Surface Point Cloud Ultrasound with Transcranial Doppler: Coregistration of Surface Point Cloud Ultrasound with Magnetic Resonance Angiography for Improved Reproducibility, Visualization, and Navigation in Transcranial Doppler Ultrasound

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

Purpose

The aim of this study was to investigate the value of a deep learning model (DLM) based on breast tumor ultrasound image segmentation in predicting pathological response to neoadjuvant chemotherapy (NAC) in breast cancer.

Methods

The dataset contains a total of 1393 ultrasound images of 913 patients from Renmin Hospital of Wuhan University, of which 956 ultrasound images of 856 patients were used as the training set, and 437 ultrasound images of 57 patients underwent NAC were used as the test set. A U-Net-based end-to-end DLM was developed for automatically tumor segmentation and area calculation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional ultrasound measurement methods (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using changes in tumor size ratios to develop receiver operating characteristic curves.

Results

The average intersection over union value of the DLM was 0.856. The early-stage ultrasound-predicted area under curve (AUC) values of pCR were not significantly different from those of the intermediate and late stages (p< 0.05). The AUCs for MSM, DLM, LAM and DAM were 0.840, 0.756, 0.778 and 0.796, respectively. There was no significant difference in AUC values of the predictive ability of the four models.

Conclusion

Ultrasonography was predictive of pCR in the early stages of NAC. DLM have a similar predictive value to conventional ultrasound for pCR, with an add benefit in effectively improving workflow.

Application of Ultrasound Combined with Magnetic Resonance Imaging in the Diagnosis and Grading of Patients with Prenatal Placenta Accreta

In order to study the clinical application value of placenta accreta (PIA) diagnosis and grading, the authors propose a method based on ultrasound combined with magnetic resonance imaging in the diagnosis and grading of prenatal placenta accreta patients. This method is adopted in materials and methods: a retrospective analysis of hospital patients with high suspicion of placenta accreta by clinical or ultrasonography between October 2019 and October 2021, the imaging and clinical data of 312 patients who underwent placental MRI examination. The MRI imaging data of all patients were jointly analyzed, and the main observation indicators are as follows: (1) dark zone in the placenta, (2) disruption of the border of the myometrium, (3) disruption of the myometrium, (4) abnormal blood vessels in the placenta, (5) enlargement of the lower part of the uterus, and (6) local bulge of the bladder/or invasion of the adjacent tissues of the uterus. The results show the following: in MRI combined with ultrasonography (P < 0.05), there was no statistical significance in the specificity and accuracy of MRI combined with ultrasound to diagnose PIA (P > 0.05). The comparison of graded diagnostic accuracy showed that in ultrasound alone < MRI alone < MRI combined with ultrasound, the differences were statistically significant (P < 0.05). Ultrasound combined with MRI in the diagnosis of placenta accreta is in good agreement with the clinical and surgical pathological results; MRI examination can be used as an important method for prenatal placenta accreta screening. MRI can classify placenta accreta to some extent.

Evaluation of cerebrovascular hemodynamics in vascular dementia patients with a new individual computational fluid dynamics algorithm

BACKGROUND

Cerebral hemodynamic disorders are involved in the occurrence and progression of vascular dementia (VaD), but the methods to detect hemodynamics remainmultifarious and uncertain nowadays. We aim to exploit a computational fluid dynamics (CFD) approach by static and dynamic parameters, which can be used to detect individual cerebrovascular hemodynamics quantitatively.

METHODS

A patient-specific CFD model was constructed with geometrical arteries on the magnetic resonance angiography (MRA) and hemodynamic parameters on ultrasound Doppler, by which, the structural and simulated hemodynamic indexes could be obtained, mainly including the cerebral arterial volume (CAV), the number of visible arterial outlets, the total cerebral blood flow (tCBF) index and the total cerebrovascular resistance (tCVR) index. The hemodynamics were detected in subcortical vascular dementia (SVaD) patients (n = 38) and cognitive normal controls (CNCs; n = 40).

RESULTS

Compared with CNCs, the SVaD patients had reduced outlets, CAV and tCBF index (all P ≤ 0.001), increased volume of white matter hyperintensity (WMH) and tCVR index (both P ≤ 0.01). The fewer outlets (OR = 0.77), higher Hachinski ischemic score (HIS) (OR = 3.65), increased tCVR index (OR = 1.98) and volume of WMH (OR = 1.12) were independently associated with SVaD. All hemodynamic parameters could differentiate the SVaD patinets and CNCs, especially the composite index calculated by outlets, tCVR index and HIS (AUC = 0.943). Fewer outlets and more WMH increased the odds of SVaD, which were partly mediated by the tCBF index (14.4% and 13.0%, respectively).

CONCLUSION

The reduced outlets, higher HIS and tCVR index may be independent risk factors for the SVaD, and a combination of these indexes can differentiate SVaD patients and CNCs reliably. The tCBF index potentially mediates the relationships between hemodynamic indexes and SVaD. Although all simulated indexes only represented the true hemodynamics indirectly, this CFD model can provide patient-specific hemodynamic alterations non-invasively and conveniently.