3D Magnetic Resonance Imaging Research Articles

Automatic segmentation of rectal tumors from MRI using multiscale densely connected convolutional neural network based on attention mechanism

Rectal cancer is one of the most common malignancies in the gastrointestinal tract. Currently, magnetic resonance imaging has become a vital tool in diagnosing and treating patients with rectal cancer. Notably, early diagnosis of rectal cancer can help improve patient survival rate; however, the clinical expertize of physicians is a limiting factor. Therefore, we propose an attention-based multiscale densely connected convolutional neural network based on an attention mechanism to improve the accuracy of diagnosis by automatically segmenting rectal tumors from two-dimensional (2D) magnetic resonance images (MRI) using computer-aided diagnostic techniques. First, to address the inability of U-Net (a classical segmentation network for medical images) and extract rich semantic features and the inconsistent shape and size of tumors between different patients, we replace the conventional convolutional blocks in the U-Net network with multiscale densely connected convolutional blocks. Second, to make the network focus better on global contextual information, we add central blocks with atrous convolution in the final coding layer or the last coding layer. Finally, we add a hybrid attention mechanism to each decoder module to help the model focus on the features of the rectal tumor region. We validated the effectiveness of the proposed method using 3773 2D MRI datasets from 572 patients. The sensitivity, specificity, Dice correlation coefficient, and Hausdorff distance of MRI rectal tumor segmentation were 85.47%, 86.35%, 94.71%, and 7.88 mm, respectively. The results showed that the proposed method outperforms conventional approaches. Moreover, the proposed method has better segmentation results in the rectal tumor segmentation task and can provide physicians with the second-most important clinical diagnostic opinion.

Fast Compressed Sensing of 3D Radial T1 Mapping with Different Sparse and Low-Rank Models.

Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T1 relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T1 maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.

Three-dimensional ultrasound to investigate synovitis in first carpometacarpal osteoarthritis: A feasibility study.

Synovitis is one of the defining characteristics of osteoarthritis (OA) in the carpometacarpal (CMC1) joint of the thumb. Quantitative characterization of synovial volume is important for furthering our understanding of CMC1 OA disease progression, treatment response, and monitoring strategies. In previous studies, three-dimensional ultrasound (3-D US) has demonstrated the feasibility of being a point-of-care system for monitoring knee OA. However, 3-D US has not been tested on the smaller joints of the hand, which presents unique physiological and imaging challenges. To develop and validate a novel application of 3-D US to monitor soft-tissue characteristics of OA in a CMC1 OA patient population compared to the current gold standard, magnetic resonance imaging (MRI). A motorized submerged transducer moving assembly was designed for this device specifically for imaging the joints of the hands and wrist. The device used a linear 3-D scanning approach, where a 14L5 2-D transducer was translated over the region of interest. Two imaging phantoms were used to test the linear and volumetric measurement accuracy of the 3-D US device. To evaluate the accuracy of the reconstructed 3-D US geometry, a multilayer monofilament string-grid phantom (10mm square grid) was scanned. To validate the volumetric measurement capabilities of the system, a simulated synovial tissue phantom with an embedded synovial effusion was fabricated and imaged. Ten CMC1 OA patients were imaged by our 3-D US and a 3.0 T MRI system to compare synovial volumes. The synovial volumes were manually segmented by two raters on the 2D slices of the 3D US reconstruction and MR images, to assess the accuracy and precision of the device for determining synovial tissue volumes. The Standard Error of Measurement and Minimal Detectable Change was used to assess the precision and sensitivity of the volume measurements. Paired sample t-tests were used to assess statistical significance. Additionally, rater reliability was assessed using Intra-Class Correlation (ICC) coefficients. The largest percent difference observed between the known physical volume of synovial extrusion in the phantom and the volume measured by our 3D US was 1.1% (p-value=0.03). The mean volume difference between the 3-D US and the gold standard MRI was 1.78% (p-value=0.48). The 3-D US synovial tissue volume measurements had a Standard Error Measurement (SEm ) of 11.21mm3 and a Minimal Detectible Change (MDC) of 31.06mm3 , while the MRI synovial tissue volume measurements had an SEM of 16.82mm3 and an MDC of 46.63mm3 . Excellent inter- and intra-rater reliability (ICCs=0.94-0.99) observed across all imaging modalities and raters. Our results indicate the feasibility of applying 3-D US technology to provide accurate and precise CMC1 synovial tissue volume measurements, similar to MRI volume measurements. Lower MDC and SEm values for 3-D US volume measurements indicate that it is a precise measurement tool to assess synovial volume and that it is sensitive to variation between volume segmentations. The application of this imaging technique to monitor OA pathogenesis and treatment response over time at the patient's bedside should be thoroughly investigated in future studies.

