Fiducial Detection Research Articles

Space Vision

This paper presents a novel mobile serious game, "Space Vision", which uses a hidden-object mechanic with fiducial marker detection to gamify a clinical test of visual acuity - a key marker of childhood eye disease. For Space Vision to become a credible clinical tool that can facilitate the screening and home-monitoring of children's visual acuity, it must be able to sustain player engagement over the extended durations required to detect vision abnormalities. Hence, we pay particular attention to developing effective game characters - a crucial aspect of children's game design. Using an early prototype with 13 school children (aged 5-6 years), we investigate player experiences through a series of evaluation sessions, involving a single one-to-one observational playtest, semi-structured interview, and ideation activity with each child. Thematic analysis of our session video recordings, written observations, and ideation artefacts, found that future game iterations must: increase resonance between players and game characters by providing aesthetic and behavioural customisation, embrace more anthropomorphic styling, better imbue narratives pertaining to children's life scripts, and feature fantasy character powers as a form of self-expression. Meanwhile, greater physical device support and digital incentivisation of body posture and head position are key to improving the reliability of the visual acuity measurement.

Automated fiducial marker detection and localization in volumetric computed tomography images: a three-step hybrid approach with deep learning.

.Purpose: Automating fiducial detection and localization in the patient’s pre-operative images can lead to better registration accuracy, reduced human errors, and shorter intervention time. Most current approaches are optimized for a single marker type, mainly spherical adhesive markers. A fully automated algorithm is proposed and evaluated for screw and spherical titanium fiducials, typically used in high-accurate frameless surgical navigation.Approach: The algorithm builds on previous approaches with morphological functions and pose estimation algorithms. A 3D convolutional neural network (CNN) is proposed for the fiducial classification task and evaluated for both traditional closed-set and emerging open-set classifiers. A proposed digital ground-truth experiment, with cone-beam computed tomography (CBCT) imaging software, is performed to determine the localization accuracy of the algorithm. The localized fiducial positions in the CBCT images by the presented algorithm were compared to the actual known positions in the virtual phantom models. The difference represents the fiducial localization error (FLE).Results: A total of 241 screws, 151 spherical fiducials, and 1550 other structures are identified with the best true positive rate 95.9% for screw and 99.3% for spherical fiducials at 8.7% and 3.4% false positive rate, respectively. The best achieved FLE mean and its standard deviation for a screw and spherical marker are 58 (14) and , respectively.Conclusions: Accurate marker detection and localization were achieved, with spherical fiducials being superior to screws. Large marker volume and smaller voxel size yield significantly smaller FLEs. Attenuating noise by mesh smoothing has a minor effect on FLE. Future work will focus on expanding the CNN for image segmentation.

LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

Image-based fiducial markers are useful in problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, and vision-based simultaneous localization and mapping (SLAM). The state-of-the-art fiducial marker detection algorithms rely on the consistency of the ambient lighting. This paper introduces LiDARTag, a novel fiducial tag design and detection algorithm suitable for light detection and ranging (LiDAR) point clouds. The proposed method runs in real-time and can process data at 100 Hz, which is faster than the currently available LiDAR sensor frequencies. Because of the LiDAR sensors' nature, rapidly changing ambient lighting will not affect the detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment. In addition, the LiDARTag nicely complements and is compatible with existing visual fiducial markers, such as AprilTags, allowing for efficient multi-sensor fusion and calibration tasks. We further propose a concept of minimizing a fitting error between a point cloud and the marker's template to estimate the marker's pose. The proposed method achieves millimeter error in translation and a few degrees in rotation. Due to LiDAR returns' sparsity, the point cloud is lifted to a continuous function in a reproducing kernel Hilbert space where the inner product can be used to determine a marker's ID. The experimental results, verified by a motion capture system, confirm that the proposed method can reliably provide a tag's pose and unique ID code. The rejection of false positives is validated on the Google Cartographer indoor dataset and the Honda H3D outdoor dataset. All implementations are coded in C++ and are available at: https://github.com/UMich-BipedLab/LiDARTag.

