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Ultrasonic through-transmission data processed using the back-projection algorithm offers depth and lateral information about a defect beyond the capabilities of current through-transmission techniques. This technique was trialled on a carbon steel block containing side-drilled holes. Imaging artefacts can arise from the use of the backprojection algorithm, due to applying a weighting of one to each pixel, irrespective of how much of the pixel is intersected by the beam. Noise can also occur within the image where there are few intersections of the pixels made. This is seen at the edges of the image. In this paper, a novel back-projection technique utilises the weighting of pixels, dependent on the normalised weight of the beam that intersects them, to reduce any artefacts that occurred previously due to the backprojection algorithm. This paper also explores the use of the algebraic reconstruction technique (ART) algorithm for noise removal, thus increasing the sharpness of the defect.

Limited view tomographic reconstruction aims to reconstruct a tomographic image from a limited number of projection views arising from sparse view or limited angle acquisitions that reduce radiation dose or shorten scanning time. However, such a reconstruction suffers from severe artifacts due to the incompleteness of sinogram. To derive quality reconstruction, previous methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction. In this work, we propose a cascaded residual dense spatial-channel attention network consisting of residual dense spatial-channel attention networks and projection data fidelity layers. We evaluate our methods on two datasets. Our experimental results on AAPM Low Dose CT Grand Challenge datasets demonstrate that our algorithm achieves a consistent and substantial improvement over the existing neural network methods on both limited angle reconstruction and sparse view reconstruction. In addition, our experimental results on Deep Lesion datasets demonstrate that our method is able to generate high-quality reconstruction for 8 major lesion types.

The paper deals with the design of a system for changing the adhesion conditions in a road vehicle. The change in adhesive force is done by changing the radial reaction which the vehicle wheel transmits to the road. The paper describes a new device (SkidSystem) and its construction. The main element of the device is done in the form of a supporting frame; four supporting wheels are attached to this frame. As a result of the mechanical change of wheel lift, the radial reaction is reduced. Subsequently, an experimental measurement is performed, where the waveforms of basic physical quantities describing the road vehicle behaviour are measured. Driving in a straight line with the so-called panic braking was selected as an experimental test. An experimental vehicle is used to carry out the experimental measurement. According to the results found, modifications of the device are proposed, so that the results were closer to testing under real conditions. Disadvantages of SlidingFrame were the increase in the car weight, a slight change in the car centre of gravity and a limited angle of inclination of the body during intensive braking.

Ultrasonic inspection of through-transmission is limited due to the inability to obtain defect depth information. Loss of signal is used as the only indicator, providing lateral defect information. This is often a problem in ultrasonic inspection. Radiographic acquisition techniques, where the X-ray source acts as the transmitter and the detector as the receiver, are conceptionally similar to ultrasonic through-transmission. In the latter, the tomography back-projection method is used to reconstruct images of an object that has been subjected to a minimum of 180° of rotation, to allow for full coverage of the item. In this paper, a novel approach based on back-projection is presented to improve image resolution and defect detectability. Two ultrasonic transducers in through-transmission configuration are utilised to capture data for image processing. The rotation of the transmitter and receiver is not possible in this set-up and, therefore, the reconstruction relies on the artificial generation of a limited rotation. Two probes are aligned either side of the material and are used to gather the ultrasonic signals. These signals are processed before the reconstruction algorithm is applied to them. Various processing and imaging reconstruction algorithms are explored, building on the basic back-projection method to obtain images that are better focused. This technique could be used within materials where there are high attenuation levels and, therefore, traditional pulse-echo is not feasible.

Optical camera communication (OCC) is a promising technology for camera-mounted mobile robots (MRs), where it can use its camera to communicate with light-emitting diode (LED) infrastructures. Due to the limited angle of view of current commercial cameras used in MRs, the cameras cannot detect LED everywhere inside an area illuminated by an LED; hence, much of the illumination surface remains unutilized. Herein, we propose an automatic camera inflection algorithm to successfully detect LED anywhere inside an LED cell (the total coverage area illuminated by LED). The inflection mechanism is a supervised learning approach. The technique utilizes the coordinate information (i.e., X and Y coordinates) of an LED cell, which is further employed to track the position of the LED. The coordinate data is automatically updated when the MR travels from one position to another. Furthermore, we perform simulations that include a discussion on tilt angle variation with increasing distance traveled by the MR and a performance analysis that demonstrates the importance and feasibility of the developed algorithm. Finally, a testbed prototype for OCC is implemented to demonstrate the proof of angle measurement theory and bit-error-rate performance of the proposed system.