MRI Derived Simulations of Flow Patterns in the Stomach.

A framework to simulate the flow in the stomach using subject-specific motility patterns and geometries was developed. Dynamic 2D magnetic resonance images (MRIs) were obtained. Motility parameters such as contraction speed and occlusion were quantified, and 3D stomach geometries were reconstructed using a semi-automated approach. Computational fluid dynamics (CFD) simulations were performed, and flow patterns were investigated. The stomach of both subjects had distinct anatomical features with computed volumes of 789 mL and 619 mL. For the one subject, the occlusion (i.e., normalized contraction size) was 12% while it was around 25% for the other subject. Contraction speeds were also different (1.9-2.8 mm/s vs 3.0-5.1 mm/s) for each subject. CFD simulations resulted in unsteady laminar flow for both subjects with average velocities of 2.1 and 3.2 mm/s. While antegrade flow was mainly observed in the simulations, a retropulsive jet was also present in both stomachs. The versatile framework developed within this study would allow the generation of CFD models of gastric motility from dynamic MRIs.Clinical Relevance- Subject-specific models of flow patterns informed by gastric motility features can elucidate the impact of contractions and anatomical variations on digestion. Such models can inform therapies to treat gastric dysfunctions and improve their efficacy.

Quantifying Muscle Size Asymmetry in Adolescent Idiopathic Scoliosis Using Three-dimensional Magnetic Resonance Imaging.

This is a case-control study of prospectively collected data. To quantify paraspinal muscle size asymmetry in adolescent idiopathic scoliosis (AIS) and determine if this asymmetry is (i) greater than observed in adolescent controls with symmetrical spines; and (ii) positively associated with skeletal maturity using Risser grade, scoliosis severity using the Cobb angle, and chronological age in years. AIS is a three-dimensional deformity of the spine which occurs in 2.5% to 3.7% of the Australian population. There is some evidence of asymmetry in paraspinal muscle activation and morphology in AIS. Asymmetric paraspinal muscle forces may facilitate asymmetric vertebral growth during adolescence. An asymmetry index [Ln(concave/convex volume)] of deep and superficial paraspinal muscle volumes, at the level of the major curve apex (Thoracic 8-9 th vertebral level) and lower-end vertebrae ( LEV , Thoracic 10-12 th vertebral level), was determined from three-dimensional Magnetic Resonance Imaging of 25 adolescents with AIS (all right thoracic curves), and 22 healthy controls (convex=left); all female, 10 to 16 years. Asymmetry index of deep paraspinal muscle volumes was greater in AIS (0.16±0.20) than healthy spine controls (-0.06±0.13) at the level of the apex ( P <0.01, linear mixed-effects analysis) but not LEV ( P >0.05). Asymmetry index was positively correlated with Risser grade ( r =0.50, P <0.05) and scoliosis Cobb angle ( r =0.45, P <0.05), but not age ( r =0.34, P >0.05). There was no difference in the asymmetry index of superficial paraspinal muscle volumes between AIS and controls ( P >0.05). The asymmetry of deep apical paraspinal muscle volume in AIS at the scoliosis apex is greater than that observed at equivalent vertebral levels in controls and may play a role in the pathogenesis of AIS.

Automated Brain Tumour Detection and Classification using Deep Features and Bayesian Optimised Classifiers.

