma in the resected tissue was found in 3 cases. Exploratory TSS was performed in 4 of the 10 PET negative patients, with no remission. Two patients were treated with GKRS, with remission in 1 case. Among the 4 other patients, bilateral adrenalectomy was performed in one case and adrenal steroidogenesis inhibitors were used in the remaining patients. Conclusions: [1][1]C-Methionine PET-CT imaging can provide valuable diagnostic information to detect a residual active corticotroph adenoma in about half of patients with a persistent or recurrent CD and equivocal or negative MRI results, thereby allowing targeted TSS or radiosurgery with a global success rate of 67% in this selected subpopulation. Presentation: 6/1/2024

9272 Diagnostic utility of 11C-Methionine PET-CT imaging in persistent or recurrent Cushing’s disease after transsphenoidal surgery

Abstract Disclosure: R. Furnica: None. F. Devuyst: None. C. Mathey: None. S. Constantinescu: None. C. De Herdt: None. L. Renaud: None. D.M. Maiter: None. Introduction: Equivocal or negative pituitary magnetic resonance imaging (MRI) findings pose a significant challenge in the management of persistent or recurrent Cushing’s disease (CD), compromising the chances of success of a further transsphenoidal surgery (TSS). The aim of our study was to determine the diagnostic utility of [1][1]C-methionine (MET) positron emission tomography coupled with computerized tomography (PET-CT) in localizing the residual corticotroph adenoma. Methods: We retrospectively analyzed the results of all MET-PET-CT performed between May 2002 and November 2023 in patients with a CD persisting or relapsing after TSS, and equivocal (n=14) or negative pituitary MRI findings (n=8). In 15 cases, images obtained from MET-PET were also co-registered with a high-resolution 3D pituitary MRI. All patients had a pathologically confirmed corticotroph tumor, and/or biochemical evidence of early postoperative remission after the first TSS, and/or proven ACTH-dependent Cushing’s syndrome with positive bilateral inferior petrosal sinus sampling. Results: Twenty-two patients were included (18 females and 4 males), with a mean age of 35.5 years at diagnosis. Initial tumors were mostly microadenomas with a mean tumor volume of 15 mm3. Thirteen out of the 22 patients had a suspect anomaly on conventional MRI that could however not be clearly distinguished from postsurgical changes, while the remaining 9 had a negative MRI. A significant MET standardized uptake value (SUV) in the suspected area of the tumor residue was defined as a ratio ≥ 1.80 relative to cerebellum SUV and was found in 12/22 patients (55%; 4 patients with negative pituitary MRI and 8 with equivocal findings). On the basis of positive imaging, repeat TSS was performed in 10 patients and was successful in 6, while Gamma Knife radiosurgery (GKRS) was performed in 2 patients allowing remission in both (total remission rate: 8/12). Among the 4 patients not cured by TSS, the presence of corticotroph adenoma in the resected tissue was found in 3 cases. Exploratory TSS was performed in 4 of the 10 PET negative patients, with no remission. Two patients were treated with GKRS, with remission in 1 case. Among the 4 other patients, bilateral adrenalectomy was performed in one case and adrenal steroidogenesis inhibitors were used in the remaining patients. Conclusions: [1][1]C-Methionine PET-CT imaging can provide valuable diagnostic information to detect a residual active corticotroph adenoma in about half of patients with a persistent or recurrent CD and equivocal or negative MRI results, thereby allowing targeted TSS or radiosurgery with a global success rate of 67% in this selected subpopulation. Presentation: 6/1/2024

4D-Flow MRI and Vector Ultrasound in the In-Vitro Evaluation of Surgical Aortic Heart Valves - a Pilot Study.

The aim of this study was the initial investigation of 4D-Flow MRI and Vector Ultrasound as novel imaging techniques in the in-vitro analysis of hemodynamics in anatomical models. Specifically, by looking at the hemodynamic performance of state-of-the-art surgical heart valves in a 3D-printed aortic arch. The mock circulatory loop simulated physiological, pulsatile flow. Two mechanical and three biological aortic valves prostheses were compared in a 3D-printed aortic arch. 4D magnetic resonance imaging and vector flow Doppler ultrasound served as imaging methods. Hemodynamic parameters such as wall shear stress, flow velocities and pressure gradients were analyzed. The flow analysis revealed characteristic flow-patterns in the 3D-printed aortic arch. The blood-flow in the arch presented complex patterns, including the formation of helixes and vortices. Higher proximal peak velocities and lower flow volumes were found for biological valves. The mock circulatory loop in combination with modern radiological imaging provides a sufficient basis for the hemodynamic comparison of aortic valves.

