Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the ...potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.
Purpose
Electromagnetic (EM) tracking is a key technology in image‐guided therapy. A new EM Micro Sensor was presented by Polhemus Inc.; it is the first to enable localization of medical instruments ...through their trackers. Different field generators (FGs) are available by Polhemus, one being almost as small as a sugar cube. As accuracy and robustness of tracking are known challenges to using EM trackers in clinical environments, the goal of this study was a standardized assessment of the Micro Sensor in both a laboratory (lab) and a computed tomography (CT) environment.
Methods
The Micro Sensor was assessed by means of Hummel et al.’s standardized protocol; it was assessed in conjunction with a Polhemus Liberty tracker and three FGs — with edge lengths of 1 (TX1), 2 (TX2), and 4 (TX4) inches. Precision as well as positional and rotational accuracy were determined in a lab and a CT suite. Distortions by four different metallic cylinders and tracking of two typical medical instruments — a hypodermic needle and a flexible endoscope — were also tested.
Results
A jitter of 0.02 mm or less was found for all FGs in the different environments, except for the TX2 FG for which no valid data could be obtained in the CT. Errors of 5 cm distance measurements were 0.6 mm or less for all FGs in the lab. While the distance errors of the TX1 FG were only slightly increased up to 1.6 mm in the CT, those of the TX4 FG were found to be up to around 10% of the measured distance (5.4 mm on average). The mean orientation error was found to be 0.9°/0.5°/0.1° for the TX4/TX2/TX1 FG in the lab. In the CT environment, rotation errors were in the same range: less than 1.2°/0.1° for the TX4/TX1 FG. Deviation under the presence of metallic cylinders stayed below 1 mm in most cases. Precision and orientational accuracy do not seem to be affected by instrument tracking and stayed in the same range as for the other measurements whereas distance errors were slightly increased up to 1.7 mm.
Conclusion
This study shows that accurate tracking of medical instruments is possible with the new Micro Sensor; it demonstrated a jitter of 0.01 mm or less, position errors below 2 mm, and rotation errors of less than 0.3°. As with other EM trackers, errors increase when large tracking volumes with ranges of up to 50 cm are required in clinical environments. For smaller tracking volumes with ranges of up to 15 cm, a high accuracy and robustness was found. This is interesting especially for the TX1 FG which can easily be placed in close vicinity to the region of interest.
Object tracking is a key enabling technology in the context of computer-assisted medical interventions. Allowing the continuous localization of medical instruments and patient anatomy, it is a ...prerequisite for providing instrument guidance to subsurface anatomical structures. The only widely used technique that enables real-time tracking of small objects without line-of-sight restrictions is electromagnetic (EM) tracking. While EM tracking has been the subject of many research efforts, clinical applications have been slow to emerge. The aim of this review paper is therefore to provide insight into the future potential and limitations of EM tracking for medical use. We describe the basic working principles of EM tracking systems, list the main sources of error, and summarize the published studies on tracking accuracy, precision and robustness along with the corresponding validation protocols proposed. State-of-the-art approaches to error compensation are also reviewed in depth. Finally, an overview of the clinical applications addressed with EM tracking is given. Throughout the paper, we report not only on scientific progress, but also provide a review on commercial systems. Given the continuous debate on the applicability of EM tracking in medicine, this paper provides a timely overview of the state-of-the-art in the field.
Physics‐based shape matching for intraoperative image guidance Suwelack, Stefan; Röhl, Sebastian; Bodenstedt, Sebastian ...
Medical physics (Lancaster),
November 2014, 2014-Nov, 2014-11-00, 20141101, 2014-11-01, Letnik:
41, Številka:
11
Journal Article
Recenzirano
Purpose:
Soft‐tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, ...time‐of‐flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model. The shape matching is especially challenging in the intraoperative setting due to noisy sensor data, only partially visible surfaces, ambiguous shape descriptors, and real‐time requirements.
Methods:
A novel physics‐based shape matching (PBSM) approach to register intraoperatively acquired surface meshes to preoperative planning data is proposed. The key idea of the method is to describe the nonrigid registration process as an electrostatic–elastic problem, where an elastic body (preoperative model) that is electrically charged slides into an oppositely charged rigid shape (intraoperative surface). It is shown that the corresponding energy functional can be efficiently solved using the finite element (FE) method. It is also demonstrated how PBSM can be combined with rigid registration schemes for robust nonrigid registration of arbitrarily aligned surfaces. Furthermore, it is shown how the approach can be combined with landmark based methods and outline its application to image guidance in laparoscopic interventions.
Results:
A profound analysis of the PBSM scheme based on in silico and phantom data is presented. Simulation studies on several liver models show that the approach is robust to the initial rigid registration and to parameter variations. The studies also reveal that the method achieves submillimeter registration accuracy (mean error between 0.32 and 0.46 mm). An unoptimized, single core implementation of the approach achieves near real‐time performance (2 TPS, 7–19 s total registration time). It outperforms established methods in terms of speed and accuracy. Furthermore, it is shown that the method is able to accurately match partial surfaces. Finally, a phantom experiment demonstrates how the method can be combined with stereo endoscopic imaging to provide nonrigid registration during laparoscopic interventions.
Conclusions:
The PBSM approach for surface matching is fast, robust, and accurate. As the technique is based on a preoperative volumetric FE model, it naturally recovers the position of volumetric structures (e.g., tumors and vessels). It cannot only be used to recover soft‐tissue deformations from intraoperative surface models but can also be combined with landmark data from volumetric imaging. In addition to applications in laparoscopic surgery, the method might prove useful in other areas that require soft‐tissue registration from sparse intraoperative sensor data (e.g., radiation therapy).
Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been ...proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The ...workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.
Key points
The workflow of radiomics is complex with several methodological steps and nuances, which often leads to inadequate reproducibility, reporting, and evaluation.
The CLEAR checklist proposes a single documentation standard for radiomics research that can guide authors, providing the minimum requirements for presenting clinical radiomics research.
The CLEAR checklist aims to include all necessary items to support reviewer evaluation of radiomics-related manuscripts.
Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort ...has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
European cancer research stakeholders met in October 2022 in Heidelberg, Germany, at the 5th Gago conference on European Cancer Policy, to discuss the current cancer research and cancer care policy ...landscape in Europe. Meeting participants highlighted gaps in the existing European programmes focusing on cancer research, including Europe's Beating Cancer Plan (EBCP), the Mission on Cancer (MoC), Understanding Cancer (UNCAN.eu), and the joint action CRANE, and put forward the next priorities, in the form of the Heidelberg Manifesto for cancer research. This meeting report presents all discussions that shed light on how infrastructures can be effectively shaped for translational, prevention, clinical and outcomes cancer research, with a focus on implementation and sustainability and while engaging patients and the public. In addition, we summarize recommendations on how to introduce frameworks for the digitalization of European cancer research. Finally, we discuss what structures, commitment, and resources are needed to establish a collaborative cancer research environment in Europe to achieve the scale required for innovation.
European cancer research stakeholders met in October 2022 at the 5th Gago conference on European Cancer Policy. This meeting report presents all discussions on how infrastructures can be effectively shaped for translational, prevention, clinical and outcomes cancer research, with a focus on implementation and sustainability and while engaging patients and the public.
COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators ...as predictors for severe courses of COVID-19.
This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies.
Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73).
This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.
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