The Tournemire Underground Research Laboratory (Aveyron, France), developed by the French Institute for Radiological Protection and Nuclear Safety, is composed of a tunnel excavated in an argillaceous geological layer belonging to a limestone–argillite–limestone sub-horizontal sedimentary sequence. Sub-vertical fault zones are intercepted by drifts and boreholes excavated from the tunnel in the argillaceous layer at a depth of about 250 m. These fault zones are characterized by velocities lower than those of the embedding rocks. In this study, we assess the capacity of first arrival traveltime tomography to detect and characterize such fault zones in a surface-to-depth transmission configuration with a limited angle aperture. The source at the surface is a sledgehammer and turns out to be very repetitive. The source trigger is transmitted to the acquisition bay via a VHF transmitter. Receivers are located at depth in underground works. First arrivals are clear and traveltime picking is straightforward. Before inverting real data, we conduct synthetic tests with the real acquisition geometry to assess limits and validity of first arrival traveltime tomography to detect a lower velocity fault zone using different initial configurations and inversion constraints during the inversion. We show that introducing a priori information on velocities in the shallow subsurface, limestones and argillaceous layers and constraints on maximum velocities greatly helps improving results and subsequent interpretation. Velocities obtained from the real data display a main lower velocity zone that extends from the galleries in the argillaceous layer right up to the surface in limestones. The location and extension of this lower velocity zone are consistent with fault and fracture zones mapped from geological observations.

[EN] Several research groups are studying organ-dedicated limited angle positron emission tomography (PET) systems to optimize performance-cost ratio, sensitivity, access to the patient, and/or flexibility. Often open systems are considered, typically consisting of two detector panels of various sizes. Such systems provide incomplete sampling due to limited angular coverage and/or truncation, which leads to artifacts in the reconstructed activity images. In addition, these organ-dedicated PET systems are usually stand-alone systems, and as a result, no attenuation information can be obtained from anatomical images acquired in the same imaging session. It has been shown that the use of time-of-flight (TOF) information reduces incomplete data artifacts and enables the joint estimation of the activity and the attenuation factors. In this work, we explore with simple 2-D simulations the performance and stability of a joint reconstruction algorithm, for imaging with a limited angle PET system. The reconstruction is based on the so-called maximum-likelihood attenuation correction factors (MLACF) algorithm and uses linear attenuation coefficients in a known-tissue-class region to obtain absolute quantification. Different panel sizes and different TOF resolutions are considered. The noise propagation is compared to that of MLEM reconstruction with exact attenuation correction (AC) for the same PET system. The results show that with good TOF resolution, images of good visual quality can be obtained. If also a good scatter correction can be implemented, quantitative PET imaging will be possible. Further research, in particular on scatter correction, is required., This work was supported in part by the European Research Council (ERC) through the European Unions Horizon 2020 Research and Innovation Program under Grant 695536; in part by the Research Foundation Flanders (FWO) under Project 12T7118N and Project G062220N; and in part by the NIH Project under Grant 1P41EB017183-01A1.

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

Limited-angle computed tomography (CT) reconstruction has a great potential to reduce X-ray radiation dose or scanning time. Suppressing shading artifacts is challenging, but of great practical significance in limited-angle CT. Traditional methods based on total variation (TV) cannot effectively remove the shading artifacts, prior image constrained compressed sensing (PICCS) is a promising method, but is sensitive to the quality of the prior image. In micro-CT, a prior image reconstructed by filtered back-projection (FBP) may contain high-level noise. An image reconstructed by PICCS tends to inherit both structures and noise of the prior image. In this study, to suppress noise and shading artifacts, we propose a new limited-angle CT reconstruction model called prior image induced relative total variation (piiRTV), that uses the structure information of a prior image to guide limited-angle CT reconstruction. The proposed piiRTV is compared to TV and PICCS. Numerical simulations and experiments on real CT projections demonstrate the effectiveness of piiRTV in suppression of noise and shading artifacts. In addition, the proposed piiRTV is more robust to the prior image quality than PICCS.

