Placental Flattening via Volumetric Parameterization

We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .Comment: MICCAI 201

Rule Out (R/O) Intracranial Aneurysm

When imaging patients for intracranial aneurysm, the goals are: (1) to assess the contour of the intracranial arteries, particularly in he regions of the ACOM (anterior communicating artery), PCOM (posterior communicating artery), ICA (internal carotid artery) bifurcation, MCA (middle cerebral artery) trifurcation, basilar tip, and PICA (posterior inferior cerebellar artery); (2) to assess the anatomy of the Circle of Willis and direction of flow, and; (3) to determine if there is evidence of a recent subarachnoid bleed. This unit describes a that can be used for standard imaging of aneurysm in stable patients. An is described for situations when there is concern for vasospasm and infarction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145398/1/cpmia0102.pd

Cerebral Infarct/Intracranial Cerebrovascular Disease

Imaging goals for intracranial cerebral vascular disease are (1) to assess the degree of parenchymal injury and identify intraparenchymal hemorrhage; (2) to determine if there are areas of altered perfusion that may be at risk for future injury; and (3) to assess the intracranial arteries (patency as well as direction of flow). This unit describes a that can be used to evaluate stable patients with acute, subacute, or chronic cerebrovascular symptoms. An is also given for cases of hyperacute strokes or cerebrovascular symptoms in an unstable patient.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145276/1/cpmia0101.pd

Accuracy of Segment-Anything Model (SAM) in medical image segmentation tasks

The segment-anything model (SAM), was introduced as a fundamental model for segmenting images. It was trained using over 1 billion masks from 11 million natural images. The model can perform zero-shot segmentation of images by using various prompts such as masks, boxes, and points. In this report, we explored (1) the accuracy of SAM on 12 public medical image segmentation datasets which cover various organs (brain, breast, chest, lung, skin, liver, bowel, pancreas, and prostate), image modalities (2D X-ray, histology, endoscropy, and 3D MRI and CT), and health conditions (normal, lesioned). (2) if the computer vision foundational segmentation model SAM can provide promising research directions for medical image segmentation. We found that SAM without re-training on medical images does not perform as accurately as U-Net or other deep learning models trained on medical images.Comment: Technical Repor

Transient and Persistent Pain Induced Connectivity Alterations in Pediatric Complex Regional Pain Syndrome

Evaluation of pain-induced changes in functional connectivity was performed in pediatric complex regional pain syndrome (CRPS) patients. High field functional magnetic resonance imaging was done in the symptomatic painful state and at follow up in the asymptomatic pain free/recovered state. Two types of connectivity alterations were defined: (1) Transient increases in functional connectivity that identified regions with increased cold-induced functional connectivity in the affected limb vs. unaffected limb in the CRPS state, but with normalized connectivity patterns in the recovered state; and (2) Persistent increases in functional connectivity that identified regions with increased cold-induced functional connectivity in the affected limb as compared to the unaffected limb that persisted also in the recovered state (recovered affected limb versus recovered unaffected limb). The data support the notion that even after symptomatic recovery, alterations in brain systems persist, particularly in amygdala and basal ganglia systems. Connectivity analysis may provide a measure of temporal normalization of different circuits/regions when evaluating therapeutic interventions for this condition. The results add emphasis to the importance of early recognition and management in improving outcome of pediatric CRPS

SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS

Purpose: To develop and evaluate a method for rapid estimation of multiparametric T1, T2, proton density (PD), and inversion efficiency (IE) maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. Methods: A SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an ISMRM/NIST phantom. The SSL-QALAS and the dictionary matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. Results: Phantom experiments showed that both the dictionary matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the ISMRM/NIST phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1, T2, PD, and IE maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.Comment: 18 figures, 4 table