Challenges in engagement with digital mental health (DMH) tools are commonly addressed through technical enhancements and algorithmic interventions. This paper shifts the focus towards the role of users' broader social context as a significant factor in engagement. Through an eight-week text messaging program aimed at enhancing psychological wellbeing, we recruited 20 participants to help us identify situational engagement disruptors (SEDs), including personal responsibilities, professional obligations, and unexpected health issues. In follow-up design workshops with 25 participants, we explored potential solutions that address such SEDs: prioritizing self-care through structured goal-setting, alternative framings for disengagement, and utilization of external resources. Our findings challenge conventional perspectives on engagement and offer actionable design implications for future DMH tools.

Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.

Over the past two decades, spectral computed tomography (CT) has undergone significant advancements, particularly in the realm of diagnostic accuracy, prompting a surge in clinical studies. This research examines the development of a new hybrid spectral CT system that combines a clinical-grade rapid kVp-switching X-ray tube with a dual-layer detector, aiming to boost quantitative spectral imaging performance in different clinical applications. The performance of the system was evaluated using varying tube voltages, duty cycles, and rotation times to enhance spectral outcomes. This experimental setup allowed for adjustments in parameters such as tube voltage pairs (140/80 and 120/70 kVp), duty cycles (ranging from 15/85 to 75/25), and tube currents (300 and 570 mA), which were tested on both large and small phantoms. The quantitative analysis was conducted using a two-input projection-based material decomposition approach. The study highlighted the impact of spectral weighting schemes on noise reduction and quantification bias in iodine density images. Results indicated that the maximum spectral separation scheme presented the least bias, suggesting its potential for improved clinical applications and outcomes. In conclusion, the research underscores the potential of integrating dual-energy technologies in hybrid spectral CT systems.

In this study, we introduce a conditional Denoising Diffusion Probabilistic Model (DDPM) approach that employs motion-corrupted images generated by FBP as the condition to reduce motion artifacts in 3D head CT scans. We address two critical questions in this application. First, how can we overcome the disparate performance observed in the skull and brain regions, which is attributable to their distinct intensity ranges? Second, which is more effective for accommodating the 3D nature of head CT and head motion: a 3D or 2D DDPM backbone? The resolution of these questions guides us towards an optimized, image-domain-only DDPM method, demonstrating significant efficacy in reducing motion artifacts in head CT scans.

This innovative research paper presents a program that introduces entrepreneurship, innovation, and biomedical product development to engineering, computer science, and other STEM and non-STEM professionals to engage them into startup creation with the goals of preventing, diagnosing or treating substance use disorder (SUD), one of the US most pressing health and social challenges. For more than two decades, SUD has been affecting people from all ages, demographic and socio-economic groups in the US. The National Institute on Drug Abuse (NIDA), as the lead federal agency supporting scientific research on drug use, has identified that current SUD research is not properly translating into commercial solutions for SUD. Seeking alternatives, NIDA is fostering the entrepreneurial spirit of SUD researchers, so they are the ones directly offering SUD-focused technologies into the market. This paper presents the MIT SUD Ventures program, a NIDA-funded project focused on training multidisciplinary teams of SUD researchers, engineers, healthcare, and management experts on how to commercialize their discoveries with the support of government funding sources. We introduce the learner profile, the content and skills deemed necessary to support their entrepreneurial efforts, the curriculum implemented and the program structure. We share results related to the learner expectations, experiences, and outcomes of this innovative practice, as well as future steps for the 2024 cohort and impact evaluation. Finally, guided by our results, we discuss the positive value of teaching entrepreneurial skills to academics and researchers to bridge the gaps between basic and translational research. We also make a call for action for all STEM professionals, especially engineers, computer scientists and technologists, to employ their scholarship and research capacity to have a positive impact on solving the SUD epidemic, where they are most needed.

The introduction of the first clinical photon-counting CT (PCCT) presents an opportunity to improve and expand quantitative imaging to new applications with its high spatial resolution and stellar quantitative capabilities. Despite this potential, PCCT employs a photon-counting detector that introduces unknowns including temporal stability that is critical to separating biological changes from scanner changes and variation in longitudinal studies. For the purpose of determining the temporal stability of a first-generation dual-source PCCT, a phantom was subjected to near-weekly scans across a two-year period, in both single-source and dual-source modes. Virtual monoenergetic images (VMI) at 40, 70, 100, and 190 keV and iodine density maps were analyzed to determine changes in relative error and noise both related and unrelated to software/hardware changes. VMIs demonstrated improvements in quantification for dual-source mode associated with software and hardware updates but otherwise illustrated invariance with variation ranging from 0.03 to 0.08%. VMI noise similarly exhibited stability between and with major scanner updates with a maximum change of 4 HU. Iodine density maps also displayed stability between scanner updates with variation up to 0.1 mg/mL but significant improvements in quantification, especially in dual-source mode, that allowed relative error in single-source and dual-source modes to match at -0.04 and -0.02 mg/mL, respectively. Spectral results in PCCT showed temporal stability over time that improved quantification accuracy particularly for dual-source mode. This stability will boost confidence in quantitative metrics such as in longitudinal studies and thus facilitate more clinical applications that may change the workflow of diagnostic radiology.