Adaptive wavelet-VNet for single-sample test time adaptation in medical image segmentation.

In medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning-based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine-tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test-time adaptation (TTA) presents a more practical but challenging solution. To improve the segmentation accuracy of deep learning-based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains. In this study, we proposed to dynamically adapt a wavelet-VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V-Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape-aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains). The proposed method showed the highest segmentation accuracy among all methods, achieving a mean(±SD) Dice coefficient (DSC) of 78.10±5.23% and a mean 95th Hausdorff distance (HD95) of 15.52±5.84mm on the liver dataset; and a mean DSC of 80.02±3.89% and a mean HD95 of 9.18±3.47mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively. The proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi-objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet.

Transducer module apodization to reduce bone heating during focused ultrasound uterine fibroid ablation with phased arrays: A numerical study.

During magnetic resonance-guided focused ultrasound (MRgFUS) surgery for uterine fibroids, ablation of fibrous tissues in proximity to the hips and spine is challenging due to heating within the bone that can cause patients to experience pain and potentially damage nerves. This far-field bone heating limits the volume of fibroid tissue that is treatable viaMRgFUS. To investigate transducer module apodization for improving the ratio of focal-to-bone heating ( ) when targeting fibroid tissue close to the hips and spine, to enable MRgFUS treatments closer to thebone. Acoustic and thermal simulations were performed using 3D magnetic resonance imaging (MRI)-derived anatomies of ten patients who underwent MRgFUS ablation for uterine fibroids using a low-frequency ( ) 6144-element flat fully-populated modular phased array system (Arrayus Technologies Inc., Burlington, Canada) at our institution as part of a larger clinical trial (NCT03323905). Transducer modules ( per module) whose beams intersected with no-pass zones delineated within the field were identified, their output power levels were reduced by varying blocking percentage levels, and the resulting temperature field distributions were evaluated across multiple sonications near the hip and spine bones in each patient. Acoustic and thermal simulations took approximately ( ) and ( ) to run for a single near-spine (near-hip) target,respectively. For all simulated sonications, transducer module blocking improved compared to the no blocking case. In just over half of sonications, full module blocking maximized (increase of 82% 38% in 50% of hip targets and 49% 30% in 62% of spine targets vs. no blocking; mean±SD), at the cost of more diffuse focusing (focal heating volumes increased by 13%±13% for hip targets and 39%±27% for spine targets) and thus requiring elevated total (hip: 6%±17%, spine: 37%±17%) and peak module-wise (hip: 65%±36%, spine: 101%±56%) acoustic power levels to achieve equivalent focal heating as the no blocking control case. In the remaining sonications, partial module blocking provided further improvements in both (increased by 29%±25% in the hip and 15%±12% in the spine) and focal heating volume (decrease of 20%±10% in the hip and 34%±17% in the spine) relative to the full blocking case. The optimal blocking percentage value was dependent on the specific patient geometry and target location of interest. Although not all individual target locations saw the benefit, element-wise phase aberration corrections improved the average compared to the no correction case (increase of 52%±47% in the hip, 35%±24% in the spine) and impacted the optimal blocking percentage value. Transducer module blocking enabled ablative treatments to be carried out closer to both hip and spine without overheating or damaging the bone (no blocking: / , full blocking: / , optimal partial blocking: / for hip/spine). The proposed transducer apodization scheme shows promise for improving MRgFUS treatments of uterine fibroids, and may ultimately increase the effective treatment envelope of MRgFUS surgery in the body by enabling tissue ablation closer to bonystructures.