Purpose The limited-angle intrafractional verification (LIVE) system was developed to track tumor movement during stereotactic body radiation therapy (SBRT). However, the four-dimensional (4D) MV/kV imaging procedure results in additional radiation dose to patients. This study is to quantify imaging radiation dose from optimized MV/kV image acquisition in the LIVE system and to determine if it exceeds the American Association of Physicists in Medicine Task Group Report 180 image dose threshold. Methods TrueBeam™ platform with a fully integrated system for image guidance was studied. Monte Carlo-simulated kV and MV beams were calibrated and then used as incident sources in an EGSnrc Monte Carlo dose calculation in a CT image-based patient model. In three representative lung SBRT treatments evaluated in this study, tumors were located in the patient's posterior left lung, mid-left lung, and right upper lung. The optimized imaging sequence comprised of arcs ranging from 2 to 7, acquired between adjacent three-dimensional (3D)/IMRT beams, with multiple simultaneous kV (125 kVp) and MV (6 MV) image projections in each arc, for different optimization scenarios. The MV imaging fields were generally confined to the treatment target while kV images were acquired with a normal open field size with a full bow-tie filter. Results In a seven-arc acquisitions case (highest imaging dose scenario), the maximum kV imaging doses to 50% of the tissue volume (D50 from DVHs), for spinal cord, right lung, heart, left lung, and the target, were 0.4, 0.4, 0.6, 0.7, and 1.4 cGy, respectively. The corresponding MV imaging doses were 0.1 cGy to spinal cord, right lung, heart, and left lung, and 11 cGy to target. In contrast, the maximum radiation dose from two cases treated with two Volumetric-Modulated Arc Therapy (VMAT) fields and two-arc image acquisitions is approximately 30% of that of the seven-arc acquisition. Conclusions We have evaluated the additional radiation dose resulting from optimized LIVE system MV/kV image acquisitions in two best (least imaging dose) and one worst (highest imaging dose) lung SBRT treatment scenarios. The results show that these MV/kV imaging doses are comparable to those resulting from current imaging procedures used in Image-Guided Radiation Therapy (IGRT) and are within the dose threshold of 5% target dose as recommended by the AAPM TG-180 report.

Optical projection tomography (OPT) is a novel three-dimensional imaging technique, which provides an approach to recreating the three-dimensional images of biological specimens ranging from millimeters to centimeters. In the current OPT setup, the specimen was immersed in the index-matching fluid. In this case, the specimen could not survive or the survival time was too short for longitudinal imaging. In this paper, we first designed a new type of sample fixation method for in vivo OPT imaging. The specimen was embedded into a transparent gel in a petri dish, and the dish was affixed to the rotational stage of our homemade OPT system. As the specimen does not need to be immersed in the index-matching fluid, this method can reduce the damage to the specimen and it is more conducive to longitudinal observation for in vivo OPT. However, this fixation method induces a problem of insufficient measurements. The angles parallel to or nearly parallel to the surface of the dish cannot be acquired. To address this problem, we, then, used a limited-angle reconstruction framework for the novel sample fixation-based OPT, which combines the algebraic reconstruction technique (ART) algorithm with prior information to solve the inverse reconstruction problem. The feasibility and effectiveness of the proposed sample fixation method and the limited-angle reconstruction framework were verified by the simulations and experiments.

In recent years, the directional sensor networks have been attractive to researchers due to their wide and different applications. These networks normally contain a number of self-configurable directional sensors holding adjustable spherical sectors with limited angle. One of the most significant problems in such networks is how to monitor the targets scattered in these networks using sensors with adjustable sensing range and, at the same time, maximize the network lifetime. This problem is recognized as Maximum Network Lifetime With Adjustable Ranges; it has been already proved as an NP-complete problem. As an efficient solution to this problem, the present paper proposes a target-oriented GA-based algorithm that can form cover sets comprising sensors with appropriate directions and sensing ranges in a way to desirably monitor all targets in the network. We examined the efficiency of the proposed algorithm by comparing its obtained results with those of a greedy-based one introduced recently in literature. The comparative results confirmed the efficient performance of the proposed algorithm and also its superiority over the greedy-based algorithm in terms of extending the network lifetime.

