Implementing a challenge-based learning experience in a bioinstrumentation blended course.

BACKGROUND: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic. METHODS: The CBL experience was implemented in a third-year bioinstrumentation course from the BME program at Tecnologico de Monterrey. Thirty-nine students enrolled in two sections formed fourteen teams that tackled blended learning activities, including online communication, lab experiments, and in-person CBL activities. Regarding the latter, students were challenged to design, prototype, and test a respiratory or cardiac gating device for radiotherapy. An institutional student opinion survey was used to assess the success of our CBL implementation. RESULTS: Student responses to the end-of-term survey showed that they strongly agreed that this course challenged them to learn new concepts and develop new skills. Furthermore, they rated the student-lecturer interaction very positively despite the blended format. Overall, students assessed their learning experience positively. However, implementing this CBL experience required a substantial time increase in planning, student tutoring, and constant communication between lecturers and the industry partner. CONCLUSION: This work provides an effective instance of CBL for BME education to improve students' learning experience despite decreased resource efficiency. Our claim is supported by the student's performance and the positive feedback from our industrial partner.

Transdisciplinary experiential learning in biomedical engineering education for healthcare systems improvement.

BACKGROUND: The growing demand for more efficient, timely, and safer health services, together with insufficient resources, put unprecedented pressure on health systems worldwide. This challenge has motivated the application of principles and tools of operations management and lean systems to healthcare processes to maximize value while reducing waste. Consequently, there is an increasing need for professionals with the appropriate clinical experience and skills in systems and process engineering. Given their multidisciplinary education and training, biomedical engineering professionals are likely among the most suitable to assume this role. In this context, biomedical engineering education must prepare students for a transdisciplinary professional role by including concepts, methods, and tools that commonly belong to industrial engineering. This work aims to create relevant learning experiences for biomedical engineering education to expand transdisciplinary knowledge and skills in students to improve and optimize hospital and healthcare care processes. METHODS: Healthcare processes were translated into specific learning experiences using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. This model allowed us to systematically identify the context where learning experiences were expected to occur, the new concepts and skills to be developed through these experiences, the stages of the student's learning journey, the resources required to implement the learning experiences, and the assessment and evaluation methods. The learning journey was structured around Kolb's experiential learning cycle, which considers four stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Data on the student's learning and experience were collected through formative and summative assessments and a student opinion survey. RESULTS: The proposed learning experiences were implemented in a 16-week elective course on hospital management for last-year biomedical engineering undergraduate students. Students engaged in analyzing and redesigning healthcare operations for improvement and optimization. Namely, students observed a relevant healthcare process, identified a problem, and defined an improvement and deployment plan. These activities were carried out using tools drawn from industrial engineering, which expanded their traditional professional role. The fieldwork occurred in two large hospitals and a university medical service in Mexico. A transdisciplinary teaching team designed and implemented these learning experiences. CONCLUSIONS: This teaching-learning experience benefited students and faculty concerning public participation, transdisciplinarity, and situated learning. However, the time devoted to the proposed learning experience represented a challenge.

Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.

Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average. Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.

Estradiol Replacement as a Potential Enhancer of Working Memory and Neuroplasticity in Hypogonadal Trans Women.

INTRODUCTION: The cognitive effects of cross-sex hormone therapy (CSHT) are not well understood. In cisgender individuals, sex hormone therapy can impact neurotransmitter levels and structural anatomy. Similarly, in gender-diverse persons, CSHT has been associated with neural adaptations, such as growth in brain structures resembling those observed in cisgender individuals of the same sex. Hormone-related changes in learning and memory, as seen in menopause, are associated with physiological hypogonadism or a decline in hormones, such as estradiol. The present study examined the effect of estradiol administration in humans on glutamate concentration in brain regions involved in semantic and working memory (i.e., the dorsolateral prefrontal cortex [DLPFC], the posterior hippocampus, and the pregenual anterior cingulate cortex) and its relationship with memory. METHODS: Eighteen trans women (male biological sex assigned at birth) ceased CSHT for 30 days for a washout phase (t1) upon study enrollment to reach a hypogonadal state. Working and semantic memory, cognition, hormonal assays, and brain imaging were assessed. Participants resumed CSHT for 60 days for a replacement phase (t2), after which the same evaluations from t1 were repeated. RESULTS: Estradiol increased among trans women after 60 days of resumed CSHT with significant improvements in semantic memory compared to the hypogonadal phase. Working memory recall was significantly and positively correlated to glutamate in the DLPFC during the reinstatement phase, although the relationship was not moderated by levels of estradiol. DISCUSSION: These results may have clinical implications for the therapeutic effects of estradiol replacement, serving as a protective factor against cognitive decline and impairment for trans women post-gonadectomy.