Fiducial detection and registration for 3D IMRT QA with organ-specific dose information.

PurposeTwo‐dimensional (2D) IMRT QA has been widely performed in Radiation Oncology clinic. However, concerns regarding its sensitivity in detecting delivery errors and its clinical meaning have been raised in publications. In this study, a robust methodology of three‐dimensional (3D) IMRT QA using fiducial registration and structure‐mapping was proposed to acquire organ‐specific dose information.MethodsComputed tomography (CT) markers were placed on the PRESAGE dosimeter as fiducials before CT simulation. Subsequently, the images were transferred to the treatment planning system to create a verification plan for the examined treatment plan. Patient’s CT images were registered to the CT images of the dosimeter for structure mapping according to the positions of the fiducials. After irradiation, the 3D dose distribution was read‐out by an optical‐CT (OCT) scanner with fiducials shown on the OCT dose images. An automatic localization algorithm was developed in MATLAB to register the markers in the OCT images to those in the CT images of the dosimeter. SlicerRT was used to show and analyze the results. Fiducial registration error was acquired by measuring the discrepancies in 20 fiducial registrations, and thus the fiducial localization error and target registration error (TRE) was estimated.ResultsDosimetry comparison between the calculated and measured dose distribution in various forms were presented, including 2D isodose lines comparison, 3D isodose surfaces with patient’s anatomical structures, 2D and 3D gamma index, dose volume histogram and 3D view of gamma failing points. From the analysis of 20 fiducial registrations, fiducial registration error was measured to be 0.62 mm and fiducial localization error was calculated to be 0.44 mm. Target registration uncertainty of the proposed methodology was estimated to be within 0.3 mm in the area of dose measurement.ConclusionsThis study proposed a robust methodology of 3D measurement‐based IMRT QA for organ‐specific dose comparison and demonstrated its clinical feasibility.

Hybrid Spine Simulator Prototype for X-ray Free Pedicle Screws Fixation Training

Simulation for surgical training is increasingly being considered a valuable addition to traditional teaching methods. 3D-printed physical simulators can be used for preoperative planning and rehearsal in spine surgery to improve surgical workflows and postoperative patient outcomes. This paper proposes an innovative strategy to build a hybrid simulation platform for training of pedicle screws fixation: the proposed method combines 3D-printed patient-specific spine models with augmented reality functionalities and virtual X-ray visualization, thus avoiding any exposure to harmful radiation during the simulation. Software functionalities are implemented by using a low-cost tracking strategy based on fiducial marker detection. Quantitative tests demonstrate the accuracy of the method to track the vertebral model and surgical tools, and to coherently visualize them in either the augmented reality or virtual fluoroscopic modalities. The obtained results encourage further research and clinical validation towards the use of the simulator as an effective tool for training in pedicle screws insertion in lumbar vertebrae.

Prospective dual-surrogate validation study of periodic imaging during treatment for accurately monitoring intrafraction motion of prostate cancer patients

Background and purposeThe goal of this prospective study is to validate the use of periodic imaging during treatment with a fiducial marker detection algorithm using radiofrequency transponders for prostate cancer patients undergoing treatment for radiation therapy. Materials and methodsTen male patients were enrolled in this study and treated for prostate cancer with implanted electromagnetic monitoring beacons. We evaluated the accuracy and limitations of Intrafraction Motion Review (IMR) by comparing the known locations of the beacons using the electromagnetic monitoring system to the position data reported from IMR images. ResultsA total of 4054 images were taken during treatment. The difference in vector magnitude of the two methods is centered around zero (mean: 0.03 cm, SD: 0.16 cm) and Lin’s Concordance Correlation Coefficient (CCC) is 0.99 (95% CI: 0.98, 1) overall. The Euclidean distance between the two methods was close to zero (median: 0.09 cm, IQR: 0.06, 0.14 cm). The difference in distance between any two markers was centered around zero (mean: 0.01 cm, SD: 0.12 cm) and Lin’s CCC is 0.97 (95% CI: 0.96, 0.98) overall. ConclusionThe accuracy of the algorithm for detected markers within the 2D images is comparable to electromagnetic monitoring for fiducial identification when detected. IMR could provide an alternate solution for patients with contraindications of use of an electromagnetic monitoring system and a cost effective alternative to the acquisition of an additional system for patient monitoring, but does not provide data for pre-treatment set-up verification and real-time 3D positioning during treatment.

