3D Magnetic Resonance Imaging Research Articles

Sorting lung tumor volumes from 4D-MRI data using an automatic tumor-based signal reduces stitching artifacts.

To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D-magnetic resonance (4D-MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. (4D-MRI) scans were collected for 10 lung cancer patients using a 2D T2-weighted single-shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor-motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. The TumorPC1 signal was most strongly correlated with superior-inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior-inferior tumor motion (p<0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p=0.02-0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy.

Comparing flow and pulmonary artery growth post-patent ductus arteriosus stenting in patients with ductal-dependent pulmonary flow using 4D magnetic resonance imaging.

The 4D magnetic resonance imaging (4D-flow MRI) provides a qualitative and quantitative assessment of cardiovascular structures and processes. 4D-flow MRI was used to study pulmonary flow in post-patent ductus arteriosus (PDA) stent insertion in duct-dependent pulmonary flow neonates at baseline (PDA stent insertion) and after 6 months, and also, to evaluate the effect of flow dynamics on the growth of pulmonary arteries (PAs). This prospective observational study included neonates with ductus arteriosus-dependent pulmonary circulation who underwent ductal stenting between June 2021 and November 2022. Cardiac 4D-flow MRI and magnetic resonance angiography were conducted in two phases; after the deployment of the PDA stent during the neonatal period and after 6 months from stent deployment. Eight neonates were recruited, but only five completed both scans. A total of 10 PAs were evaluated during each phase. The median left PA (LPA) and right PA (RPA) diameters and indexed flow for LPA and RPA were evaluated. The growth rate of LPA was observed to be lower than that of RPA (percentage diameter increase: 74 vs. 153%). LPA Z-score was lower than RPA. Indexed flow in both LPA and RPA showed a reduction in the 6-month scan, which was consistent with reduced stent patency. 4D-flow cardiac MRI showed different growth rates and reduced flow between LPA and RPA post-PDA stent. These insights can aid in future management decisions.

Comparison of Free-Breathing 3D Phase-Resolved Functional Lung (PREFUL) MRI With Dynamic 19 F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers.

Non-contrast-enhanced 1 H magnetic resonance imaging (MRI) with full lung coverage shows promise for assessment of regional lung ventilation but a comparison with direct ventilation measurement using 19 F MRI is lacking. To compare ventilation parameters calculated using 3D phase-resolved functional lung (PREFUL) MRI with 19 F MRI. Prospective. Fifteen patients with asthma, 14 patients with chronic obstructive lung disease, and 13 healthy volunteers. A 3D gradient-echo pulse sequence with golden-angle increment and stack-of-stars encoding at 1.5 T. All participants underwent 3D PREFUL MRI and 19 F MRI. For 3D PREFUL, static regional ventilation (RVent) and dynamic flow-volume cross-correlation metric (FVL-CM) were calculated. For both parameters, ventilation defect percentage (VDP) values and ventilation defect (VD) maps (including a combination of both parameters [VDPCombined ]) were determined. For 19 F MRI, images from eight consecutive breaths under volume-controlled inhalation of perfluoropropane were acquired. Time-to-fill (TTF) and wash-in (WI) parameters were extracted. For all 19 F parameters, a VD map was generated and the corresponding VDP values were calculated. For all parameters, the relationship between the two techniques was assessed using a Spearman correlation (r). Differences between VDP values were compared using Bland-Altman analysis. For regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients were computed. 3D PREFUL VDP values were significantly correlated to VDP measures by 19 F (r range: 0.59-0.70). For VDPRVent , no significant bias was observed with VDP of the third and fourth breath (bias range = -6.8:7.7%, P range = 0.25:0.30). For VDPFVL-CM , no significant bias was found with VDP values of fourth-eighth breaths (bias range = -2.0:12.5%, P range = 0.12:0.75). The overall spatial overlap of all VD maps increased with each breath, ranging from 61% to 81%, stabilizing at the fourth breath. 3D PREFUL MRI parameters showed moderate to strong correlation with 19 F MRI. Depending on the 3D PREFUL VD map, the best regional agreement was found to 19 F VD maps of third-fifth breath. 3 TECHNICAL EFFICACY: Stage 2.

