OBJECTIVE:To better understand the long-term patient-reported outcomes (PROs) in satisfaction and health-related quality of life (QOL) following post-mastectomy reconstruction (PMR) using the ...BREAST-Q, comparing PROs from patients undergoing implant-based breast reconstruction (IBR) or autologous breast reconstruction (ABR).
SUMMARY OF BACKGROUND DATA:Multiple studies have demonstrated growth in mastectomy rates and concurrent increase in PMR utilization. However, most studies examining PMR PROs focus on short postoperative time periods—mainly within 2 years.
METHODS:BREAST-Q scores from IBR or ABR patients at a tertiary center were prospectively collected from 2009 to 2017. Mean scores and standard deviations (SDs) were calculated for satisfaction with breast, satisfaction with outcome, psychosocial well-being, physical well-being of the chest, and sexual well-being. Satisfaction with breasts and physical well-being of the chest were compared using regression models at postoperative years 1, 3, 5, and 7.
RESULTS:Overall, 3268 patients were included, with 336 undergoing ABR and 2932 undergoing IBR. Regression analysis demonstrated that ABR patients had greater postoperative satisfaction with breast scores at all timepoints compared with IBR patients. Postoperative radiation and mental illness adversely impacted satisfaction with breast scores. Furthermore, mental illness impacted physical wellbeing of the chest at all timepoints. IBR patients had satisfaction scores that remained stable over the study period.
CONCLUSION:This study presents the largest prospective examination of PROs in PMR to date. Patients who opted for ABR had significantly higher satisfaction with their breast and QOL at each assessed time point, but IBR patients had stable long-term satisfaction and QOL postoperatively.
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data ...can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.
After WWII, U.S. leaders sought to create liberal rule-of-law regimes in Germany and Japan, but the effort was often unsuccessful. Kostal argues that the manifest failings of America’s own ...rule-of-law democracy were partially to blame, weakening U.S. credibility and resolve and revealing the country’s ambiguous status as a global moral authority.
In real-time dynamic reconstruction, geometry and motion are the major focuses while appearance is not fully explored, leading to the low-quality appearance of the reconstructed surfaces. In this ...article, we propose a lightweight lighting model that considers spatially varying lighting conditions caused by self-occlusion. This model estimates per-vertex masks on top of a single Spherical Harmonic (SH) lighting to represent spatially varying lighting conditions without adding too much computation cost. The mask is estimated based on the local geometry of a vertex to model the self-occlusion effect, which is the major reason leading to the spatial variation of lighting. Furthermore, to use this model in dynamic reconstruction, we also improve the motion estimation quality by adding a real-time per-vertex displacement estimation step. Experiments demonstrate that both the reconstructed appearance and the motion are largely improved compared with the current state-of-the-art techniques.
Purpose:
To investigate a measurement method for evaluating the resolution properties of CT imaging systems across reconstruction algorithms, dose, and contrast.
Methods:
An algorithm was developed ...to extract the task-based modulation transfer function (MTF) from disk images generated from the rod inserts in the ACR phantom (model 464 Gammex, WI). These inserts are conventionally employed for HU accuracy assessment. The edge of the disk objects was analyzed to determine the edge-spread function, which was differentiated to yield the line-spread function and Fourier-transformed to generate the object-specific MTF for task-based assessment, denoted MTFTask. The proposed MTF measurement method was validated against the conventional wire technique and further applied to measure the MTF of CT images reconstructed with an adaptive statistical iterative algorithm (ASIR) and a model-based iterative (MBIR) algorithm. Results were further compared to the standard filtered back projection (FBP) algorithm. Measurements were performed and compared across different doses and contrast levels to ascertain the MTFTask dependencies on those factors.
Results:
For the FBP reconstructed images, the MTFTask measured with the inserts were the same as the MTF measured from the wire-based method. For the ASIR and MBIR data, the MTFTask using the high contrast insert was similar to the wire-based MTF and equal or superior to that of FBP. However, results for the MTFTask measured using the low-contrast inserts, the MTFTask for ASIR and MBIR data was lower than for the FBP, which was constant throughout all measurements. Similarly, as a function of mA, the MTFTask for ASIR and MBIR varied as a function of noise–-with MTFTask being proportional to mA. Overall greater variability of MTFTask across dose and contrast was observed for MBIR than for ASIR.
Conclusions:
This approach provides a method for assessing the task-based MTF of a CT system using conventional and iterative reconstructions. Results demonstrated that the object-specific MTF can vary as a function of dose and contrast. The analysis highlighted the paradigm shift for iterative reconstructions when compared to FBP, where iterative reconstructions generally offer superior noise performance but with varying resolution as a function of dose and contrast. The MTFTask generated by this method is expected to provide a more comprehensive assessment of image resolution across different reconstruction algorithms and imaging tasks.
