In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient's CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-treatment volumetric imaging. This is especially beneficial for radiotherapy at motion-associated cancer sites and image-guided interventional procedures.

Accurate and automatic multi-needle detection in three-dimensional (3D) ultrasound (US) is a key step of treatment planning for US-guided brachytherapy. However, most current studies are concentrated on single-needle detection by only using a small number of images with a needle, regardless of the massive database of US images without needles. In this paper, we propose a workflow for multi-needle detection by considering the images without needles as auxiliary. Concretely, we train position-specific dictionaries on 3D overlapping patches of auxiliary images, where we develop an enhanced sparse dictionary learning method by integrating spatial continuity of 3D US, dubbed order-graph regularized dictionary learning. Using the learned dictionaries, target images are reconstructed to obtain residual pixels which are then clustered in every slice to yield centers. With the obtained centers, regions of interest (ROIs) are constructed via seeking cylinders. Finally, we detect needles by using the random sample consensus algorithm per ROI and then locate the tips by finding the sharp intensity drops along the detected axis for every needle. Extensive experiments were conducted on a phantom dataset and a prostate dataset of 70/21 patients without/with needles. Visualization and quantitative results show the effectiveness of our proposed workflow. Specifically, our method can correctly detect 95% of needles with a tip location error of 1.01 mm on the prostate dataset. This technique provides accurate multi-needle detection for US-guided HDR prostate brachytherapy, facilitating the clinical workflow.

In this paper, we consider the classical contention resolution problem in which an unknown subset of n possible nodes are activated and connected to a shared channel. The problem is solved in the first round that an active node transmits alone (thus breaking symmetry). Contention resolution has been an active research topic for over four decades. Accordingly, tight upper and lower bounds are known for most major model assumptions. There remains, however, an important case that is unresolved: contention resolution with multiple channels and collision detection. (Tight bounds are known for contention resolution with multiple channels, and contention resolution with collision detection, but not for the combination of both assumptions.)

A core challenge in wireless communication is choosing appropriate transmission rates for packets. This rate selection problem is well understood in the context of unicast communication from a sender to a known receiver that can reply with acknowledgments. The problem is more difficult, however, in the multicast scenario where a sender must communicate with a potentially large and changing group of receivers with varied link qualities. In such settings, it is inefficient to gather feedback, and achieving good performance for every receiver is complicated by the potential diversity of their link conditions. This paper tackles this problem from an algorithmic perspective: identifying near optimal strategies for selecting rates that guarantee every receiver achieves throughput within reasonable factors of the optimal capacity of its link to the sender. Our algorithms have the added benefit that they are blind: they assume the sender has no information about the network and receives no feedback on its transmissions. We then prove new lower bounds on the fundamental difficulty of achieving good performance in the presence of fast fading (rapid and frequent changes to link quality), and conclude by studying strategies for achieving good throughput over multiple hops. We argue that the implementation of our algorithms should be easy because of the feature of being blind (it is independent to the network structure and the quality of links, so it's robust to changes). Our theoretical framework yields many new open problems within this important general topic of distributed transmission rate selection.

Finding a maximal independent set (MIS) is a classic problem in graph theory that has been widely studied in the context of distributed algorithms. Standard distributed solutions to the MIS problem focus on time complexity. In this paper, we also consider fairness. For a given MIS algorithm A and graph G, we define the inequality factor for A on G to be the largest ratio between the probabilities of the nodes joining an MIS in the graph. We say an algorithm is fair with respect to a family of graphs if it achieves a constant inequality factor for all graphs in the family. In this paper, we seek efficient and fair algorithms for common graph families. We begin by describing an algorithm that is fair and runs in O(log* n)-time in rooted trees of size n. Moving to unrooted trees, we describe a fair algorithm that runs in O(log n) time. Generalizing further to bipartite graphs, we describe a third fair algorithm that requires O(log2 n) rounds. We also show a fair algorithm for planar graphs that runs in O(log2 n) rounds, and describe an algorithm that can be run in any graph, yielding good bounds on inequality in regions that can be efficiently colored with a small number of colors. We conclude our theoretical analysis with a lower bound that identifies a graph where all MIS algorithms achieve an inequality bound in Ω(n)-eliminating the possibility of an MIS algorithm that is fair in all graphs. Finally, to motivate the need for provable fairness guarantees, we simulate both our tree algorithm and Luby's MIS algorithm [13] in a variety of different tree topologies-some synthetic and some derived from real world data. Whereas our algorithm always yield an inequality factor ≤3.25 in these simulations, Luby's algorithms yields factors as large as 168.

Finding a maximal independent set (MIS) is a classic problem in graph theory that has been widely study in the context of distributed algorithms. Standard distributed MIS solutions focus on time complexity. Here we focus on a novel attribute, fairness, where we consider an MIS algorithm fair if all nodes have similar probabilities of joining the set. In many contexts, fairness is important because a node's election to the MIS can have an impact on the resources it consumes. This paper addresses fairness by providing a provably fair and efficient distributed MIS algorithm for unrooted trees. The algorithm runs in O(logn) time and guarantees a correct MIS such that each node enters the set with probability at least 1/4 - ε, for arbitrarily small ε.