Brain tumour detection and classification require trained radiologists for efficient diagnosis. The proposed work aims to build a Computer Aided Diagnosis (CAD) tool to automate brain tumour detection using Machine Learning (ML) and Deep Learning (DL) techniques. Magnetic Resonance Image (MRI) collected from the publicly available Kaggle dataset is used for brain tumour detection and classification. Deep features extracted from the global pooling layer of Pretrained Resnet18 network are classified using 3 different ML Classifiers, such as Support vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT). The above classifiers are further hyperparameter optimised using Bayesian Algorithm (BA) to enhance the performance. Fusion of features extracted from shallow and deep layers of the pretrained Resnet18 network followed by BA-optimised ML classifiers is further used to enhance the detection and classification performance. The confusion matrix derived from the classifier model is used to evaluate the system's performance. Evaluation metrics, such as accuracy, sensitivity, specificity, precision, F1 score, Balance Classification Rate (BCR), Mathews Correlation Coefficient (MCC) and Kappa Coefficient (Kp), are calculated. Maximum accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC, and Kp of 99.11 %, 98.99 %, 99.22 %, 99.09 %, 99.09 %, 99.10 %, 98.21 %, 98.21 %, respectively, were obtained for detection using fusion of shallow and deep features of Resnet18 pretrained network classified by BA optimized SVM classifier. Feature fusion performs better for classification task with accuracy, sensitivity, specificity, precision, F1 score, BCR, MCC and Kp of 97.31 %, 97.30 %, 98.65 %, 97.37 %, 97.34 %, 97.97%, 95.99 %, 93.95 %, respectively. The proposed brain tumour detection and classification framework using deep feature extraction from Resnet 18 pretrained network in conjunction with feature fusion and optimised ML classifiers can improve the system performance. Henceforth, the proposed work can be used as an assistive tool to aid the radiologist in automated brain tumour analysis and treatment.

A New Medical Analytical Framework for Automated Detection of MRI Brain Tumor Using Evolutionary Quantum Inspired Level Set Technique.

Segmenting brain tumors in 3D magnetic resonance imaging (3D-MRI) accurately is critical for easing the diagnostic and treatment processes. In the field of energy functional theory-based methods for image segmentation and analysis, level set methods have emerged as a potent computational approach that has greatly aided in the advancement of the geometric active contour model. An important factor in reducing segmentation error and the number of required iterations when using the level set technique is the choice of the initial contour points, both of which are important when dealing with the wide range of sizes, shapes, and structures that brain tumors may take. To define the velocity function, conventional methods simply use the image gradient, edge strength, and region intensity. This article suggests a clustering method influenced by the Quantum Inspired Dragonfly Algorithm (QDA), a metaheuristic optimizer inspired by the swarming behaviors of dragonflies, to accurately extract initial contour points. The proposed model employs a quantum-inspired computing paradigm to stabilize the trade-off between exploitation and exploration, thereby compensating for any shortcomings of the conventional DA-based clustering method, such as slow convergence or falling into a local optimum. To begin, the quantum rotation gate concept can be used to relocate a colony of agents to a location where they can better achieve the optimum value. The main technique is then given a robust local search capacity by adopting a mutation procedure to enhance the swarm's mutation and realize its variety. After a preliminary phase in which the cranium is disembodied from the brain, tumor contours (edges) are determined with the help of QDA. An initial contour for the MRI series will be derived from these extracted edges. The final step is to use a level set segmentation technique to isolate the tumor area across all volume segments. When applied to 3D-MRI images from the BraTS' 2019 dataset, the proposed technique outperformed state-of-the-art approaches to brain tumor segmentation, as shown by the obtained results.

Initial Observation of Contrast Profiles for 2-Dimensional and 3-Dimensional Magnetic Resonance Imaging Sequences in Magnetic Resonance–Guided Radiation Therapy for Locally Advanced Pancreatic Cancer

In our experience treating locally advanced pancreatic cancer with magnetic resonance-guided radiation therapy (MRgRT), the true-fast imaging with steady-state free precession sequences used to generate both the real-time 2-dimensional (2D) magnetic resonance images (MRI; 2D cine) and the pretreatment high-resolution 3-dimensional (3D) MRI impart differing intensities for relevant structures between the 2 scans. Since these variations can confound target tracking selection, we propose that an understanding of the differing contrast profiles could improve selection of tracking structures. We retrospectively reviewed both 2D cine and 3D MRI images for 20 patients with pancreatic cancer treated with MRgRT. At simulation, an appropriate tracking target was identified and contoured on a single 3-mm sagittal slice of the 3D MRI. This sagittal slice was directly compared with the coregistered 7-mm 2D cine to identify structures with notable discrepancies in signal intensity. The 3D MRI was then explored in additional planes to confirm structure identities. For quantitative verification of the clinically observed differences, the pixel intensity distributions of 2D cine and 3D MRI digital imaging and communications in medicine data sets were statistically compared. In all patients reviewed, arteries (aorta, celiac, superior mesenteric artery, hepatic artery) appeared mildly hyperintense on both scans. However, veins (portal vein, superior mesenteric vein) appeared hyperintense on 2D cine but isointense on 3D MRI. Biliary structures appeared mildly hyperintense on 2D cine but starkly hyperintense on 3D MRI. The pixel intensity distributions extracted from 2D cine and 3D MRI images were confirmed to differ significantly (2 sample Kolmogorov-Smirnov test; test statistic, 0.40; P < .001). There are significant variations in image intensity between the immediate pretreatment 2D cine compared with the initial planning 3D MRI. Understanding variations of image intensity between the different MRI sequences used in MRgRT is valuable to radiation oncologists and may lead to improved target tracking and optimized treatment delivery.