A generative adversarial network-based (GAN-based) architecture for automatic fiducial marker detection in prostate MRI-only radiotherapy simulation images.

Clinical sites utilizing magnetic resonance imaging (MRI)-only simulation for prostate radiotherapy planning typically use fiducial markers for pretreatment patient positioning and alignment. Fiducial markers appear as small signal voids in MRI images and are often difficult to discern. Existing clinical methods for fiducial marker localization require multiple MRI sequences and/or manual interaction and specialized expertise. In this study, we develop a robust method for automatic fiducial marker detection in prostate MRI simulation images and quantify the pretreatment alignment accuracy using automatically detected fiducial markers in MRI. In this study, a deep learning-based algorithm was used to convert MRI images into labeled fiducial marker volumes. Seventy-seven prostate cancer patients who received marker implantation prior to MRI and CT simulation imaging were selected for this study. Multiple-Echo T1 -VIBE MRI images were acquired, and images were stratified (at the patient level) based on the presence of intraprostatic calcifications. Ground truth (GT) contours were defined by an expert on MRI using CT images. Training was done using the pix2pix generative adversarial network (GAN) image-to-image translation package and model testing was done using fivefold cross validation. For performance comparison, an experienced medical dosimetrist and a medical physicist each manually contoured fiducial markers in MRI images. The percent of correct detections and F1 classification scores are reported for markers detected using the automatic detection algorithm and human observers. The patient positioning errors were quantified by calculating the target registration errors (TREs) from fiducial marker driven rigid registration between MRI and CBCT images. Target registration errors were quantified for fiducial marker contours defined on MRI by the automatic detection algorithm and the two expert human observers. Ninety-six percent of implanted fiducial markers were correctly identified using the automatic detection algorithm. Two expert raters correctly identified 97% and 96% of fiducial markers, respectively. The F1 classification score was 0.68, 0.75, and 0.72 for the automatic detection algorithm and two human raters, respectively. The main source of false discoveries was intraprostatic calcifications. The mean TRE differences between alignments from automatic detection algorithm and human detected markers and GT were <1mm. We have developed a deep learning-based approach to automatically detect fiducial markers in MRI-only simulation images in a clinically representative patient cohort. The automatic detection algorithm-predicted markers can allow for patient setup with similar accuracy to independent human observers.

Artificial intelligence-based framework in evaluating intrafraction motion for liver cancer robotic stereotactic body radiation therapy with fiducial tracking.

This study aimed to design a fully automated framework to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. The proposed framework includes three modules: (a) automated fiducial marker detection, (b) three-dimensional (3D) position reconstruction, and (c) intrafraction motion evaluation. A total of 5927 images from real patients treated with CyberKnife fiducial tracking were collected. The ground truth was established by labeling coarse bounding boxes manually, and binary mask images were then obtained by applying a binary threshold and filter. These images and labels were used to train a detection model using a fully convolutional network (fCN). The output of the detection model can be used to reconstruct the 3D positions of the fiducial markers and then evaluate the intrafraction motion via a rigid transformation. For a patient test, the motion amplitudes, rotations, and fiducial cohort deformations were calculated used the developed framework for 13 patients with a total of 52 fractions. The precision and recall of the fiducial marker detection model were 98.6% and 95.6%, respectively, showing high model performance. The mean (±SD) centroid error between the predicted fiducial markers and the ground truth was 0.25±0.47 pixels on the test data. For intrafraction motion evaluation, the mean (±SD) translations in the superior-posterior (SI), left-right (LR), and anterior-posterior (AP) directions were 13.1±2.2mm, 2.0±0.4mm, and 5.2±1.4mm, respectively, and the mean (±SD) rotations in the roll, pitch and yaw directions were 2.9±1.5°, 2.5±1.5°, and 3.1±2.2°. Seventy-one percent of the fractions had rotations larger than the system limitations. With rotation correction during rigid registration, only 2 of the 52 fractions had residual errors larger than 2mm in any direction, while without rotation correction, the probability of large residual errors increased to 46.2%. We developed a framework with high performance and accuracy for automatic fiducial marker detection, which can be used to evaluate intrafraction motion using orthogonal x-ray images from CyberKnife. For liver patients, most fractions have fiducial cohort rotations larger than the system limitations; however, the fiducial cohort deformation is small, especially for the scenario with rotation correction.