Spectral CT thermometry can non-invasively monitor internal temperatures to reduce local tumor recurrences caused by insufficient heating/treatment of the tumor and its surrounding safety margin. For its clinical translation, the applied metal artifact reduction algorithm requires quantitative accuracy to ensure the accuracy of generated temperature maps. The newly developed Spectrally Obtained Needle Artifact Reduction (SONAR) algorithm leverages the known shape of the applicator and spectral CT's material decomposition capabilities to isolate the applicator in projections. Projections with long path lengths through metal were then corrected by replacing them with modeled projections of an angled cylinder. To evaluate the accuracy of SONAR, a liver-mimicking phantom embedded with an ablation applicator and thermometers was scanned with a dual-layer spectral CT at phantom temperatures of 35 and 80 °C. Using spectral CT thermometry, temperature maps at 80 °C were generated for image slices with and without the applicator. SONAR significantly decreased streaks along the axis of the applicator. It also eliminated underestimated temperatures immediately adjacent to the applicator and overestimated temperatures in the periphery (2-3 cm from applicator). While application of SONAR resulted in minimal absolute difference in the temperature map without the applicator, averaging 1.1 ± 0.8 °C, temperatures decreased 7.0 ± 4.0 and 10.1 ± 2.3 °C at distances of 2-3 and 0.5-1 cm from the applicator, respectively, to better match the expected temperature. SONAR ultimately minimized metal artifacts and lessened overestimation of temperature in spectral CT thermometry maps. These quantitatively accurate maps will facilitate the in vivo evaluation of spectral CT thermometry for non-invasive temperature monitoring of thermal ablations in order to reduce local tumor recurrences.

The first clinical dual-source photon-counting CT couples high spatial resolution with spectral imaging that is advantageous to imaging of small vessels, such as the coronary arteries, in cardiovascular disease. While both the high spatial resolution and quantification accuracy have been established in PCCT, the effect of lumen size on spectral quantification has not been evaluated. Phantoms with an internal tube diameter ranging from 4 to 12 mm were printed with calcium-based polylactic acid filament to mimic a coronary artery. These diameter phantoms were filled with solutions with iodine concentrations of 2, 5, and 10 mg/mL and scanned with phantoms of varying sizes on a PCCT. Virtual monoenergetic images (VMI) at 70 keV, iodine density maps, and virtual non-contrast maps were measured to determine the effect of lumen diameter on spectral quantification at different iodine concentrations, radiation doses, and phantom sizes. Each evaluated spectral result exhibited consistent quantification at lumen diameters greater than 6 mm with all phantom sizes. VMI 70 keV were within ±15, ±12, and ±4 of VMI 70 keV at a lumen diameter of 12 mm and the small phantom for iodine concentrations of 2, 5, and 10 mg/mL. At a lumen diameter of 4 mm, significant deviations were present in VMI 70 keV, iodine density maps, and VNC with large phantoms, which averaged 55 HU, 1.4 mg/mL, and 61 HU at an iodine concentration of 5 mg/mL, respectively. The consistent spectral results across lumen diameters demonstrated the synergy between high spatial resolution and quantification that will spur the use of quantitative metrics and development of new applications in diagnostic cardiac imaging.

Despite the evident benefits of spectral computed tomography (CT) in delivering qualitative imaging superior to that of conventional CT in adults, its application in pediatric diagnostic imaging is still relatively limited due to various reasons, including design limitations and radiation dose considerations. The use of specialized K-edge filters, in conjunction with other spectral technologies, has been demonstrated to improve spectral quantification accuracy. X-ray flux limitations generally pose challenges in these concepts when applied to adults. However, such limitations are not present in pediatric imaging, allowing the full exploitation of K-edge filters to improve performance. To facilitate the adoption of spectral CT's benefits, as seen in the adult population, into pediatric settings, we introduce an innovative double bowtie filter design. This design incorporates a K-edge material coupled with Teflon and is integrated with rapid kVp-switching technology. A Python simulation was built to model a rapid kVp-switching x-ray tube and to estimate Cramer-Rao lower bound (CRLB) noise in photoelectric and Compton scatter basis domains. We estimate a conventional bowtie filter and corresponding reference patient dose before optimizing double bowtie configurations to contain the highest obtainable spectral signal-to-noise content for the specified phantom. Our findings indicate that an optimal combination of holmium and Teflon in the filter geometry can increase spectral SNR up to twofold the conventional estimates, while still maintaining low radiation dose exposure. This study broadens the scope for pediatric patients to fully benefit from the capabilities of spectral CT.