Brain Parcellation Repeatability and Reproducibility Using Conventional and Quantitative 3D MR Imaging.

Automatic brain parcellation is typically performed on dedicated MR imaging sequences, which require valuable examination time. In this study, a 3D MR imaging quantification sequence to retrieve R1 and R2 relaxation rates and proton density maps was used to synthesize a T1-weighted image stack for brain volume measurement, thereby combining image data for multiple purposes. The repeatability and reproducibility of using the conventional and synthetic input data were evaluated. Twelve subjects with a mean age of 54 years were scanned twice at 1.5T and 3T with 3D-QALAS and a conventionally acquired T1-weighted sequence. Using SyMRI, we converted the R1, R2, and proton density maps into synthetic T1-weighted images. Both the conventional T1-weighted and the synthetic 3D-T1-weighted inversion recovery images were processed for brain parcellation by NeuroQuant. Bland-Altman statistics were used to correlate the volumes of 12 brain structures. The coefficient of variation was used to evaluate the repeatability. A high correlation with medians of 0.97 for 1.5T and 0.92 for 3T was found. A high repeatability was shown with a median coefficient of variation of 1.2% for both T1-weighted and synthetic 3D-T1-weighted inversion recovery at 1.5T, and 1.5% for T1-weighted imaging and 4.4% for synthetic 3D-T1-weighted inversion recovery at 3T. However, significant biases were observed between the methods and field strengths. It is possible to perform MR imaging quantification of R1, R2, and proton density maps to synthesize a 3D-T1-weighted image stack, which can be used for automatic brain parcellation. Synthetic parameter settings should be reinvestigated to reduce the observed bias.

Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT. Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans. Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for target D 95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on target D 95% up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs. Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

Paper 48: Association of 3D MRI Volumetric Assessment of Rotator Cuff Pathology with Preoperative Patient Reported Outcomes

Paper 48: Association of 3D MRI Volumetric Assessment of Rotator Cuff Pathology with Preoperative Patient Reported Outcomes

Semi-supervised hybrid spine network for segmentation of spine MR images.

Automatic segmentation of vertebral bodies (VBs) and intervertebral discs (IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating spinal diseases. However, segmenting the VBs and IVDs simultaneously is not trivial. Moreover, problems exist, including blurry segmentation caused by anisotropy resolution, high computational cost, inter-class similarity and intra-class variability, and data imbalances. We proposed a two-stage algorithm, named semi-supervised hybrid spine network (SSHSNet), to address these problems by achieving accurate simultaneous VB and IVD segmentation. In the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross pseudo supervision to obtain intra-slice features and coarse segmentation. In the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This model can be used to extract inter-slice information and combine the coarse segmentation and intra-slice features provided from the first stage. Moreover, a cross tri-attention module was applied to compensate for the loss of inter-slice and intra-slice information separately generated from 2D and 3D networks, thereby improving feature representation ability and achieving satisfactory segmentation results. The proposed SSHSNet was validated on a publicly available spine MR image dataset, and remarkable segmentation performance was achieved. Moreover, results show that the proposed method has great potential in dealing with the data imbalance problem. Based on previous reports, few studies have incorporated a semi-supervised learning strategy with a cross attention mechanism for spine segmentation. Therefore, the proposed method may provide a useful tool for spine segmentation and aid clinically in spinal disease diagnoses and treatments. Codes are publicly available at: https://github.com/Meiyan88/SSHSNet.