Progressive Dynamic Time Warping for Noninvasive Blood Pressure Estimation

Abstract Arterial blood pressure is one of the most important cardiovascular parameters. Yet, current-generation devices for continuous, noninvasive acquisition are few, expensive and bulky. Novel signal processing applied to easily acquired unimodal signals can alleviate this issue, reducing size, cost and expanding the use of such devices to ambulatory, everyday settings. The features of pulse waves acquired by photo- or impedance-plethysmography can be used to estimate the underlying blood pressure. We present a progressive dynamic time warping algorithm, which implicitly parametrizes the morphological changes in these waves. This warping method is universally applicable to most pulse wave shapes, as it is largely independent of fiducial point detection or explicit parametrization. The algorithm performance is validated in a feature selection and regression framework against a continuous, noninvasive Finapres NOVA monitor, regarding systolic, mean and diastolic pressures during a light physical strain test protocol on four clinically healthy subjects (age18- 33, one female). The obtained mean error is 2.13 mmHg, the mean absolute error is 5.4 mmHg and the standard deviation is 5.6 mmHg. These results improve on our previous work on dynamic time warping. Using single-sensor, peripherally acquired pulse waves, progressive dynamic time warping can thus improve the flexibility of noninvasive, continuous blood pressure estimation.

A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images

Abstract In some applications such as 2D-3D registration, undistorted images are required to achieve optimal results. These types of images can be obtained from a distortion-free C-arm (flat-panel detector) or by undistorting the images given from a conventional C-arm (analogue image intensifier.) Undistorting images require a plate with fiducials connected to the C-arm detector. Detecting fiducials is affected by differences in the image contrast due to elements in the background. Therefore, the results vary from image to image and could require manual tuning of parameters. We propose a deep-learning approach for detecting undistortion-platefiducials in X-ray images to overcome the drawbacks previously stated. With an undistortion plate, we took 1120 XRays using a C-arm in different poses. Every X-ray is afterward cut into 60 sub-images. We used these sub-images for training a convolutional neural network (CNN). Comparing the CNN and a traditional image processing method based on the Hough Circle algorithm, we found that the detected fiducials using the traditional method give a similar fiducial positioning error. Nevertheless, the fiducial detection rate goes from 89.7% using the traditional method to 100% with the developed CNN. The results show that the detection rate and precision of our deep-learning approach guarantee the undistortion of conventional C-Arm images.

Textual fiducial detection in breast conserving surgery for a near-real time image guidance system.

Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast cancer surgery provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent information leads to difficulty in ensuring complete tumor resection. Here we propose a novel image guidance platform that utilizes character-based fiducials for easy detection and small fiducial points for precise and accurate localization. Our system is work-flow friendly, and near-real time with use of stereo cameras for surface acquisition. Using simple image processing techniques, the proposed technique can localize fiducials and character labels, providing updates without relying on video history. Character based fiducial labels can be recognized and used to determine correspondence between left and right images in a pair of stereo cameras, and frame to frame in a sequence of images during a procedure. Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized. The stereo camera system can determine surface points with accuracy below 2mm when compared to optically tracked stylus points. These surface points are incorporated to a four-panel guidance display that includes preoperative supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.