EEUR-Net: End-to-End Optimization of Under-Sampling and Reconstruction Network for 3D Magnetic Resonance Imaging

It is time-consuming to acquire complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Under-sampling in the 3D k-space is promising in accelerating such 3D MRI process. Although 3D under-sampling can be conducted according to predefined probability density, the density-based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D under-sampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific under-sampling patterns for the fully sampled 3D k-space data. Innovatively, our under-sampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of under-sampled data. Through end-to-end training, we obtain an optimized 3D under-sampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrate that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian under-sampling schemes, our end-to-end learned under-sampling pattern considerably improves the reconstruction quality of under-sampled MRI images.

3D BIOPRINTING TISSUE-ENGINEERED MENISCAL CONSTRUCTS

Meniscal injuries affect over 1.5 million people across Europe and the USA annually. Injury greatly reduces knee joint mobility and quality of life and frequently leads to the development of osteoarthritis. Tissue engineered strategies have emerged in response to a lack of viable treatments for meniscal pathologies. However, to date, constructs mimicking the structural and functional organisation of native tissue, whilst promoting deposition of new extracellular matrix, remains a bottleneck in meniscal repair. 3D bioprinting allows for deposition and patterning of biological materials with high spatial resolution. This project aims to develop a biomimetic 3D bioprinted meniscal substitute.Meniscal tissue was characterised to effectively inform the design of biomaterials for bioprinting constructs with appropriate structural and functional properties. Histology, gene expression and mass spectrometry were performed on native tissue to investigate tissue architecture, matrix components, cell populations and protein expression regionally across the meniscus. 3D laser scanning and magnetic resonance imaging were employed to acquire the external geometrical information prior to fabrication of a 3D printed meniscus. Bioink suitability was investigated through regional meniscal cell encapsulation in blended hydrogels, with the incorporation of growth factors and assessed for their suitability through rheology, scanning electron microscopy, histology and gene expression analysis.Meniscal tissue characterisation revealed regional variations in matrix compositions, cellular populations and protein expression. The process of imaging through to 3D printing highlighted the capability of producing a construct that accurately replicated meniscal geometries. Regional meniscal cell encapsulation into hydrogels revealed a recovery in cell phenotype, with the incorporation of growth factors into the bioink's stimulating cellular re-differentiation and improved zonal functionality.Meniscus biofabrication highlights the potential to print patient specific, customisable meniscal implants. Achieving zonally distinct variations in cell and matrix deposition highlights the ability to fabricate a highly complex tissue engineered construct.Acknowledgements: This work was undertaken as part of the UK Research and Innovation (UKRI)-funded CDT in Advanced Biomedical Materials.

Evaluating the clinical applicability of neural networks for meningioma tumor segmentation on 3D MRI

Magnetic resonance imaging (MRI), in both 2D and 3D configurations, serves as the leading non-invasive and non-ionizing technique for the detection and clinical assessment of brain tumors. 3D multiparametric MRIs (mpMRIs) offer enhanced spatial and biological context over other MRIs, which can help clinicians plan tailored treatment and therapy. Presently, surgical intervention remains the primary treatment method for brain tumors, requiring accurate segmentation of tumor tissue on MRI scans. However, manual tumor segmentation and traditional machine learning techniques are labor intensive and subject to observer bias. Deep learning models can provide enhanced accuracy and precision in tumor segmentation due to their ability to automatically extract intricate features and patterns from MRI scans. In this study, MRI scans from 358 meningioma patients were used to train a series of deep learning models to segment the tumor subregions. We hypothesized that a deep learning framework designed for meningioma tumor segmentation and trained utilizing 3D mpMRIs would demonstrate superior segmentation accuracy compared to a framework trained on 2D MRIs or single-sequence 3D MRIs. We compared the model predictions with the ground truth labels using similarity metrics to assess accuracy and clinical applicability. The model trained on 3D mpMRI scans showed reliable performance for meningioma subregion segmentation with a Dice Similarity Coefficient score of 0.91 and a median Sensitivity of 91.38%. It outperformed the other models due to the additional spatial context provided by 3D mpMRIs, suggesting it could be readily used in clinical practice to help guide treatment strategy.

The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images

Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.