3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer’s disease detection using structural MRI

Alzheimer’s disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dementia. While there are no treatments to reverse AD’s progression, spotting the disease’s onset can have a significant impact in the medical field. Deep learning (DL) has revolutionized medical image classification by automating feature engineering, removing the requirement for human experts in feature extraction. DL-based solutions are highly accurate but demand a lot of training data, which poses a common challenge. Transfer learning (TL) has gained attention for its knack for handling limited data and expediting model training. This study uses TL to classify AD using T1-weighted 3D Magnetic Resonance Imaging (MRI) from the Alzheimer’s Disease Neuroimaging (ADNI) database. Four modified pre-trained deep neural networks (DNN), VGG16, MobileNet, DenseNet121, and NASNetMobile, are trained and evaluated on the ADNI dataset. The 3-1-3 weight averaging technique and fine-tuning improve the performance of the classification models. The evaluated accuracies for AD classification are VGG16: 98.75%; MobileNet: 97.5%; DenseNet: 97.5%; and NASNetMobile: 96.25%. The receiver operating characteristic (ROC), precision–recall (PR), and Kolmogorov-Smirnov (KS) statistic plots validate the effectiveness of the modified pre-trained model. Modified VGG16 excels with area under the curve (AUC) values of 0.99 for ROC and 0.998 for PR curves. The proposed approach shows effective AD classification by achieving high accuracy using the 3-1-3 weight averaging technique and fine-tuning.

Global and local feature extraction based on convolutional neural network residual learning for MR image denoising.

Given the different noise distribution information of global and local magnetic resonance (MR) images, this study aims to extend the current work on convolutional neural networks that preserve global structure and local details in MR image denoising tasks. This study proposed a parallel and serial network for denoising 3D MR images, called 3D-PSNet. We use the residual depthwise separable convolution block to learn the local information of the feature map, reduce the network parameters, and thus improve the training speed and parameter efficiency. In addition, we consider the feature extraction of the global image and utilize residual dilated convolution to process the feature map to expand the receptive field of the network and avoid the loss of global information. Finally, we combine both of them to form a parallel network. What's more, we integrate reinforced residual convolution blocks with dense connections to form serial network branches, which can remove redundant information and refine features to further obtain accurate noise information. The peak signal-to-noise ratio, structural similarity index measure, and root mean square error metrics of 3D-PSNet are as high as 47.79%, 99.81%, and 0.40%, respectively, achieving competitive denoising effect on three public datasets. The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets. The proposed 3D-PSNet takes advantage of multi-scale receptive fields, local feature extraction and residual dense connections to more effectively restore the global structure and local fine features in MR images, and is expected to help doctors quickly and accurately diagnose patients' conditions.

Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine.

This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar IVD MRI data from 300 patients were retrospectively analyzed. Two clinicians assessed the manual segmentation and grading for inter-rater reliability using Cohen's kappa. The IVDs were then processed and classified using an automated convolutional neural network (CNN)-GNN pipeline, and their performance was evaluated using F1 scores. Manual Pfirrmann grading exhibited moderate agreement (κ = 0.455-0.565) among the clinicians, with higher exact match frequencies at lower lumbar levels. Single-grade discrepancies were prevalent except at L5/S1. Automated segmentation of IVDs using a pretrained U-Net model achieved an F1 score of 0.85, with a precision and recall of 0.83 and 0.88, respectively. Following 3D reconstruction of the automatically segmented IVD into a 3D point-cloud representation of the target intervertebral disc, the GNN model demonstrated moderate performance in Pfirrmann classification. The highest precision (0.81) and F1 score (0.71) were observed at L2/3, whereas the overall metrics indicated moderate performance (precision: 0.46, recall: 0.47, and F1 score: 0.46), with variability across spinal levels. The integration of CNN and GNN offers a new perspective for automating IVD analysis in MRI. Although the current performance highlights the need for further refinement, the moderate accuracy of the model, combined with its 3D visualization capabilities, establishes a promising foundation for more advanced grading systems.

Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.

Deep Learning-Enhanced Accelerated 2D TSE and 3D Superresolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment.