COVID-19 Volumetric Pulmonary Lesion Estimation on CT Images using a U-NET and Probabilistic Active Contour Segmentation.

A two-step method for obtaining a volumetric estimation of COVID-19 related lesion from CT images is proposed. The first step consists in applying a U-NET convolutional neural network to provide a segmentation of the lung-parenchyma. This architecture is trained and validated using the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) dataset, which is publicly available. The second step consists in obtaining the volumetric lesion estimation using an automatic algorithm based on a probabilistic active contour (PACO) region delimitation approach. Our pipeline successfully segmented COVID-19 related lesions in CT images, with exception of some mislabeled regions including lung airways and vasculature. Our workflow was applied to images in a cohort of 50 patients.

Evaluation of Echo Planar Imaging (EPI) Distortion Correction using Synb0-DisCo and Reversed Phase Encoding Acquisition.

Diffusion weighted imaging is a widely used imaging technique for the assessment of white matter using tractography. Nevertheless, due to practical constraints such as limited acquisition times, differences in scanning methods and physical artifacts, these images must be processed by image correction algorithms in order to produce reliable results. State-of-the art susceptibility correction algorithms such as FSL's TOPUP algorithm typically requires at least two images acquired with no diffusion encoding (b=0) in the regular and reverse phase encoding directions, commonly known as double-blip acquisitions, in order to calculate an undistorted volume. Since not all imaging protocols include a double-blip acquisition, they cannot take advantage of these state-of-the art distortion correction algorithms. A new approach based on a Synthetic b-0 Distortion Correction (Synb0-DisCo) has been tested with favourable results. Synb0-DisCo has proven to reduce variation in diffusion modeling creating a synthetic b-0 image to complement the single phase encoding b0 image. In this study, we aim to assess if there are any significant differences in Synb0-DisCo's efficacy resulting from different b-values. To observe critical metrics in the performance of susceptibility correction algorithms we use a 20 healthy subject database from project larynx to create four image sets containing: raw images, single phase encoding eddy correction, double phase encoding eddy correction and one single phase encoding plus a synthetic Synb0-DisCo image eddy correction. From this four image sets we then obtained the mean squared error (MSE) and mutual information (MI). We observed a diminished mean in the MSE, along a smaller dispersion, in the raw image set (Mean: 0.0306; C.I.[0.0369,0.024]) in comparison to the Synb0 image set (Mean: 0.0130; C.I.[0.0194, 0.0067]) We also observe a shift in the MSE depending on the b-value, where b-0 incurs the least MSE which does not occur in b-1000 and b-2000. This effect is lessened in the Synb0 image set. In absence of double phase encoding b-0 image, Synb0-DisCo proves to be a reliable algorithm to improve susceptibility distortion correction.

Comparison of compressed sensing reconstruction algorithms for 31P magnetic resonance spectroscopic imaging.

Phosphorus MR spectroscopy and spectroscopic imaging (31P-MRS/MRSI) provide information about energy metabolism, membrane degradation and pH in vivo. In spite of their proven utility, 31P-MRS/MRSI are not often used primarily because of the challenges imposed by the low sensitivity and low concentration of metabolites leading to low signal to noise ratio (SNR), coarse spatial resolution and prolonged acquisition time. More recently there has been considerable interest in compressed sensing as an acceleration method for MR signal acquisition. This approach takes advantage of the intrinsic sparsity of the spectral data. In this work, we present a 31P-MRSI sequence that combines a flyback EPSI trajectory and compressed sensing, and we compared two different reconstruction methods, L1 norm minimization and low rank Hankel matrix completion. Our phantom results showed good preservation of spectral quality for both ×2.0 and ×3.0 acceleration factors, using both CS reconstruction methods. However, in vivo 31P-MRS brain data showed the low rank reconstruction approach was most suitable. Overall, this study shows the feasibility of combining a flyback EPSI trajectory and compressed sensing in the acquisition of 31P-MRSI as well as the better suitability of a low rank reconstruction approach.

Dynamic 31 P spectroscopic imaging of skeletal muscles combining flyback echo-planar spectroscopic imaging and compressed sensing.