Thin-slice 2D MR Imaging of the Shoulder Joint Using Denoising Deep Learning Reconstruction Provides Higher Image Quality Than 3D MR Imaging

This study was conducted to evaluate whether thin-slice 2D fat-saturated proton density-weighted images of the shoulder joint in three imaging planes combined with parallel imaging, partial Fourier technique, and denoising approach with deep learning-based reconstruction (dDLR) are more useful than 3D fat-saturated proton density multi-planar voxel images. Eighteen patients who underwent MRI of the shoulder joint at 3T were enrolled. The denoising effect of dDLR in 2D was evaluated using coefficient of variation (CV). Qualitative evaluation of anatomical structures, noise, and artifacts in 2D after dDLR and 3D was performed by two radiologists using a five-point Likert scale. All were analyzed statistically. Gwet's agreement coefficients were also calculated. The CV of 2D after dDLR was significantly lower than that before dDLR (P < 0.05). Both radiologists rated 2D higher than 3D for all anatomical structures and noise (P < 0.05), except for artifacts. Both Gwet's agreement coefficients of anatomical structures, noise, and artifacts in 2D and 3D produced nearly perfect agreement between the two radiologists. The evaluation of 2D tended to be more reproducible than 3D. 2D with parallel imaging, partial Fourier technique, and dDLR was proved to be superior to 3D for depicting shoulder joint structures with lower noise.

The Value of 3D SPACE MRI in Differentiating between Sequestrated Lumbar Disc Herniation and Tumors: Two Cases and Literature.

Intervertebral disc herniation, defined as the protrusion or extrusion of the disc mass outside the disc space, is common and easy to diagnose on conventional Magnetic Resonance imaging (MRI) or Computed Tomography (CT) scans. However, the sequestrated disc fragments are challenging to detect, and intervertebral disc mass displacement into the dural sac, which can lead to serious neurological problems such as Cauda equina syndrome (CES). The sequestrated disc fragments do not have specific clinical or radiological characteristics that can differentiate an atypical disc mass from a tumor, making the diagnosis difficult preoperatively. Herein, we describe the use of Sampling Perfection with Application Optimized Contrast using different flip angle Evolution in Magnetic Resonance Imaging (3D SPACE MRI) in the diagnosis of the intervertebral disc fragment that mimicked a tumor. In this study, we report two cases of sequestered lumbar disc herniation. The first case was a 37-year-old patient with a 2-year history of intermittent left lower limb pain that aggravates with exercise and is relieved at rest, while the second case was a 42-year-old patient with a history of 40 days of numbness and pain in the left lower limb. 3D SPACE MRI is a beneficial diagnostic imaging tool for discriminating between disc mass that mimics a tumor and a tumor before surgery. If the disc fragment mimicking a tumor can be identified before the operation, open surgical treatment won't be necessary for all patients.

Three-dimensional Lagrangian coherent structures in patients with aortic regurgitation

Understanding blood transport in cardiovascular flows is important for managing patients with cardiovascular disease. In this study, three-dimensional Lagrangian coherent structures have been extracted for the first time in both healthy patients and patients with aortic regurgitation. To achieve this, a computationally efficient approach based on Lagrangian descriptors was employed with four-dimensional (4D) magnetic resonance imaging velocity fields. In healthy subjects, Lagrangian coherent structures analysis revealed well-defined mitral jet structures during early filling, directing flow toward ejection during systole. For patients with aortic regurgitation, complex flow structures included interactions between the mitral and regurgitant jets, indicating altered blood transport mechanisms. This study highlights the ability of Lagrangian descriptors to extract coherent structures from patient-specific 4D flow MRI data in a computationally efficient way. It also underscores the importance of extracting three-dimensional Lagrangian coherent structures to gain a better understanding of the complex interaction between the mitral inflow and the regurgitant jet.

2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are the two mimic autoimmune diseases of the central nervous system, which are rare in East Asia. Quantitative detection of contrast-enhancing lesions (CELs) on contrast-enhancing T1-weighted magnetic resonance (MR) images is of great significance for assessing the disease activity of MS and NMOSD. However, it is challenging to develop automatic segmentation algorithms due to the lack of data. In this work, we present an automatic segmentation model of CELs based on Fully Convolutional with Attention DenseNet (FCA-DenseNet) and transfer learning strategy to address the challenge of CEL quantification in small-scale datasets. A transfer learning approach was employed in this study, whereby pretraining was conducted using 77 MS subjects from the open access datasets (MICCAI 2016, MICCAI 2017, ISBI 2015) for white matter hyperintensity segmentation, followed by fine-tuning using 24 MS and NMOSD subjects from the local dataset for CEL segmentation. The proposed FCA-DenseNet combined the Fully Convolutional DenseNet and Convolutional Block Attention Module in order to improve the learning capability. A 2.5D data slicing strategy was used to process complex 3D MR images. U-Net, ResUNet, TransUNet, and Attention-UNet are used as comparison models to FCA-DenseNet. Dice similarity coefficient (DSC), positive predictive value (PPV), true positive rate (TPR), and volume difference (VD) are used as evaluation metrics to evaluate the performances of different models. FCA-DenseNet outperforms all other models in terms of all evaluation metrics, with a DSC of 0.661±0.187, PPV of 0.719±0.201, TPR of 0.680±0.254, and VD of 0.388±0.334. Transfer learning strategy has achieved success in building segmentation models on a small-scale local dataset where traditional deep learning approaches fail to train effectively. The improved FCA-DenseNet, combined with transfer learning strategy and 2.5D data slicing strategy, has successfully addressed the challenges in constructing deep learning models on small-scale datasets, making it conducive to clinical quantification of brain CELs and diagnosis of MS and NMOSD.