Utilizing the TrueBeam Advanced Imaging Package to monitor intrafraction motion with periodic kV imaging and automatic marker detection during VMAT prostate treatments.

BackgroundFiducial markers are frequently used before treatment for image‐guided patient setup in radiation therapy (RT), but can also be used during treatment for image‐guided intrafraction motion detection. This report describes our implementation of automatic marker detection with periodic kV imaging (TrueBeam v2.5) to monitor and correct intrafraction motion during prostate RT.MethodsWe evaluated the reproducibility and accuracy of software fiducial detection using a phantom with 3 implanted fiducial markers. Clinical implementation for patients with intraprostatic fiducials receiving volumetric modulated arc therapy (VMAT) utilized periodic on‐board kV imaging with 10 s intervals during treatment delivery. For each image, the software automatically identified fiducial locations and determined whether their distance relative to planned locations were within a 3 mm tolerance. Motion was corrected if either ≥2 fiducials in a single image or ≥1 fiducial in sequential images were out of tolerance.ResultsPhantom studies demonstrated poorer performance of linear fiducials compared to collapsible fiducials, and wide variability to accurately detect fiducials across eight software settings. For any given setting, results were relatively reproducible and precise to ~0.5 mm. Across 17 patients treated with a median of 20 fractions, the software recommended a shift in 44% of fractions, and a shift was actually implemented after visual confirmation of movement greater than the 3 mm threshold in 20% of fractions. Adjustment of our approach led to improved accuracy for the latter (n = 7) patient subset. On average, table repositioning added 3.0 ± 0.3 min to patient time on table. Periodic kV imaging increased skin dose by an estimated 1 cGy per treatment arc.ConclusionsPeriodic kV imaging with automatic detection of motion during VMAT prostate treatments is commercially available, and can be successfully implemented to mitigate effects of intrafraction motion with careful attention to software settings.

Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation.

Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for it are conventionally from an isolated pulse of ECG and PPG signals. As a result, the estimated BP is intermittent. Objective: This paper presents feature extraction from each beat of ECG and PPG signals to make BP measurements uninterrupted. These features are extracted by employing Haar transformation to adaptively attenuate measurement noise and improve the fiducial point detection precision. Method: the use of only PAT feature as an independent variable leads to an inaccurate estimation of either Systolic Blood Pressure (SBP) or Diastolic Blood Pressure (DBP) or both. We propose the extraction of supplementary features that are highly correlated to physiological parameters. Concurrent data was collected as per the Association for the Advancement of Medical Instrumentation (AAMI) guidelines from 171 human subjects belonging to diverse age groups. An Adaptive Window Wavelet Transformation (AWWT) technique based on Haar wavelet transformation has been introduced to segregate pulses. Further, an algorithm based on log-linear regression analysis is developed to process extracted features from each beat to calculate BP. Results: The mean error of 0.43 and 0.20 mmHg, mean absolute error of 4.6 and 2.3 mmHg, and Standard deviation of 6.13 and 3.06 mmHg is achieved for SBP and DBP respectively. Conclusions: The features extracted are highly precise and evaluated BP values are as per the AAMI standards. Clinical Impact: This continuous real-time BP monitoring technique can be useful in the treatment of hypertensive and potential-hypertensive subjects.

