Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers.

Medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend.

Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries.

In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as \textit{half-scan} data, as only half of a complete spherical measurement aperture is employed. Although previous studies have demonstrated that half-scan data can uniquely and stably reconstruct the sought-after object, no closed-form reconstruction formula for use with half-scan data has been reported.

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics.

Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

Purpose: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.

Methods: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis.

Adjuvant therapy and recurrence risk in non-myoinvasive high-grade (stage IC) endometrial cancer: A systematic review and meta-analysis.

Objective: To summarize practice patterns and outcomes among patients with non-myoinvasive high-grade (formerly stage IA, now stage IC) endometrial cancer.

Methods: We conducted a systematic search using MEDLINE, Embase, Cochrane, Web of Science, and ClinicalTrials.gov databases from inception to May 8, 2024 to identify studies reporting on treatment and outcomes of non-myoinvasive high-grade endometrial cancer.

Unusual Maternal and Fetal Findings With Cell-Free DNA Screening.

Importance: With advances in prenatal cell-free DNA (cfDNA) technology, the information available with cfDNA continues to expand beyond the common fetal aneuploidies such as trisomies 21, 18, and 13. Due to the admixture of maternal and fetal/placental DNA, prenatal cfDNA remains a screening test with the possibility of false-positive and false-negative results.

Objective: This review aims to summarize unusual incidental maternal and fetal genomic abnormalities detectable by cfDNA and to provide anticipatory guidance regarding management.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives.

Ortho-positronium lifetime for soft-tissue classification.

The objective of this work is to showcase the ortho-positronium lifetime as a probe for soft-tissue characterization. We employed positron annihilation lifetime spectroscopy to experimentally measure the three components of the positron annihilation lifetime-para-positronium (p-Ps), positron, and ortho-positronium (o-Ps)-for three types of porcine, non-fixated soft tissues ex vivo: adipose, hepatic, and muscle. Then, we benchmarked our measurements with X-ray phase-contrast imaging, which is the current state-of-the-art for soft-tissue analysis.

Full-Wave Image Reconstruction in Transcranial Photoacoustic Computed Tomography Using a Finite Element Method.

Transcranial photoacoustic computed tomography presents challenges in human brain imaging due to skull-induced acoustic aberration. Existing full-wave image reconstruction methods rely on a unified elastic wave equa- tion for skull shear and longitudinal wave propagation, therefore demanding substantial computational resources. We propose an efficient discrete imaging model based on finite element discretization.

Investigating the use of signal detection information in supervised learning-based image denoising with consideration of task-shift.

Purpose: Recently, learning-based denoising methods that incorporate task-relevant information into the training procedure have been developed to enhance the utility of the denoised images. However, this line of research is relatively new and underdeveloped, and some fundamental issues remain unexplored. Our purpose is to yield insights into general issues related to these task-informed methods.

Geographic and racial disparities in the quality of surgical care among patients with nonmetastatic uterine cancer.

Background: Although the rates of minimally invasive surgery and sentinel lymph node biopsy have increased considerably over time in the surgical management of early-stage uterine cancer, practice varies significantly in the United States, and there are disparities among low-volume centers and patients of Black race. A significant number of counties in the United States are without a gynecologic oncologist, and almost half of the counties with the highest gynecologic cancer rates lack a local gynecologic oncologist.

Objective: This study aimed to evaluate the relationships of distance traveled and proximity to gynecologic oncologists with the receipt of and racial disparities in the quality of surgical care among patients who underwent a hysterectomy for nonmetastatic uterine cancer.

The doc versus the bot: A pilot study to assess the quality and accuracy of physician and chatbot responses to clinical questions in gynecologic oncology.

Artificial intelligence (AI) applications to medical care are currently under investigation. We aimed to evaluate and compare the quality and accuracy of physician and chatbot responses to common clinical questions in gynecologic oncology. In this cross-sectional pilot study, ten questions about the knowledge and management of gynecologic cancers were selected.

Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting.

Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part II.

Significance: Quantitative phase imaging (QPI) is a non-invasive, label-free technique that provides intrinsic information about the sample under study. Such information includes the structure, function, and dynamics of the sample. QPI overcomes the limitations of conventional fluorescence microscopy in terms of phototoxicity to the sample and photobleaching of the fluorophore.

Scalp hypothermia to reduce chemotherapy-induced alopecia: A systematic review and meta-analysis.

Background: Chemotherapy-induced alopecia (CIA) is a common and emotionally-taxing side effect of chemotherapy, including taxane agents used frequently in treatment of gynecologic cancers. Scalp hypothermia, also known as "cold caps", is a possible method to prevent severe CIA, studied primarily in the breast cancer population.

Objectives: To compile existing data on scalp hypothermia in cancer patients receiving taxane chemotherapy in order to investigate its application to the gynecologic cancer population.

Assessing the Capacity of a Denoising Diffusion Probabilistic Model to Reproduce Spatial Context.

Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models-denoising diffusion probabilistic models (DDPMs)-demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To date, these claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity.

Introducing the Special Issue Honoring Lihong V. Wang, Pioneer in Biomedical Optics.

The editorial concludes the JBO Special Issue Honoring Lihong V. Wang, outlining Prof. Wang's salient contributions to advancing the field of biomedical optics.

Controversies in the Surgical Management of Gynecologic Cancer: Balancing the Decision to Operate or Hesitate.

Cancer outcomes are largely measured in terms of disease-free survival or overall survival, which is highly dependent on timely diagnosis and access to treatment methods available within the country's existing health care system. Although cancer survival rates have markedly led in the past few decades, any improvement in the 5-year survival of gynecologic cancers has been modest, as in the case of ovarian and cervical cancers, or has declined, as in the case of endometrial cancer. The lack of effective screening options contributes to many women presenting with advanced-stage disease and the need for radical approaches to treatment.

Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics.

Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

Purpose: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.

Methods: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis.

Aesthetic perception of patient in developmental age in interceptive orthodontic treatment.

Aim: Patients at development age show considerable attention to the shape of the face from both an aesthetic and relational point of view, to arouse interest from researchers. There are few studies related to profile analysis in patients of developmental age. Therefore, the objective of the present study was to analyse the importance of the aesthetic perception of the patient in development age in relation to the profile, before and after interceptive orthodontic treatment.