Respiratory motion phantoms can be used for evaluation of CT imaging technologies such as motion artifact reduction algorithms and deformable image registration. However, current respiratory motion phantoms do not exhibit detailed lung tissue structures and thus do not provide a realistic testing environment. This paper presents PixelPrint[4D], a method for 3D-printing deformable lung phantoms featuring highly realistic internal structures, suitable for a broad range of CT evaluations, optimizations, and research. The phantom in this study was designed with a patient 4DCT as a reference and 3D-printed using an extended version of the PixelPrint method for developing patient-specific CT phantoms. A flexible thermoplastic polyurethane (TPU) 3D-printing material was used, which produced regions with attenuation between -840 and -48 Hounsfield units (HU). A linear compression device was then designed and used to compress the phantom in the superior-inferior (SI) direction, and the phantom was scanned at different compression levels matched to the diaphragm displacements measured on the reference patient 4DCT. Deformable image registration (DIR) was performed, and motion vector fields were obtained for both patient and phantom images. SI displacements of selected features in the lung had mean errors of 0.5 mm difference from the patient, or less than the reconstructed slice thickness. In conclusion, the deformable lung phantom developed in this study exhibits realistic lung structures and deformation characteristics under compression, indicating potential for advancing more lifelike respiratory motion phantoms.

A new pneumonia detection method is proposed to provide both pneumonia detection in respiratory sound signals and wheeze and crackle discrimination when pneumonia episodes are detected. In the proposed method, two-step hierarchy, classifying pneumonia in the first step and discriminating wheezing and crackling in the second step, is considered; the conventional pneumonia detection method is modified to improve pneumonia detection performance, while wheezing and crackling discrimination functionality is added to facilitate the application of appropriate remedies for each case. We used resampling techniques to address the imbalance in the ICBHI pneumonia dataset. The random forest algorithm is used to classify pneumonia from healthy respiratory data, as well as to distinguish between wheeze and crackle from pneumonia data. Against the ICBHI respiratory dataset, the proposed random forest-based hierarchy pneumonia detection method provides 85.40% accuracy in detecting pneumonia and 82.70% accuracy in discriminating wheeze from crackling, respectively.

Cone-beam CT imaging using non-circular orbits has been demonstrated to be effective in reducing artifacts around metal. With the increasing interest in spectral imaging in the interventional suite, there are potential advantages to combine both technologies to yield further image quality benefits. We simulated a neuro-interventional application where imaging around the embolization is challenged by metal artifacts and the differentiation of bleeds and contrast extravasation is difficult with single-energy imaging. The imaging system was simulated with a dual-layer detector and different sinusoidal orbits. Material decomposition used a projection-domain approach followed by a model-based reconstruction of the density line integrals of each basis. The spectral non-circular orbits acquisitions were compared with single-energy circular, single-energy non-circular, and spectral circular orbits. Results using spectral non-circular orbit contain minimal metal artifacts and allow the differentiation of bleeds and contrast extravasation, demonstrating the potential of the combined technologies.

In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach combines sophisticated prior knowledge from unsupervised training with a rigorous physical model of the measurements. A faster and more stable variant is proposed that uses a "jumpstarted" process to reduce the number of time steps required in the reverse process and a gradient approximation to reduce the computational cost. Performance is investigated for two spectral CT systems: dual-kVp and dual-layer detector CT. On both systems, DPS achieves high Structure Similarity Index Metric Measure(SSIM) with only 10% of iterations as used in the model-based material decomposition(MBMD). Jumpstarted DPS (JSDPS) further reduces computational time by over 85% and achieves the highest accuracy, the lowest uncertainty, and the lowest computational costs compared to classic DPS and MBMD. The results demonstrate the potential of JSDPS for providing relatively fast and accurate material decomposition based on spectral CT data.

Accurate scatter correction is essential to obtain highquality reconstructions in computed tomography. While many correction strategies for this longstanding issue have been developed, additional efforts may be required for spectral CT imaging - which is particularly sensitive to unmodeled biases. In this work we explore a joint estimation approach within a one-step model-based material decomposition framework to simultaneously estimate material densities and scatter profiles in spectral CT. The method is applied to simulated phantom data obtained using a parametric additive scatter mode, and compared to the unmodeled scatter scenario. In these preliminary experiments, We find that this joint estimation approach has the potential to significantly reduce artifacts associated with unmodeled scatter and to improve material density estimates.

There has been a great deal of work seeking to improve image quality in CT reconstruction through deep-learning-based denoising; however, there are many applications where it is spatial resolution that limits application and diagnostics. In this work, we week to improve spatial resolution in CT reconstructions through a combination of deep learning and physical modeling of detector blur. To achieve this goal, we leverage diffusion models as deep image priors to help regularize a joint deblurring and reconstruction problem. Specifically, we adopt Diffusion Posterior Sampling (DPS) as a way to combine a deep prior with a likelihood-based forward model for the measurements. The model we adopt is nonlinear since detector blur is applied after the nonlinear attenuation given by the Beer-Lambert lab. We trained a score estimator for a CT score-based prior, and then apply Bayes rule to combine this prior with a measurement likelihood score for CT reconstruction with detector blur. We demonstrate the approach in simulated data, and compare image outputs with traditional filtered-backprojection (FBP) and model-based iterative reconstruction (MBIR) across a range of exposures. We find a particular advantage of the DPS approach for low exposure data and report on major differences in the errors between DPS and classical reconstruction methods.