The aim of this study was to evaluate the use of a multicontrast deep learning (DL)-reconstructed 4-fold accelerated 2-dimensional (2D) turbo spin echo (TSE) protocol and the feasibility of 3-dimensional (3D) superresolution reconstruction (SRR) of DL-enhanced 6-fold accelerated 2D Dixon TSE magnetic resonance imaging (MRI) for comprehensive knee joint assessment, by comparing image quality and diagnostic performance with a conventional 2-fold accelerated 2D TSE knee MRI protocol. This prospective, ethics-approved study included 19 symptomatic adult subjects who underwent knee MRI on a clinical 3 T scanner. Every subject was scanned with 3 DL-enhanced acquisition protocols in a single session: a clinical standard 2-fold in-plane parallel imaging (PI) accelerated 2D TSE-based protocol (5 sequences, 11 minutes 23 seconds) that served as a reference, a DL-reconstructed 4-fold accelerated 2D TSE protocol combining 2-fold PI and 2-fold simultaneous multislice acceleration (5 sequences, 6 minutes 24 seconds), and a 3D SRR protocol based on DL-enhanced 6-fold accelerated (ie, 3-fold PI and 2-fold simultaneous multislice) 2D Dixon TSE MRI (6 anisotropic 2D Dixon TSE acquisitions rotated around the phase-encoding axis, 6 minutes 24 seconds). This resulted in a total of 228 knee MRI scans comprising 21,204 images. Three readers evaluated all pseudonymized and randomized images in terms of image quality using a 5-point Likert scale. Two of the readers (musculoskeletal radiologists) additionally evaluated anatomical visibility and diagnostic confidence to assess normal and pathological knee structures with a 5-point Likert scale. They recorded the presence and location of internal knee derangements, including cartilage defects, meniscal tears, tears of ligaments, tendons and muscles, and bone injuries. The statistical analysis included nonparametric Friedman tests, and interreader and intrareader agreement assessment using the weighted Fleiss-Cohen kappa (κ) statistic. P values of less than 0.05 were considered statistically significant. The evaluated DL-enhanced 4-fold accelerated 2D TSE protocol provided very similar image quality and anatomical visibility to the standard 2D TSE protocol, whereas the 3D SRR Dixon TSE protocol scored less in terms of overall image quality due to reduced edge sharpness and the presence of artifacts (P < 0.001). Subjective signal-to-noise ratio, contrast resolution, fluid brightness, and fat suppression were good to excellent for all protocols. For 1 reader, the Dixon method of the 3D SRR protocol provided significantly better fat suppression than the spectral fat saturation applied in the standard 2D TSE protocol (P < 0.05). The visualization of knee structures with 3D SRR Dixon TSE was very similar to the standard protocol, except for cartilage, tendons, and bone, which were affected by the presence of reconstruction and aliasing artifacts (P < 0.001). The diagnostic confidence of both readers was high for all protocols and all knee structures, except for cartilage and tendons. The standard 2D TSE protocol showed a significantly higher diagnostic confidence for assessing tendons than 3D SRR Dixon TSE MRI (P < 0.01). The interreader and intrareader agreement for the assessment of internal knee derangements using any of the 3 protocols was substantial to almost perfect (κ = 0.67-1.00). For cartilage, the interreader agreement was substantial for DL-enhanced accelerated 2D TSE (κ = 0.79) and almost perfect for standard 2D TSE (κ = 0.98) and 3D SRR Dixon TSE (κ = 0.87). For menisci, the interreader agreement was substantial for 3D SRR Dixon TSE (κ = 0.70-0.80) and substantial to almost perfect for standard 2D TSE (κ = 0.80-0.99) and DL-enhanced 2D TSE (κ = 0.87-1.00). Moreover, the total acquisition time was reduced by 44% when using the DL-enhanced accelerated 2D TSE or 3D SRR Dixon TSE protocol instead of the conventional 2D TSE protocol. The presented DL-enhanced 4-fold accelerated 2D TSE protocol provides image quality and diagnostic performance similar to the standard 2D protocol. Moreover, the 3D SRR of DL-enhanced 6-fold accelerated 2D Dixon TSE MRI is feasible for multicontrast 3D knee MRI as its diagnostic performance is comparable to standard 2-fold accelerated 2D knee MRI. However, reconstruction and aliasing artifacts need to be further addressed to guarantee a more reliable visualization and assessment of cartilage, tendons, and bone. Both the 2D and 3D SRR DL-enhanced protocols enable a 44% faster examination compared with conventional 2-fold accelerated routine 2D TSE knee MRI and thus open new paths for more efficient clinical 2D and 3D knee MRI.

Neuroimaging of autobiographical memory in dementia with Lewy bodies: a story of insula.