Image Reconstruction for Accelerated MR Scan With Faster Fourier Convolutional Neural Networks.

High quality image reconstruction from undersampled k -space data is key to accelerating MR scanning. Current deep learning methods are limited by the small receptive fields in reconstruction networks, which restrict the exploitation of long-range information, and impede the mitigation of full-image artifacts, particularly in 3D reconstruction tasks. Additionally, the substantial computational demands of 3D reconstruction considerably hinder advancements in related fields. To tackle these challenges, we propose the following: 1) A novel convolution operator named Faster Fourier Convolution (FasterFC), aims at providing an adaptable broad receptive field for spatial domain reconstruction networks with fast computational speed. 2) A split-slice strategy that substantially reduces the computational load of 3D reconstruction, enabling high-resolution, multi-coil, 3D MR image reconstruction while fully utilizing inter-layer and intra-layer information. 3) A single-to-group algorithm that efficiently utilizes scan-specific and data-driven priors to enhance k -space interpolation effects. 4) A multi-stage, multi-coil, 3D fast MRI method, called the faster Fourier convolution based single-to-group network (FAS-Net), comprising a single-to-group k -space interpolation algorithm and a FasterFC-based image domain reconstruction module, significantly minimizes the computational demands of 3D reconstruction through split-slice strategy. Experimental evaluations conducted on the NYU fastMRI and Stanford MRI Data datasets reveal that the FasterFC significantly enhances the quality of both 2D and 3D reconstruction results. Moreover, FAS-Net, characterized as a method that can achieve high-resolution (320, 320, 256), multi-coil, (8 coils), 3D fast MRI, exhibits superior reconstruction performance compared to other state-of-the-art 2D and 3D methods.

Coarse-Super-Resolution-Fine Network (CoSF-Net): A Unified End-to-End Neural Network for 4D-MRI With Simultaneous Motion Estimation and Super-Resolution.

Four-dimensional magnetic resonance imaging (4D-MRI) is an emerging technique for tumor motion management in image-guided radiation therapy (IGRT). However, current 4D-MRI suffers from low spatial resolution and strong motion artifacts owing to the long acquisition time and patients' respiratory variations. If not managed properly, these limitations can adversely affect treatment planning and delivery in IGRT. In this study, we developed a novel deep learning framework called the coarse-super-resolution-fine network (CoSF-Net) to achieve simultaneous motion estimation and super-resolution within a unified model. We designed CoSF-Net by fully excavating the inherent properties of 4D-MRI, with consideration of limited and imperfectly matched training datasets. We conducted extensive experiments on multiple real patient datasets to assess the feasibility and robustness of the developed network. Compared with existing networks and three state-of-the-art conventional algorithms, CoSF-Net not only accurately estimated the deformable vector fields between the respiratory phases of 4D-MRI but also simultaneously improved the spatial resolution of 4D-MRI, enhancing anatomical features and producing 4D-MR images with high spatiotemporal resolution.

Comparison of T2-Weighted 3D-Space and T2-Weighted 2D-TSE Sequences in Lumbar Spine Magnetic Resonance Imaging (MRI)