Detectability of Modulated X-Rays from LISA’s Supermassive Black Hole Mergers

Abstract One of the central goals of the Laser Interferometer Space Antenna (LISA) is the detection of gravitational waves from the merger of supermassive black holes. Contrary to stellar-mass black hole mergers, such events are expected to be rich X-ray sources due to the accretion of material from the circumbinary disks onto the black holes. The orbital dynamics before merger is also expected to modulate the resulting X-ray emission via Doppler boosting in a quasiperiodic way and in a simple phase relation with the gravitational wave from the inspiral of the black holes. Detecting the X-ray source would enable a precise and early localization of the binary, thus allowing many telescopes to observe the very moment of the merger. Although identifying the correct X-ray source in the relatively large LISA sky localization will be challenging due to the presence of many confounding point sources, the quasiperiodic modulation may aid in the identification. We explore the practical feasibility of such idea. We simulate populations of merging supermassive black holes, their detection with LISA, and their X-ray light curves using a simple model. Taking the parameters of the X-ray telescope on the proposed NASA Transient Astrophysics Probe, we then design and simulate an observation campaign that searches for the modulated X-ray source while LISA is still observing the inspiral of the black holes. Assuming a fiducial LISA detection rate of 10 mergers per year at redshift closer than 3.5, we expect a few detections of modulated X-ray counterparts over the nominal duration of the LISA mission.

A Novel Approach for ECG-Based Human Identification Using Spectral Correlation and Deep Learning

In this paper, we utilize the electrocardiogram (ECG) as a primary biometric modality in human identification. The design steps of the proposed approach are the following. First, we segment the ECG signal and utilize its cyclostationarity and spectral correlation to enrich the signal’s original informational content. Then, we generate spectral correlation images. During this process, we disregard the time-consuming algorithmic step, typically used in other similar ECG-based machine learning approaches, namely the fiducial points detection and noise removal steps. Next, our spectral correlation images are fed into two convolutional neural network (CNN) architectures, which we fine-tune, test and evaluate, before we suggest a final architecture that demonstrates improved ECG-based human identification accuracy. To evaluate the efficiency of the proposed approach, we perform cross-validation on nine small and large scale ECG databases that encompass both normal and abnormal ECG signals. Experimental results show that independent of the database used, our approach results in improved system performance (compared to state-of-art approaches), yielding an identification accuracy, false acceptance and false rejection rates of 95.6%, 0.2%, and 0.1% respectively.

A Generative Adversarial Network (GAN)-based Approach for Automatic Fiducial Marker Detection in MR Radiotherapy Simulation Images

To design and evaluate a robust method for the automatic detection of fiducial markers (FMs) in MR simulation images used for prostate cancer radiotherapy planning. MR-only simulation continues to see expanded use due to the benefits of eliminating CT from standard MR/CT radiotherapy planning workflows, including reduced registration uncertainties, cost, and time. Standard x-ray-based IGRT treatments that use FMs for patient alignment pose a challenge for MR-only simulation, where FMs appear as small and often difficult to detect signal voids. In practice, multiple MR sequences with varying echo times and manual interaction are often required to distinguish FMs. Such methods are vulnerable to patient motion and require increased clinician effort. In this work we present an automatic FM detection method requiring a single standard GRE-based MR sequence. 21 prostate cancer patients who each had three FMs implanted prior to CT simulation and MR imaging were retrospectively selected for this study. Contrast enhanced T1 VIBE images were used in this study. Ground truth FM contours were manually defined on the patients’ CT and corresponding MR images. The cohort was divided into five groups, with one group held out for testing and the other four used for training in a five-fold cross validation approach. The MR images and FM contours (converted to labeled binary masks) from the training sets were used to train a Generative Adversarial Network (GAN) with the pix2pix image-to-image translation package. Trained models take an input MR image and output an image with suspected FM pixels labeled. The final three FM contours for each patient were defined as the three largest clustered objects in the labeled volume. Successful FM identification was defined based on the DICE coefficient of the automatically detected and ground truth FM contours. The Fiducial Registration Error (FRE) between the automatically detected and ground truth FM locations were calculated. FMs were correctly identified in 97% of tested cases (Table 1). All implanted FMs were correctly identified in 19/21 of the tested patients. The two incorrect FM identifications occurred with a marker implanted at the prostate/rectal wall interface and urethra. The mean FRE difference in the Anterior-Posterior, Left-Right and Superior-Inferior directions was 0.3 ± 0.7, 0.7 ± 1.4 and 0.8 ± 1.1 mm respectively. We developed a GAN-based approach to automatic FM detection in MR simulation images, and used it to demonstrate the feasibility of detecting FMs with a single standard MR sequence. Future work will involve testing the model on a larger cohort of patients and further investigating potential sources of FM misidentification, including calcifications and proximity to tissue interfaces.Abstract 189; Table 1Group 1 (Patient 1-4)Group 2 (Patient 5-8)Group 3 (Patient 9-12)Group 4 (Patient 13-17)Group 5 (Patient 18-21)% FM Detection accuracy1001001009292ΔFRE Magnitude (mm)1.4 ± 1.90.7 ± 11 ± 1.61.5 ± 1.81.6 ± 2.6 Open table in a new tab