Hyperspectral anomaly detection is an important task in the remote sensing domain. Recently, researchers have shown great interest in deep learning-based methods because they can learn hierarchical, ...abstract, and high-level representations. However, the latent features learned from the autoencoder (AE) are not always able to reflect the intrinsic structure of hyperspectral data because the locality property is not considered during the learning process. In order to address this problem, a novel manifold constrained AE network (MC-AEN)-based hyperspectral anomaly detection method is proposed in this article. First, the manifold learning method is employed to learn the embedding manifold. Then, the latent representations are learned by an AE network with the learned embedding manifold constraints to preserve the intrinsic structure of hyperspectral data. Finally, the reconstruction errors are calculated to detect anomalies. The global reconstruction error from MC-AEN and the local reconstruction error from the learned latent representations are combined to fully utilize the learned knowledge for better detection performance. We test our proposed algorithm on three different real data sets. Experimental results on these three data sets show the superiority of our proposed method.
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we ...propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion .
Since 9/11, why have we won smashing battlefield victories only to botch nearly everything that comes next? In the opening phases of war in Afghanistan, Iraq, and Libya, we mopped the floor with our ...enemies. But in short order, things went horribly wrong.
We soon discovered we had no coherent plan to manage the "day after." The ensuing debacles had truly staggering consequences-many thousands of lives lost, trillions of dollars squandered, and the apparent discrediting of our foreign policy establishment. This helped set the stage for an extraordinary historical moment in which America's role in the world, along with our commitment to democracy at home and abroad, have become subject to growing doubt. With the benefit of hindsight, can we discern what went wrong? Why have we had such great difficulty planning for the aftermath of war?
In The Day After, Brendan Gallagher-an Army lieutenant colonel with multiple combat tours to Iraq and Afghanistan, and a Princeton Ph.D.-seeks to tackle this vital question. Gallagher argues there is a tension between our desire to create a new democracy and our competing desire to pull out as soon as possible. Our leaders often strive to accomplish both to keep everyone happy. But by avoiding the tough underlying decisions, it fosters an incoherent strategy. This makes chaos more likely.
The Day After draws on new interviews with dozens of civilian and military officials, ranging from US cabinet secretaries to four-star generals. It also sheds light on how, in Kosovo, we lowered our postwar aims to quietly achieve a surprising partial success. Striking at the heart of what went wrong in our recent wars, and what we should do about it, Gallagher asks whether we will learn from our mistakes, or provoke even more disasters? Human lives, money, elections, and America's place in the world may hinge on the answer.
No one likes nation-building. The public dismisses it. Politicians criticize it. The traditional military disdains it, and civilian agencies lack the blueprint necessary to make it work. Yet ...functioning states play a foundational role in international security and stability. Left unattended, ungoverned spaces can produce crises from migration to economic collapse to terrorism.                Keith W. Mines has taken part in nation-building efforts as a Special Forces officer, diplomat, occupation administrator, and United Nations official. In Why Nation-Building Matters  he uses cases from his own career to argue that repairing failed states is a high-yield investment in our own nation’s global future. Eyewitness accounts of eight projects––in Colombia, Grenada, El Salvador, Somalia, Haiti, Darfur, Afghanistan, and Iraq—inform Mines’s in-depth analysis of how foreign interventions succeed and fail. Building on that analysis, he establishes a framework for nation-building in the core areas of building security forces, economic development, and political consolidation that blend soft and hard power into an effective package.                Grounded in real-world experience, Why Nation-Building Matters is an informed and essential guide to meeting one of the foremost challenges of our foreign policy present and future.  
The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ...ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit depth. In this paper, we investigate an alternative CS approach that shifts the emphasis from the sampling rate to the number of bits per measurement. In particular, we explore the extreme case of 1-bit CS measurements, which capture just their sign. Our results come in two flavors. First, we consider ideal reconstruction from noiseless 1-bit measurements and provide a lower bound on the best achievable reconstruction error. We also demonstrate that i.i.d. random Gaussian matrices provide measurement mappings that, with overwhelming probability, achieve nearly optimal error decay. Next, we consider reconstruction robustness to measurement errors and noise and introduce the binary ε-stable embedding property, which characterizes the robustness of the measurement process to sign changes. We show that the same class of matrices that provide almost optimal noiseless performance also enable such a robust mapping. On the practical side, we introduce the binary iterative hard thresholding algorithm for signal reconstruction from 1-bit measurements that offers state-of-the-art performance.
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