Scalp surface estimation and head registration using sparse sampling and 3D statistical models.

Registering the head and estimating the scalp surface are important for various biomedical procedures, including those using neuronavigation to localize brain stimulation or recording. However, neuronavigation systems rely on manually-identified fiducial head targets and often require a patient-specific MRI for accurate registration, limiting adoption. We propose a practical technique capable of inferring the scalp shape and use it to accurately register the subject's head. Our method does not require anatomical landmark annotation or an individual MRI scan, yet achieves accurate registration of the subject's head and estimation of its surface. The scalp shape is estimated from surface samples easily acquired using existing pointer tools, and registration exploits statistical head model priors. Our method allows for the acquisition of non-trivial shapes from a limited number of data points while leveraging their object class priors, surpassing the accuracy of common reconstruction and registration methods using the same tools. The proposed approach is evaluated in a virtual study with head MRI data from 1152 subjects, achieving an average reconstruction root-mean-square error of 2.95 mm, which outperforms a common neuronavigation technique by 2.70 mm. We also characterize the error under different conditions and provide guidelines for efficient sampling. Furthermore, we demonstrate and validate the proposed method on data from 50 subjects collected with conventional neuronavigation tools and setup, obtaining an average root-mean-square error of 2.89 mm; adding landmark-based registration improves this error to 2.63 mm. The simulation and experimental results support the proposed method's effectiveness with or without landmark annotation, highlighting its broad applicability.

Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.

The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10-20 System's neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.

Estimation and Tracking of Blood Pressure Using Routinely Acquired Photoplethysmographic Signals and Deep Neural Networks.

Continuous tracking of blood pressure in critically ill patients allows rapid identification of clinically important changes and helps guide treatment. Classically, such tracking requires invasive monitoring with its associated risks, discomfort, and low availability outside critical care units. We hypothesized that information contained in a prevalent noninvasively acquired signal (photoplethysmograph: a byproduct of pulse oximetry) combined with advanced machine learning will allow continuous estimation of the patient's blood pressure. DESIGN: Retrospective cohort study with split sampling for model training and testing. SETTING: A single urban academic hospital. PATIENTS: Three-hundred twenty-nine adult patients admitted to a critical care unit. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One hundred thirty-six thousand four-hundred fifty-nine photoplethysmography waveforms of length 30 seconds were used for training (60%), validation (20%), and testing (20%) of the blood pressure estimation network. Each sample had an associated systolic, mean, and diastolic blood pressures extracted from concurrently recorded invasive arterial line waveforms. Blood pressure estimation using photoplethysmography waveforms is achieved using advanced machine learning methods (convolutional neural networks and a Siamese architectural configuration) calibrated for each patient on a single, first available photoplethysmography sample and associated blood pressure reading. The average estimation bias error was 0.52, 0.1, and -0.76 mm Hg for diastolic, mean, and systolic blood pressure, respectively, with associated mean absolute errors of 4.11, 5.51, and 7.98 mm Hg. If used to identify clinically important changes in blood pressure from the initial baseline, with a threshold of a 10 mm Hg increase or decrease in blood pressure, our algorithm shows an accuracy of 85%, 78%, and 74% for diastolic, mean, and systolic blood pressure, respectively. We also report the network's performance in detecting systolic and diastolic hypo- or hypertension with accuracies ranging from 86% to 97%. CONCLUSIONS: Using advanced machine learning tools, we show that blood pressure estimation can be achieved using a common noninvasively recorded signal, the photoplethysmography. Such tools can allow for better monitoring of patients that do not have invasively recorded blood pressure, both in the critical care setting and on inpatient wards.