Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior
Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV. Existing solution based on conditional generative models relies on the fidelity of synthetic truncation patterns at training phase, which poses limitations for the generalizability of the method to potential unknown types of truncation. In this study, we evaluate a zero-shot method based on a pretrained unconditional generative diffusion prior, where truncation pattern with arbitrary forms can be specified at inference phase. In evaluation on simulated chest CT slices with synthetic FOV truncation, the method is capable of recovering anatomically consistent body sections and subcutaneous adipose tissue measurement error caused by FOV truncation. However, the correction accuracy is inferior to the conditionally trained counterpart.
Code (0)
등록된 구현이 없습니다.
Tasks
Computed Tomography (CT)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Cross-View Completion Models are Zero-shot Correspondence Estimators
In this work, we explore new perspectives on cross-view completion learning by drawing an analogy to self-supervised correspondence learning. Through our analysis, we demonstrate that the cross-attention map within cross…
DecoderDepth EstimationGeometric MatchingConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation
Recent advances in zero-shot text-to-3D generation have revolutionized 3D content creation by enabling direct synthesis from textual descriptions. While state-of-the-art methods leverage 3D Gaussian Splatting with score …
3D GenerationText to 3DGenPC: Zero-shot Point Cloud Completion via 3D Generative Priors
Existing point cloud completion methods, which typically depend on predefined synthetic training datasets, encounter significant challenges when applied to out-of-distribution, real-world scans. To overcome this limitati…
3D GenerationImage to 3DPoint Cloud CompletionHierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer
Despite rapid progress in the voice style transfer (VST) field, recent zero-shot VST systems still lack the ability to transfer the voice style of a novel speaker. In this paper, we present HierVST, a hierarchical adapti…
Style TransferVariational InferenceMulti-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding r…
Matrix CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Task Learning+2