Although deficits in learning and retrieving new information are well characterized in dementia with Lewy bodies, autobiographical memory has never been explored in this disease. Yet, autobiographical memory impairments are a pervasive feature of dementia, well characterized in other neurodegenerative diseases. Moreover, autobiographical memory corresponds to an extension over time of the self, which we hypothesize is altered in dementia with Lewy bodies and impairment of which could be linked to the insular atrophy occurring from an early stage of the disease. In this study, we sought to characterize autobiographical memory impairments and explore their neural correlates in dementia with Lewy bodies, on the assumption that insular damage could impact the self, including its most elaborate components, such as autobiographical memory. Twenty patients with prodromal to mild dementia with Lewy bodies were selected to participate in this exploratory study along with 20 healthy control subjects. The Autobiographical Interview was used to assess autobiographical memory. Performances were compared between patients and control subjects, and an analysis across life periods and recall conditions was performed. 3D magnetic resonance images were acquired for all participants, and correlational analyses were performed in the patient group using voxel-based morphometry. The behavioural results of the Autobiographical Interview showed that autobiographical memory performances were significantly impaired in dementia with Lewy body patients compared to control subjects in a temporally ungraded manner, for both the free recall and the specific probe conditions (P < 0.0001), though with greater improvement after probing in the patient group. Furthermore, autobiographical memory impairments were correlated with grey matter volume within right insular cortex, temporoparietal junction, precuneus, putamen, left temporal cortex, bilateral parahippocampus and cerebellum, using a threshold of P = 0.005 uncorrected. The behavioural results confirm the existence of temporally ungraded autobiographical memory impairments in dementia with Lewy bodies, from the early stage of the disease. As we expected, neuroimaging analysis revealed a role for the insula and the precuneus in autobiographical memory retrieval, two regions associated with elementary aspects of the self, among other brain regions classically associated with autobiographical memory, such as medial temporal lobe and temporoparietal junction. Our findings provide important insights regarding the involvement of the insula in the self and suggest that insular damage could lead to a global collapse of the self, including its more elaborated components, such as autobiographical memory.

3D Quantitative-Amplified Magnetic Resonance Imaging (3D q-aMRI).

Amplified MRI (aMRI) is a promising new technique that can visualize pulsatile brain tissue motion by amplifying sub-voxel motion in cine MRI data, but it lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a novel post-processing algorithm called 3D quantitative amplified MRI (3D q-aMRI). This algorithm enables the visualization and quantification of pulsatile brain motion. 3D q-aMRI was validated and optimized on a 3D digital phantom and was applied in vivo on healthy volunteers for its ability to accurately measure brain parenchyma and CSF voxel displacement. Simulation results show that 3D q-aMRI can accurately quantify sub-voxel motions in the order of 0.01 of a voxel size. The algorithm hyperparameters were optimized and tested on in vivo data. The repeatability and reproducibility of 3D q-aMRI were shown on six healthy volunteers. The voxel displacement field extracted by 3D q-aMRI is highly correlated with the displacement measurements estimated by phase contrast (PC) MRI. In addition, the voxel displacement profile through the cerebral aqueduct resembled the CSF flow profile reported in previous literature. Differences in brain motion was observed in patients with dementia compared with age-matched healthy controls. In summary, 3D q-aMRI is a promising new technique that can both visualize and quantify pulsatile brain motion. Its ability to accurately quantify sub-voxel motion in physical units holds potential for the assessment of pulsatile brain motion as well as the indirect assessment of CSF homeostasis. While further research is warranted, 3D q-aMRI may provide important diagnostic information for neurological disorders such as Alzheimer's disease.

Multicompartment imaging of the brain using a comprehensive MR imaging protocol

In this study, we describe a comprehensive 3D magnetic resonance imaging (MRI) protocol designed to assess major tissue and fluid components in the brain. The protocol comprises four different sequences: 1) magnetization transfer prepared Cones (MT-Cones) for two-pool MT modeling to quantify macromolecular content; 2) short-TR adiabatic inversion-recovery prepared Cones (STAIR-Cones) for myelin water imaging; 3) proton-density weighted Cones (PDw-Cones) for total water imaging; and 4) highly T2 weighted Cones (T2w-Cones) for free water imaging. By integrating these techniques, we successfully mapped key brain components—namely macromolecules, myelin water, intra/extracellular water, and free water—in ten healthy volunteers and five patients with multiple sclerosis (MS) using a 3T clinical scanner. Brain macromolecular proton fraction (MMPF), myelin water proton fraction (MWPF), intra/extracellular water proton fraction (IEWPF), and free water proton fraction (FWPF) values were generated in white matter (WM), grey matter (GM), and MS lesions. Excellent repeatability of the protocol was demonstrated with high intra-class correlation coefficient (ICC) values. In MS patients, the MMPF and MWPF values of the lesions and normal-appearing WM (NAWM) were significantly lower than those in normal WM (NWM) in healthy volunteers. Moreover, we observed significantly higher FWPF values in MS lesions compared to those in NWM and NAWM regions. This study demonstrates the capability of our technique to volumetrically map major brain components. The technique may have particular value in providing a comprehensive assessment of neuroinflammatory and neurodegenerative diseases of the brain.