Restricted by the scanning environment and the radiation exposure of computed tomography (CT), the obtained projection data are sometimes incomplete, which results in an ill-posed problem, such as a limited-angle image reconstruction. In such circumstance, the commonly used analytic and iterative algorithms, such as filtered back-projection and simultaneous algebraic reconstruction technique (SART), will not work well. Nowadays, a popular iterative image reconstruction algorithm ( $${\hbox {SART}}+{\hbox {TV}}$$ ) solving the optimization model based on the minimization of total variation (TV) of the image applies to the sparse-view reconstruction problem well; it is not effective on small limited-angle reconstruction problem, especially in aspect of suppressing slope artifacts when the limited-angle projection views are severely reduced. In this work, we develop a reconstruction model based on the Mumford–Shah-like model and wavelet tight frames that applies to limited-angle CT; and the corresponding iterative method is given. Numerical experiments and quantitative analysis demonstrate that our method outperforms SART and $${\hbox {SART}}+{\hbox {TV}}$$ in suppressing slope artifacts when the limited-angle projection views are severely decreased.

Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging, especially when the angle range is extremely small. Both analytical and iterative models need more projections for effective modeling. Deep learning methods have gained prevalence due to their excellent reconstruction performances, but such success is mainly limited within the same dataset and does not generalize across datasets with different distributions. Hereby we propose ExtraPolationNetwork for limited-angle CT reconstruction via the introduction of a sinogram extrapolation module, which is theoretically justified. The module complements extra sinogram information and boots model generalizability. Extensive experimental results show that our reconstruction model achieves state-of-the-art performance on NIH-AAPM dataset, similar to existing approaches. More importantly, we show that using such a sinogram extrapolation module significantly improves the generalization capability of the model on unseen datasets (e.g., COVID-19 and LIDC datasets) when compared to existing approaches.

Uniportal video-assisted thoracic surgery is becoming more common worldwide, but the limited angle of approach of inserted surgical instruments makes it challenging. Because of this, segmentectomy via a uniportal approach is more difficult technically than a multiportal approach. In addition, the inflation/deflation technique, which is a standard method for identifying the intersegmental plane, is often less useful because it can be difficult to get a good surgical view. To resolve this problem, a technique using near infrared imaging and indocyanine green administration technique can be very helpful in performing a uniportal segmentectomy. In this video tutorial, we demonstrate a uniportal thoracoscopic dorsal segmentectomy of the right upper lobe of a patient with primary lung cancer, using a near infrared imaging/indocyanine green technique. We explain the nuances of the procedure and how to perform it, and we discuss our successful results.

We propose an algorithm for limited angle CT which extends the LEARN algorithm of Chen et al. [IEEE TMI, 2018]. The algorithm is based on a superiorized [Herman et al., Med Phys, 2012] version of the simultaneous algebraic reconstruction technique (SART), in which a convolutional neural network (CNN) is used to perturb the solution between SART iterations. Specifically, by assuming a fixed number of iterations and unrolling the iteration, it can be trained in end-to-end fashion as a neural network with untrained layers (representing forward and back projection) in addition to trained convolutional layers. Additionally, we replace the three-layer CNN used in LEARN with a U-net-based architecture to achieve multilevel decomposition and precise localization during the iterative training. The algorithm is implemented in Python using the Pyro-NN library [Syben et al., Med Phys, 2019] and trained on simulated limited angle data generated from lung CT images. Our numerical experiments indicate that the superiorized SART algorithm with U-net-based CNN significantly reduces artifacts arising in limited angle scenarios. In particular, it is able to recover internal features that similar methods using the three-layer CNN are unable to recover, such as edges of ribs and soft tissue.

The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed by conventional methods, such as the Feldkamp, Davis and Kres (FDK) algorithm [1], suffer from heavy artifacts and missing features. In this paper, we propose a novel pipeline of neural networks jointly by a FDK-based neural network revisited from Wurfl et al.’s work [2] and an image domain U-Net to enhance the 3-dimensional reconstruction quality for limited projection sinogram less than 180 degrees, i.e. 145 degrees in our work. Experimental results, on simulated projections of real-scan CTs, show that the proposed pipeline can reduce some of the major artifacts caused by the limited views while keep the key features, with a 16.60% improvement than Wurfl et al.’s work on peak signal-to-noise ratio.