Background: Spine-related issues like low back pain have substantially increased in the past few decades across all age categories having various reasons. Objectives: To compare the image quality and diagnostic value of 3D space MRI sequence with 2D TSE MRI sequence in lumbar spine imaging. Methods: Fifty patients, irrespective of gender having age between 30-70 years, who presented with lower back pain were enrolled for the study. Lumbar spine imaging was done using 2D TSE and 3D space sequences and the subsequent images were evaluated by two senior consultant radiologists for qualitative analysis of visibility and pathologic indices of both sequences. Data was analyzed on SPSS 25.0. Statistical analysis was done using Cohen's kappa (k). Results: Female participants were 56% of total study population. Mean age of study participants found to be 53.62±10.76 years. Statistically significant difference (p-value =0.000) was noted for visibility score of all the lumbar spine components, when the 3D SPACE and 2D TSE images were compared with 3D-SPACE images found being superior in visibility. Inter- observer agreement for visibility of various regions of lumber spine was nearly perfect (k&gt; 0.8) and substantial (0.6 to 0.8) for 3D-SPACE, while substantial (0.8&lt;k&gt; 0.6) for 2D-TSE. Pathologic indexes had a significant level of inter-observer agreement (k &gt; 0.6). Inter-observer agreement for 3D sequence (k = 0.72) was greater than that for 2D-TSE sequence (k = 0.64). Conclusion: Images from 3D-SPACE sequences provide superior visibility over 2D-TSE and could be recommended as alternative imaging method for lumbar spine pathologies. Higher inter observer agreement illustrate that it may be included in routine sequences to make the better diagnosis for lumbar spine pathologies correctly.

Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation.

Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating three-dimensional (3D) MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. To build a deep learning method exploring sparse annotations, namely only a single two-dimensional slice label for each 3D training MR image. Retrospective. Three-dimensional MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1377 image slices) are for prostate segmentation. The other 100 (8800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. 1.5 T and 3.0 T; axial T2-weighted and late gadolinium-enhanced, 3D respiratory navigated, inversion recovery prepared gradient echo pulse sequence. A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled noncentral slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Quantitative evaluation metrics including boundary intersection-over-union (B-IoU), Dice similarity coefficient, average symmetric surface distance, and relative absolute volume difference were calculated. Paired t test was performed, and P < 0.05 was considered statistically significant. Compared to fully supervised training with only the labeled central slice, mean teacher, uncertainty-aware mean teacher, deep co-training, interpolation consistency training (ICT), and ambiguity-consensus mean teacher, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% ± 7.6% vs. ICT B-IoU: 60.3% ± 11.2%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% ± 6.8% vs. ICT B-IoU: 60.1% ± 7.1%). A collaborative learning method trained using sparse annotations can segment prostate and left atrium with high accuracy. 0 TECHNICAL EFFICACY: Stage 1.

Diagnostic Performance of Magnetic Resonance Imaging for Parathyroid Localization of Primary Hyperparathyroidism: A Systematic Review.

Accurate preoperative localization is crucial for successful minimally invasive parathyroidectomy in primary hyperparathyroidism (PHPT). Preoperative localization can be challenging in patients with recurrent and/or multigland disease (MGD). This has led clinicians to investigate multiple imaging techniques, most of which are associated with radiation exposure. Magnetic resonance imaging (MRI) offers ionizing radiation-free and accurate imaging, making it an attractive alternative imaging modality. The objective of this systematic review is to provide an overview of the diagnostic performance of MRI in the localization of PHPT. PubMed and Embase libraries were searched from 1 January 2000 to 31 March 2023. Studies were included that investigated MRI techniques for the localization of PHPT. The exclusion criteria were (1) secondary/tertiary hyperparathyroidism, (2) studies that provided no diagnostic performance values, (3) studies published before 2000, and (4) studies using 0.5 Tesla MRI scanners. Twenty-four articles were included in the systematic review, with a total of 1127 patients with PHPT. In 14 studies investigating conventional MRI for PHPT localization, sensitivities varied between 39.1% and 94.3%. When employing more advanced MRI protocols like 4D MRI for PHPT localization in 11 studies, sensitivities ranged from 55.6% to 100%. The combination of MR imaging with functional techniques such as 18F-FCH-PET/MRI yielded the highest diagnostic accuracy, with sensitivities ranging from 84.2% to 100% in five studies. Despite the limitations of the available evidence, the results of this review indicate that the combination of MR imaging with functional imaging techniques such as 18F-FCH-PET/MRI yielded the highest diagnostic accuracy. Further research on emerging MR imaging modalities, such as 4D MRI and PET/MRI, is warranted, as MRI exposes patients to minimal or no ionizing radiation compared to other imaging modalities.