NCNet: Deformable medical image registration network based on neighborhood cross-attention combined with multi-resolution constraints

Objective. Existing registration networks based on cross-attention design usually divide the image pairs to be registered into patches for input. The division and merging operations of a series of patches are difficult to maintain the topology of the deformation field and reduce the interpretability of the network. Therefore, our goal is to develop a new network architecture based on a cross-attention mechanism combined with a multi-resolution strategy to improve the accuracy and interpretability of medical image registration. Approach. We propose a new deformable image registration network NCNet based on neighborhood cross-attention combined with multi-resolution strategy. The network structure mainly consists of a multi-resolution feature encoder, a multi-head neighborhood cross-attention module and a registration decoder. The hierarchical feature extraction capability of our encoder is improved by introducing large kernel parallel convolution blocks; the cross-attention module based on neighborhood calculation is used to reduce the impact on the topology of the deformation field and double normalization is used to reduce its computational complexity. Main result. We performed atlas-based registration and inter-subject registration tasks on the public 3D brain magnetic resonance imaging datasets LPBA40 and IXI respectively. Compared with the popular VoxelMorph method, our method improves the average DSC value by 7.9% and 3.6% on LPBA40 and IXI. Compared with the popular TransMorph method, our method improves the average DSC value by 4.9% and 1.3% on LPBA40 and IXI. Significance. We proved the advantages of the neighborhood attention calculation method compared to the window attention calculation method based on partitioning patches, and analyzed the impact of the pyramid feature encoder and double normalization on network performance. This has made a valuable contribution to promoting the further development of medical image registration methods.

Hypoperfusion of hypothalamic subunits in medication-overuse headache using a 3D PCASL MRI

Hypothalamus is a crucial deep brain area that is responsible for the integration and coordination of various brain functions. The altered perfusion of hypothalamus during headache caused by medication-overuse headache (MOH) was previously unknown. In the current study, the altered perfusion of hypothalamic subregions in MOH patients was investigated using state-of-the-art 3D pseudo-continuous arterial spin labeling (PCASL) MR imaging. In this study, 29 normal controls subjects (NCs) and 29 MOH patients underwent 3D PCASL and brain structural MR imaging. The hypothalamus was automatically segmented into 10 subunits and the volume of each subunit was automatically determined using Freesurfer software (v7.4.1). All segmented hypothalamic subunits were converted to individual hypothalamic subunit masks. The cerebral blood flow (CBF) images were coregistered with the raw brain structural images and resliced. The CBF value of each hypothalamic subunit was extracted from the warped CBF images. The volume and CBF value of each hypothalamic subunit were analyzed using the independent sample T test and Mann–Whitney U test, receiver operating characteristic (ROC) curve analysis, and Pearson and Spearman correlation analysis. Hypothalamic subunits with significantly decreased perfusion were located in the left posterior, left tubular superior, right anterior–inferior, right tubular inferior, right tubular superior, right posterior subunit and the entire right hypothalamus [CBF value for MOH vs NC (mL/100 g·min): 48.41 ± 6.75 vs 54.08 ± 11.47, 44.44 ± 4.79 vs 48.11 ± 7.73, 41.49 (32.90, 61.46) vs 49.38 ± 10.47, 46.62 ± 7.04 vs 53.90 ± 11.75, 42.12 ± 5.74 vs 47.02 ± 9.99, 42.79 ± 5.15 vs 47.93 ± 10.48 and 43.58 ± 5.06 vs 48.65 ± 9.33, respectively] in MOH compared to NC (P < 0.05). ROC analysis for these positive subunits revealed that area under the curve was 0.658–0.693, and ROC curve for left posterior subunit had the highest specificity of 93.10% while the entire right hypothalamus had the highest sensitivity of 72.41%. Further correlation analysis showed that the CBF value of the left posterior, right anterior–inferior, right tubular superior, whole right hypothalamus presented significantly negative correlation with Hamilton Depression Scale (HAMD) score (P < 0.05). Hypoperfusion of hypothalamic subunits may contribute to the understanding of MOH pathogenesis, and the 3D PCASL could be considered as a potential diagnostic and assessment tool for MOH.

