Real-Time Path Planning for Needle Insertion With Multiple Targets
Bibliographic record
Abstract
Needle insertion into soft tissue has gained considerable attention in recent years in medical applications due to its ever-increasing potential in minimally invasive procedures.Steerable bevel-tip needles offer higher maneuverability independent of the insertion depth and, consequently, are preferable in many needle steering applications compared to symmetric-tip needles.However, due to the nonholonomic kinematics of the bevel-tip needle inside soft tissue, its path planning poses a considerable challenge.Though the topic of single-target path planning is rather well studied and researched, the multiple-target path-planning problem remains under-researched.In this work, we study the path-planning problem for multiple targets based on Rapidly-Exploring-Random-Tree (RRT) algorithms.These algorithms are proper candidates for intra-operative planning of needle motion due to their fast computation and simple implementations.They also work well in highdimensional configuration spaces and under nonholonomic kinematic constraints, both of which are the characteristics of steerable bevel-tip needle motion inside soft tissue.We present two novel RRT-based path-planning approaches to steerable bevel-tip needles to reach multiple targets inside soft tissue: a 2D path planner for preoperative applications and a 3D real-time path planner for intraoperative applications.In both planners, without the needle having to completely retract and reinsert toward each separate target, the amount of tissue damage compared to the conventional sequential insertion of the needle toward each target decreases significantly.Particularly, our 3D planner works well in real-world applications where tissue and anatomical structures may vary due to tissue deformation during insertion, patient's motion, or physiological changes.In addition, our 3D planner accounts for the needle's natural curvature variation during insertion due to tissue inhomogeneity.Moreover, both of the proposed planners have real clinical applications, where iii
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".