MLC: Multi-level consistency learning for semi-supervised left atrium segmentation

Atrial fibrillation is the most common type of arrhythmia associated with a high mortality rate. Left atrium segmentation is crucial for the diagnosis and treatment of atrial fibrillation. Accurate left atrium segmentation with limited labeled data is a tricky problem. In this paper, a novel multi-level consistency semi-supervised learning method is proposed for left atrium segmentation from 3D magnetic resonance images. The proposed framework can efficiently utilize limited labeled data and large amounts of unlabeled data by performing consistency predictions under task level, data level, and feature level perturbations. For task consistency, the segmentation results and signed distance maps were used for both segmentation and distance estimation tasks. For data level perturbation, random flips (horizontal or vertical) were introduced for unlabeled data. Moreover, based on virtual adversarial training, we design a multi-layer feature perturbation in the structure of skipping connection. Our method is evaluated on the publicly available Left Atrium Segmentation Challenge dataset version 2018. For the model trained with a label rate of 20%, the evaluation metrics Dice, Jaccard, ASD, and 95HD are 91.69%, 84.71%, 1.43 voxel, and 5.44 voxel, respectively. The experimental results show that the proposed method outperforms other semi-supervised learning methods and even achieves better performance than the fully supervised V-Net.

Determination of internal target volume with Cine-MRI sequence for prostate MRI-Guided radiotherapy.

In prostate radiotherapy, the intrafractional target motion negatively affects treatment accuracy. Generating internal target volume (ITV) using four-dimensional (4D) images may resolve the issue of intrafractional target motion induced by bladder filling and bowel movement. However, no 4D imaging techniques suitable for the prostate are currently available in clinical practice. This study aimed to determine the ITV based on cine magnetic resonance imaging (MRI) sequence for intrafractional target motion management in prostate MRI-guided radiotherapy. A reference ITV was generated in simulation process. Then, the reference ITV was adapted with cine MRI sequence before online planning in each fraction. Finally, the reference ITV was updated with the cine MRI sequence acquired during beam delivery after each fraction. Cine MRI sequences and positioning three-dimensional (3D) MRI from 35 patients were retrospectively collected. Clinical target volume (CTV) coverage was computed according to the two-dimensional contour of CTV and ITV on cine MRI images. Relative target size was calculated as the ratio of the volume of ITV and CTV. Isotropic planning target volume (PTV; 5mm margin) and anisotropic PTV (3mm margin in the posterior direction and 5mm margin in other directions) were generated for comparison. The CTV coverage rate of the proposed ITV had a mean value of 98.61%±0.51%, whereas the CTV coverage rates of the isotropic and anisotropic PTVs were 97.43%±0.41% and 96.58%±0.73%, respectively. The proposed ITV had a relative target size of 1.79±0.17, whereas the anisotropic and isotropic PTVs had relative target sizes of 1.92±0.12 and 2.21±0.19, respectively. For both the CTV coverage rate and target relative size, significant differences were observed between the proposed ITV and the other two PTVs (p<0.05). The ITV achieved higher CTV coverage with smaller size than conventional isotropic and anisotropic PTVs, indicating that it can effectively deal with the intrafractional movement of the prostate.

Feasibility of abdominal fat quantification on MRI and impact on effectiveness of abdominal compression for radiotherapy motion management

The impact of fat on abdominal compression effectiveness in abdominal cancers was determined using magnetic resonance imaging (MRI). Visceral and subcutaneous fat were delineated on T2W 3D MRI, and motion change with compression was measured on 2D cine MRI. Results from 16 participants showed no correlation between fat percentage, body mass index (BMI), and motion change. Median BMI was 28.7 (SD, 4.9). Mean motion reduction was 7.8 mm (IQR, 5.0; p = 0.001) with compression. While no direct link was found between fat, BMI, and compression effectiveness, abdominal compression remains crucial for motion management in radiotherapy planning, providing dosimetric benefits.

Regression of the Rician Noise Level in 3D Magnetic Resonance Images from the Distribution of the First Significant Digit

This paper investigates the distribution characteristics of Fourier, discrete cosine, and discrete sine transform coefficients in T1 MRI images. This paper reveals their adherence to Benford’s law, characterized by a logarithmic distribution of first digits. The impact of Rician noise on the first digit distribution is examined, which causes deviations from the ideal distribution. A novel methodology is proposed for noise level estimation, employing metrics such as the Bhattacharyya distance, Kullback–Leibler divergence, total variation distance, Hellinger distance, and Jensen–Shannon divergence. Supervised learning techniques utilize these metrics as regressors. Evaluations on MRI scans from several datasets coming from a wide range of different acquisition devices of 1.5 T and 3 T, comprising hundreds of patients, validate the adherence of noiseless T1 MRI frequency domain coefficients to Benford’s law. Through rigorous experimentation, our methodology has demonstrated competitiveness with established noise estimation techniques, even surpassing them in numerous conducted experiments. This research empirically supports the application of Benford’s law in transforms, offering a reliable approach for noise estimation in denoising algorithms and advancing image quality assessment.