Visibility and Size of the Pancreatic Duct and Common Bile Duct on Routine 2D MRI Sequences in Children Without Pancreaticobiliary Disease.

BACKGROUND. Pancreatic duct (PD) or common bile duct (CBD) dilatation can indicate ductal pathology, but limited data describe normal pediatric duct measurements on routine 2D MRI sequences. OBJECTIVE. The purpose of this study was to characterize the visibility and diameter of the PD and CBD on 2D MR images in children without pancreaticobiliary disease. METHODS. This retrospective study included patients who underwent abdominal MRI using a rapid protocol (composed of noncontrast axial and coronal 2D SSFSE sequences) to assess for suspected appendicitis or ovarian torsion in the emergency department setting between January 23, 2023, and September 13, 2023, and excluded patients with a pancreatic or hepatobiliary abnormality on MRI or laboratory assessment. Four pediatric radiologists independently reviewed examinations. Reviewers recorded PD visibility in each of four segments (i.e., head, neck, body, and tail) and CBD visibility, and reviewers measured PD diameter in each segment and maximal CBD diameter. Duct measurements by age were characterized by linear regression analyses. RESULTS. The study included 177 patients (112 female patients, 65 male patients; mean age, 12.3 ± 3.4 [SD] years [age range, 5.1-17.7 years]). The observers reported PD visibility in the head in 35.6-94.9% of patients, neck in 18.6-72.3%, body in 22.6-89.8%, and tail in 7.3-26.0% and reported PD visibility in all four segments in 6.2-22.6% of patients. Maximum PD diameter in any segment, as a mean across observers, was 1.8 mm (range across observers, 0.7-3.5 mm). The expected maximal PD diameter in any segment, in terms of the 5th and 95th percentile values of observers' mean measurements, was 1.4-2.3 mm; the prediction interval's upper limit increased from age 5 to age 17 from 2.1 to 2.5 mm. All observers reported CBD visibility in all patients. The mean CBD diameter across observers was 3.1 mm (range across observers, 2.9-3.4 mm). The expected CBD diameter, in terms of the 5th and 95th percentile values of observers' mean measurements, was 2.3-4.9 mm; the prediction interval's upper limit increased from age 5 to age 17 from 3.9 to 5.0 mm. CONCLUSION. We report expected upper limits for PD and CBD measurements on 2D MR images in children without evidence of pancreaticobiliary disease. CLINICAL IMPACT. These findings may aid radiologists' identification of pancreaticobiliary duct abnormalities on routine abdominal MRI examinations.

Dementia with Lewy bodies and gait neural basis: a cross-sectional study

BackgroundDementia with Lewy Bodies (DLB) is responsible for cognitive-behavioural disorders but also for gait disorders. The latter are thought to be related to parkinsonism, but the neural bases of these disorders are not well known, especially in the early stages. The aim of this study was to investigate by volumetric Magnetic Resonance Imaging the neuronal basis of gait disorders in DLB patients, compared to Healthy Elderly Controls and Alzheimer’s Disease patients.MethodsClinical examination with motor assessment including 10-meter walking speed, one-leg balance and Timed Up and Go test, a comprehensive neuropsychological evaluation and 3D brain Magnetic Resonance Imaging were performed on 84 DLB patients, 39 Alzheimer’s Disease patients and 22 Healthy Elderly Controls. We used Statistical Parametric Mapping 12 to perform a one-sample t-test to investigate the correlation between each gait score and gray matter volume (P ≤ 0.05 corrected for family-wise error).ResultsWe found a correlation for DLB patients between walking speed and gray matter decrease (P < 0.05, corrected for family-wise error) in caudate nuclei, anterior cingulate cortex, mid-cingulate cortex, hippocampi, supplementary motor area, right cerebellar cortex and left parietal operculum. We found no correlation with Timed Up and Go test and one-leg balance.ConclusionGait disorders are underpinned by certain classical regions such as the cerebellum and the supplementary motor area. Our results suggest there may be a motivational and emotional component of voluntary gait in DLB subjects, underpinned by the cingulate cortex, a spatial orientation component, underpinned by hippocampi and suggest the involvement of brain processing speed and parkinsonism, underpinned by the caudate nuclei.Trial registrationThe study protocol has been registered on ClinicalTrials.gov. (NCT01876459) on June 12, 2013.

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

PurposeThis study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.Design/methodology/approachMultiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.FindingsThe study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.Originality/valueThis study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.