Dynamic 3D morphology of chick embryos and allantois depicted nondestructively by 3.0T clinical magnetic resonance imaging

Driven by a global trend of applying replace-reduce-refine or 3Rs' guidance for experimental animals in life sciences, chick embryo and particularly allantois with its chorioallantoic membrane have been increasingly utilized to substitute laboratory animals, which call for more extensive and updated knowledge about this novel experimental setup. In this study, being noninvasive, nonionizing, and super-contrasting with high spatiotemporal resolutions, magnetic resonance imaging (MRI) was chosen as an imaging modality for in ovo monitoring morphologic evolution of the chick embryo, allantois, and chorioallantoic membrane longitudinally throughout embryonic day (ED) 1 until ED20. Cooled in 0°C ice bath for 60 min to reduce MRI motion artifacts, 3 chick embryos (n=60 in total) on each ED were scanned by a clinical 3.0T MRI scanner to demonstrate 3D images of both T2- and T1-weighted imaging (T2WI, T1WI) sequences at axial, sagittal, and coronal slices. The volumes of both the entire chick embryo and allantois were semi-automatically segmented based on intensity-based thresholding and region-growing algorithms. The morphometries or quantified 3D structures were achieved by refined segmentation, and confirmed by histological analyses (one for each ED). After MRI, the rest of chick embryos (n=40) continued for incubation. The images from ED2 to ED4 could demonstrate the structural changes of latebra, suggesting its transition into a nutrient supplying channel of yolk sac. The allantois could be recognized by MRI, and its relative volumes on each ED revealed an evolving profile peaked on ED12, with a statistically significant difference from those of earlier and later EDs (P < 0.01). The hypointensity of the yolk due to the susceptibility effect of its enriched iron content overshadowed the otherwise hyperintensity of its lipid components. The chick embryos survived prior cooling and MRI till hatching on ED21. The results could be further developed into a 3D MRI atlas of chick embryo. Clinical 3.0T MRI proved effective as a noninvasive approach to study in ovo 3D embryonic development across the full period (ED1-ED20), which can complement the present knowhow for poultry industry and biomedical science.

Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy.

Physiological motion impacts the dose delivered to tumours and vital organs in external beam radiotherapy and particularly in particle therapy. The excellent soft-tissue demarcation of 4D magnetic resonance imaging (4D-MRI) could inform on intra-fractional motion, but long image reconstruction times hinder its use in online treatment adaptation. Here we employ techniques from high-performance computing to reduce 4D-MRI reconstruction times below two minutes to facilitate their use in MR-guided radiotherapy. Four patients with pancreatic adenocarcinoma were scanned with a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac. Fast parallelised open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm were developed for central processing unit (CPU) and graphics processing unit (GPU) architectures. We assessed the impact of architecture, oversampling and respiratory binning strategy on 4D-MRI reconstruction time and compared images using the structural similarity (SSIM) index against a MATLAB reference implementation. Scaling and bottlenecks for the different architectures were studied using multi-GPU systems. All reconstructed 4D-MRI were identical to the reference implementation (SSIM 0.99). Images reconstructed with overlapping respiratory bins were sharper at the cost of longer reconstruction times. The CPU +GPU implementation was over 17 times faster than the reference implementation, reconstructing images in 60 1 s and hyper-scaled using multiple GPUs. Respiratory-resolved 4D-MRI reconstruction times can be reduced using high-performance computing methods for online workflows in MR-guided radiotherapy with potential applications in particle therapy.

White Matter Metabolite Ratios Predict Cognitive Outcome in Pediatric Traumatic Brain Injury.

The prognostic ability of global white matter and gray matter metabolite ratios following pediatric traumatic brain injury (TBI) and their relationship to 12-month neuropsychological assessments of intelligence quotient (IQ), attention, and memory is presented. Three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) in pediatric subjects with complicated mild (cMild), moderate, and severe TBI was acquired acutely (6-18 days) and 12 months post-injury and compared to age-matched typically developing adolescents. A global linear regression model, co-registering MRSI metabolite maps with 3D high-resolution magnetic resonance images, was used to identify longitudinal white matter and gray matter metabolite ratio changes. Acutely, gray matter NAA/Cr, white matter NAA/Cr, and white matter NAA/Cho ratios were significantly lower in TBI groups compared to controls. Gray matter NAA/Cho was reduced only in the severe TBI group. At 12 months, all metabolite ratios normalized to control levels in each of the TBI groups. Acute gray matter and white matter NAA ratios were significantly correlated to 12-month assessments of IQ, attention, and memory. These findings suggest that whole brain gray matter and white matter metabolite ratios reflect longitudinal changes in neuronal metabolism following TBI, which can be used to predict neuropsychological outcomes in pediatric subjects.