Automated fiducial marker detection and fiducial point localization in CT images for lung biopsy image-guided surgery systems.

In the lung biopsy image-guided surgery systems, the fiducial markers are used for point-based registration of the patient space to the CT image space. Fiducial marker detection and fiducial point localization in CT images have great influence on the accuracy of registration and guidance. This study proposes a fiducial marker detection approach based on the features of marker image slice sequences and a fiducial point localization approach according to marker projection images, without depending on the priori-knowledge of the marker default parameters provided by the manufacturers. The accuracy of our method was validated based on a CT image dataset of 24 patients. The experimental results showed that all 144 markers of 24 patients were correctly detected, and the fiducial points were localized with the average error of 0.35 mm. In addition, the localization accuracy of the proposed method was improved by an average of 12.5% compared with the accuracy of the previous method using the marker default parameters provided by the manufacturers. Thus, the study demonstrated that the proposed detection and localization methods are accurate and robust, which is quite encouraging to meet the requirement of future clinical applications in the image guided lung biopsy and surgery systems.

How the power spectrum of dust continuum images may hide the presence of a characteristic filament width

Context. Herschel observations of interstellar clouds support a paradigm for star formation in which molecular filaments play a central role. One of the foundations of this paradigm is the finding, based on detailed studies of the transverse column density profiles observed with Herschel, that nearby molecular filaments share a common inner width of ∼0.1 pc. The existence of a characteristic filament width has been recently questioned, however, on the grounds that it seems inconsistent with the scale-free nature of the power spectrum of interstellar cloud images. Aims. In an effort to clarify the origin of this apparent discrepancy, we examined the power spectra of the Herschel/SPIRE 250 μm images of the Polaris, Aquila, and Taurus–L1495 clouds in detail and performed a number of simple numerical experiments by injecting synthetic filaments in both the Herschel images and synthetic background images. Methods. We constructed several populations of synthetic filaments of 0.1 pc width with realistic area filling factors (Afil) and distributions of column density contrasts (δc). After adding synthetic filaments to the original Herschel images, we recomputed the image power spectra and compared the results with the original, essentially scale-free power spectra. We used the χ2variance of the residuals between the best power-law fit and the output power spectrum in each simulation as a diagnostic of the presence (or absence) of a significant departure from a scale-free power spectrum. Results. We find that χ2variance depends primarily on the combined parameter δ22 Afil. According to our numerical experiments, a significant departure from a scale-free behavior and thus the presence of a characteristic filament width become detectable in the power spectrum when δ22 Afil ⪆ 0.1 for synthetic filaments with Gaussian profiles and δ22 Afil ⪆ 0.4 for synthetic filaments with Plummer-like density profiles. Analysis of the real Herschel 250 μm data suggests that δ22 Afil is ∼0.01 in the case of the Polaris cloud and ∼0.016 in the Aquila cloud, significantly below the fiducial detection limit of δ22 Afil ∼ 0.1 in both cases. In both clouds, the observed filament contrasts and area filling factors are such that the filamentary structure contributes only ∼1/5 of the power in the image power spectrum at angular frequencies where an effect of the characteristic filament width is expected. Conclusions. We conclude that the essentially scale-free power spectra of Herschel images remain consistent with the existence of a characteristic filament width ∼0.1 pc and do not invalidate the conclusions drawn from studies of the filament profiles.