PURPOSE: Dynamic phosphorus MR spectroscopic imaging (31 P-MRSI) experiments require temporal resolution on the order of seconds to concurrently assess different muscle groups. A highly accelerated pulse sequence combining flyback echo-planar spectroscopic imaging (EPSI) and compressed sensing was developed and tested in a phantom and healthy humans during an exercise-recovery challenge of the lower leg muscles, using a clinical 3T MRI. METHODS: A flyback EPSI readout designed to achieve 2.25 × 2.25 cm2 resolution over a 18 × 18 cm2 field of view (i.e., 8 × 8 matrix) was combined with compressed sensing through the inclusion of pseudorandom gradient blips to sub-sample the ky-kt dimensions by a factor of 2.7×, achieving a temporal resolution of 9 s. The sequence was first tested in a phantom to assess performance compared to fully sampled EPSI (fidEPSI) and phase encoded chemical shift imaging (fidCSI). Then, tests were performed in 11 healthy volunteers during an exercise-recovery challenge of the lower leg muscles. Voxels containing tissue from different muscle groups were evaluated measuring percentage phosphocreatine (%PCr) depletion, time constant of PCr recovery (τPCr) and intracellular pH at rest and following exercise. RESULTS: The sequence was capable to track the dynamic PCr response of multiple muscles simultaneously. No statistical differences were found in the metabolite ratio, pH or linewidth when compared with fidEPSI and fidCSI in the phantom study. Dynamic experiments showed differences in PCr depletion when comparing soleus with gastrocnemius muscles. Intracellular pH, τPCr and %PCr decrease were consistent with reported values. CONCLUSION: Highly accelerated 31 P-MRSI combining flyback EPSI and compressed sensing is capable of assessing concurrent energy metabolism in multiple muscle groups using a clinical 3T MR system.

Phosphorus magnetic resonance spectroscopic imaging using flyback echo planar readout trajectories.

OBJECT: To present and evaluate a fast phosphorus magnetic resonance spectroscopic imaging (MRSI) sequence using echo planar spectroscopic imaging with flyback readout gradient trajectories. MATERIALS AND METHODS: Waveforms were designed and implemented using a 3 Tesla MRI system. 31P spectra were acquired with 2 × 2 cm2 and 3 × 3 cm2 resolution over a 20- and 21-cm field of view and spectral bandwidths up to 1923 Hz. The sequence was first tested using a 20-cm-diameter phosphate phantom, and subsequent in vivo tests were performed on healthy human calf muscles and brains from five volunteers. RESULTS: Flyback EPSI achieved 10× and 7× reductions in acquisition time, with 68.0 ± 1.2 and 69.8 ± 2.2% signal-to-noise ratio (SNR) per unit of time efficiency (theoretical SNR efficiency was 74.5 and 76.4%) for the in vivo experiments, compared to conventional phase-encoded MRSI for the 2 × 2 cm2 and 3 × 3 cm2 resolution waveforms, respectively. Statistical analysis showed no difference in the quantification of most metabolites. Time savings and SNR comparisons were consistent across phantom, leg and brain experiments. CONCLUSION: EPSI using flyback readout trajectories was found to be a reliable alternative for acquiring 31P-MRSI data in a shorter acquisition time.

Automated Classification of Severity in Cardiac Dyssynchrony Merging Clinical Data and Mechanical Descriptors.

Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%-30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers (28 ± 5 years, LVEF of 59.7% ± 5.8%) and a HF group of 42 subjects (53.12 ± 15.05 years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.

Opportunities and Challenges of 7 Tesla Magnetic Resonance Imaging: A Review.

The desire to achieve clinical ultra-high magnetic resonance imaging (MRI) systems stems from the fact that higher field strength leads to higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and spatial resolution. During last few years 7T MRI systems have become a quasi standard for ultra-high field MRI (UhFMRI) systems. This review presents a detailed account of opportunities and challenges associated with a clinical 7T MRI system for cranial and extracranial imaging. As with all of the previous transitions to higher field strengths, the switch from high to UhFMRI is not easy. The engineering and scientific community have to overcome challenges like magnetic field inhomogeneity, patient safety and comfort issues, and cost and related problems in order to achieve a clinically viable UhFMRI system. In addition, a large number of clinical studies are still required to show the improvements in quality of diagnostics that would come with 7T MRI, in order to bring such a research tool to the clinic.