Assessment of Three Breast Volume Measurement Techniques: Single Marking, MRI and Crisalix 3D Software®.

Measuring breast volume is important to obtain satisfactory breast surgery results, and many techniques are used for this purpose. Thus, the aim of the present study was to compare 3 breast volume techniques: Pessoa's single marking technique, magnetic resonance imaging (MRI) and Crisalix 3D software®. Fourteen patients indicated for mammoplasty were selected. Three breast volume measurement techniques were compared: Pessoa's single marking technique, MRI and Crisalix 3D software®. The volumes were tabulated and analyzed using R software. Average age was 30.93 ± 10.25 years. The breast volume was 1554.54 ± 512.54 cm3, as measured by the MRI technique (considered the gold standard), 1199.64 ± 403.13 cm3 using Crisalix 3D software® and 1518.04 ± 468.72 cm3 by Pessoa's single marking technique. Comparison between the Crisalix 3D software® and MRI techniques using the pairwise t test demonstrated a statistically significant difference (t = 4.3957, df = 27, p value = 0001543), but no significant difference between the single marking and MRI techniques (t = 1.3841, df = 27, p value = 0.1777). When compared to MRI, breast volume measurement using Pessoa's single marking technique showed no statistically significant difference between them. However, the Crisalix 3D® technique exhibited a difference in relation to MRI. Anthropometric measurements are useful in measuring breast volume because they are easy to obtain, practical and inexpensive, and should be part of a plastic surgeon's arsenal. This journal requires that authors assign a level of evidence to each article. For a full description of these evidence-based medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Single-Setting 3D MRI/US-Guided Frozen Sectioning and Cryoablation of the Index Lesion: Mid-Term Oncologic and Functional Outcomes from a Pilot Study.

Our study explored frozen section reliability in prostate cancer (PCa) diagnoses and described surgical steps of a 3D magnetic resonance imaging (MRI)-ultrasound (US)-guided prostate biopsy (PB) and focal cryoablation of the index lesion (IL) in a single-setting procedure. Patients with a suspicious prostatic specific antigen (PSA) value, with a PIRADS 4 or 5 single lesion, were enrolled for trans perineal 3D MRI-US-guided PB and TRUS-guided focal cryoablation. Three cores were taken from the IL, three cores from the surrounding area, while systematic sampling was performed for the rest of the gland. After confirmation of PCa in frozen sections, focal cryoablation was performed. The 1st-year follow-up schedule included a PSA test at a 3-month interval, MRI 3 months and 1 year postoperatively and PB of the treated area at 1 year. Following the follow-up schedule, an involved PSA test at a 3-month interval and yearly MRI were performed. The PCa diagnosis was histologically confirmed in all three patients with frozen sections. At final histology, a single Gleason score upgrade from 6 (3 + 3) to 7 (3 + 4) was observed. All patients were discharged on postoperative day 1. At the 3-month evaluation, mean PSA values decreased from 12.54 (baseline) to 1.73 ng/mL and MRI images showed complete ablation of the IL in all patients. Urinary continence and potency were preserved in all patients. At the 1-year follow-up, one patient had suspicious ipsilateral recurrence on MRI and underwent a new analogous procedure. Post follow-up was uneventful and PSA remained stable in all patients. Three-dimensional MRI-US-guided frozen sectioning and focal cryoablation of the IL is a step forward towards a "patient-tailored" minimally invasive approach to the diagnosis and cure of PCa.

Ultra-high field magnetic resonance imaging of the quadriceps tendon enthesis in healthy subjects.