Developing a MLC modifier program to improve fiducial detection for MV/kV imaging during hypofractionated prostate volumetric modulated arc therapy.

PurposeTo develop an Eclipse plug‐in (MLC_MODIFIER) that automatically modifies control points to expose fiducials obscured by MLC during VMAT, thereby facilitating tracking using periodic MV/kV imaging.MethodThree‐dimensional fiducial tracking was performed during VMAT by pairing short‐arc (3°) MV digital tomosynthesis (DTS) images to triggered kV images. To evaluate MLC_MODIFIER efficacy, two cohorts of patients were considered. For first 12 patients, plans were manually edited to expose one fiducial marker. Next for 15 patients, plans were modified using MLC_MODIFIER script. MLC_MODIFIER evaluated MLC apertures at appropriate angles for marker visibility. Angles subtended by control points were compressed and low‐dose “imaging” control points were inserted and exposed one marker with 1 cm margin. Patient's images were retrospectively reviewed to determine rate of MV registration failures. Failure categories were poor DTS image quality, MLC blockage of fiducials, or unknown reasons. Dosimetric differences in rectum, bladder, and urethra D1 cc, PTV maximum dose, and PTV dose homogeneity (PTV HI) were evaluated. Statistical significance was evaluated using Fisher's exact and Student's t test.ResultOverall MV registration failures, failures due to poor image quality, MLC blockage, and unknown reasons were 33% versus 8.9% (P < 0.0001), 8% versus 6.4% (P < 0.05), 13.6% versus 0.1% (P < 0.0001), and 7.6% versus 2.4% (P < 0.0001) for manually edited and MLC_MODIFIER plans, respectively. PTV maximum and HI increased on average from unmodified plans by 2.1% and 0.3% (P < 0.004) and 22.0% and 3.3% (P < 0.004) for manually edited and MLC_MODIFIED plans, respectively. Changes in bladder, rectum, and urethra D1CC were similar for each method and less than 0.7%.ConclusionIncreasing fiducial visibility via an automated process comprised of angular compression of control points and insertion of additional “imaging” control points is feasible. Degradation of plan quality is minimal. Fiducial detection and registration success rates are significantly improved compared to manually edited apertures.

Assessment of the accuracy of truebeam intrafraction motion review (IMR) system for prostate treatment guidance.

Intrafraction motion review (IMR), a real-time 2D, motion management feature of the Varian Truebeam™ incorporates triggered imaging, automatic fiducial marker detection and automatic beam hold. With the increasing adoption of high dose per fraction stereotactic body radiotherapy (SBRT) this system provides a potential means to ensure treatment accuracy. The goal of this study was therefore to investigate and quantify key performance characteristics of IMR for prostate treatment guidance. Phantom experiments were performed with a custom Computerized Imaging Reference Systems, Inc (CIRS) pelvis phantom with implanted gold seeds and the Hexamotion™ 5D motion platform. The system accuracy was assessed statically and under typical prostate motion trajectories. The IMR functionality and marker detectability was tested under different anatomical conditions and with different imaging acquisition modes. Imaging dose for triggered imaging modes was determined using an ionisation chamber based on IPEMB dose calibration protocol for kV energies. For zero displacement, the IMR demonstrated submillimeter agreement with the known position. Similarly, dynamic motion differences between the IMR reported position and 2D trajectory displacement were within 1mm. Static displacement in the anterior direction was reported by IMR as sinusoidal motion on the x-axis (kV angle). The 2D nature of IMR limits the ability to detect motion out of the plane of the kV image detector. Using typical clinical imaging settings, imaging dose determined at the patient surface was 2.58mGy/frame and the corresponding IMR displayed dose was 2.63mGy/frame. The methodology used was able to quantify the accuracy of the IMR system. The IMR was able to accurately and consistently report fiducial positions within the limitations inherent of a 2D system. IMR is fully integrated with the Truebeam system with an easy to use and efficient workflow and is clinically beneficial especially within the context of SBRT.