Although enthesitis is a hallmark of several rheumatologic conditions, current imaging methods are still unable to characterize entheses changes because of the corresponding short transverse relaxation times (T2). A growing number of MR studies have used Ultra-High Field (UHF) MRI in order to assess low-T2 tissues e.g., tendon but never in humans. The purpose of the present study was to assess in vivo the enthesis of the quadriceps tendon in healthy subjects using UHF MRI. Eleven healthy subjects volunteered in an osteoarthritis imaging study. The inclusion criteria were: no knee trauma, Lequesne index = 0, less than 3h of sport activities per week, and Kellgren and Lawrence grade = 0. 3D MR images were acquired at 7T using GRE sequences and a T2* mapping. Regions of interest i.e., trabecular bone, subchondral bone, enthesis, and tendon body were identified, and T2* values were quantified and compared. Quadriceps tendon enthesis was visible as a hyper-intense signal. The largest and the lowest T2* values were quantified in the subchondral bone region and the tendon body respectively. T2* value within subchondral bone was significantly higher than T2* value within the enthesis. T2* in subchondral bone region was significantly higher than the whole tendon body T2*. A T2* gradient was observed along the axis from the enthesis toward the tendon body. It illustrates different water biophysical properties. These results provide normative values which could be used in the field of inflammatory rheumatologic diseases and mechanical disorders affecting the tendon.

Turbulence in surgical suction heads as detected by MRI.

Blood loss is common during surgical procedures, especially in open cardiac surgery. Allogenic blood transfusion is associated with increased morbidity and mortality. Blood conservation programs in cardiac surgery recommend re-transfusion of shed blood directly or after processing, as this decreases transfusion rates of allogenic blood. But aspiration of blood from the wound area is often associated with increased hemolysis, due to flow induced forces, mainly through development of turbulence. We evaluated magnetic resonance imaging (MRI) as a qualitative tool for detection of turbulence. MRI is sensitive to flow; this study uses velocity-compensated T1-weighted 3D MRI for turbulence detection in four geometrically different cardiotomy suction heads under comparable flow conditions (0-1250mL/min). Our standard control suction head Model A showed pronounced signs of turbulence at all flow rates measured, while turbulence was only detectable in our modified Models 1-3 at higher flow rates (Models 1 and 3) or not at all (Model 2). The comparison of flow performance of surgical suction heads with different geometries via acceleration-sensitized 3D MRI revealed significant differences in turbulence development between our standard control Model A and the modified alternatives (Models 1-3). As flow conditions during measurement have been comparable, the specific geometry of the respective suction heads must have been the main factor responsible. The underlying mechanisms and causative factors can only be speculated about, but as other investigations have shown, hemolytic activity is positively associated with degree of turbulence. The turbulence data measured in this study correlate with data from other investigations about hemolysis induced by surgical suction heads. The experimental MRI technique used showed added value for further elucidating the underlying physical phenomena causing blood damage due to non-physiological flow.

Comparing bone shape models from deep learning processing of magnetic resonance imaging to computed tomography-based models

The purpose of this study was to develop a deep learning approach to automatically segment the scapular bone on magnetic resonance imaging (MRI) images and to compare the accuracy of these three-dimensional (3D) models with that of 3D computed tomography (CT). Fifty-five patients with high-resolution 3D fat-saturated T2 MRI were retrospectively identified. The underlying pathology included rotator cuff tendinopathy and tears, shoulder instability, and impingement. Two experienced musculoskeletal researchers manually segmented the scapular bone. Five cross-validation training and validation splits were generated to independently train two-dimensional (2D) and 3D models using a convolutional neural network approach. Model performance was evaluated using the Dice similarity coefficient (DSC). All models with DSC > 0.70 were ensembled and used for the test set, which consisted of four patients with matching high-resolution MRI and CT scans. Clinically relevant glenoid measurements, including glenoid height, width, and retroversion, were calculated for two of the patients. Paired t-tests and Wilcoxon signed-rank tests were used to compare the DSC of the models. The 2D and 3D models achieved a best DSC of 0.86 and 0.82, respectively, with no significant difference observed. Augmentation of imaging data significantly improved 3D but not 2D model performance. In comparing clinical measurements of 3D MRI and CT, there was a mean difference ranging from 1.29 mm to 3.46 mm and 0.05° to 7.47°. We have presented a fully automatic, deep learning-based strategy for extracting scapular shape from a high-resolution MRI scan. Further developments of this technology have the potential to allow for surgeons to obtain all clinically relevant information from MRI scans and reduce the need for multiple imaging studies for patients with shoulder pathology.