This paper presents an analytical study of the radial-flux slotless limited-angle torque motors. The modelling is based on the magnetic equivalent circuit of the actuators and uses the electromagnetic equations to calculate the air-gap flux as well as the produced torque. This model is then used for designing the actuators both in outer-rotor and inner-rotor structures, considering the design constraints and desired characteristics. As the objectives in design stage usually conflict with each other, an intelligent multi-objective optimization algorithm is required to design the best fit actuators. Analytical and simulation results are presented and compared to show the accuracy of the model and verify the design equations as well as the design approach. As this type of actuators is a key element in industrial control, the contributions of this paper are focused on new analytical field solution based on magnetic equivalent circuit, design and optimization approach for radial-flux structure and introducing a general procedure that could be extended to similar structures and actuators.

Inverse problems spanning four or more dimensions such as space, time and other independent parameters have become increasingly important. State-of-the-art 4D reconstruction methods use model based iterative reconstruction (MBIR), but depend critically on the quality of the prior modeling. Recently, plug-and-play (PnP) methods have been shown to be an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms. However, state-of-the-art denoisers such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D or 3D images and extending them to higher dimensions is difficult due to algorithmic complexity and the increased difficulty of effective training. In this paper, we present multi-slice fusion, a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating the multiple lower-dimensional models. We apply our method to 4D cone-beam X-ray CT reconstruction for non destructive evaluation (NDE) of samples that are dynamically moving during acquisition. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time and demonstrate the inherent parallelizable nature of the algorithm. We present simulated and real experimental results on sparse-view and limited-angle CT data to demonstrate that multi-slice fusion can substantially improve the quality of reconstructions relative to traditional methods, while also being practical to implement and train., arXiv admin note: substantial text overlap with arXiv:1906.06601

In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the alternating direction method of multipliers (ADMM) has guaranteed convergence under certain conditions. In addition, we incorporate a box constraint that is reasonable for imaging applications, and the convergence for the additional box constraint can also be established. Numerical results on both synthetic and experimental datasets demonstrate the effectiveness and efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction., 29 pages

Thoracoscopic major pulmonary resection via a uniportal approach is becoming more common worldwide as the adoption of minimally invasive surgical procedures increases. With a uniportal approach, 3 or 4 surgical instruments, including a thoracoscope, are simultaneously inserted via an approximately 4-cm access port. The surgeon must master the surgical techniques required to perform a major pulmonary resection safely using the uniportal method, specifically the challenge of dissecting the tissue and inserting staplers unidirectionally, due to the limited angle of approach. In this video tutorial, we demonstrate a uniportal thoracoscopic upper division segmentectomy of the left upper lobe of a patient with primary lung cancer, using a unidirectional anterior approach without dissection of the fissure. We explain the nuances of the procedure and how to perform it, and we discuss our successful results.

Electrical resistance tomography (ERT) has been investigated in monitoring conductive flows due to its high speed, non-intrusive and no radiation hazard advantages. Recently, we have developed an ERT system for the novel application of smart wastewater metering. The dedicated low cost and high-speed design of the reported ERT device allows for imaging pipes with different flow constituents and monitoring the sewer networks. This work extends the capability of such a system to work with partially filled lateral pipes where the incomplete data issue arises due to the electrodes losing contact with the conductive medium. Although the ERT for such a limited region has been developed for many years, there is no study on imaging content within these limited regions. For wastewater monitoring, this means imaging the wastewater and solid inclusions at the same time. This paper has presented a modified ERT system that has the capacity to image inclusions within the conductive region using limited data. We have adjusted the ERT hardware to register the information of the non-contact electrodes and hence the valid measurements. A limited region image reconstruction method based on Jacobian reformulation is applied to gain robustness when it comes to inclusion recovery in limited data ERT. Both simulation and experimental results have demonstrated an enhanced performance brought by the limited region method in comparison to the global reconstruction.

The fissureless technique for lobectomy is considered one of the most useful and effective techniques for patients who have a fused fissure because it offers a relatively low risk of prolonged postoperative air leakage. In the fissureless technique, we use "unidirectional dissection" or "unidirectional stapling" because of the limited angle of approach necessitated by thoracoscopic surgery. Uniportal thoracoscopic surgery has even more access angle limitations than a typical multiportal approach and can be very challenging. When performing fissureless lobectomies, especially lower lobectomies, we have to insert a stapler to divide the bronchus behind which the pulmonary arteries run. It is critically important to avoid injury to the pulmonary arteries as this might cause catastrophic bleeding. We use the "suction device trick" to protect the pulmonary arteries: The suction device is inserted between the lower bronchus and the pulmonary artery, which runs behind it, when stapling, and this ensures that stapling can safely be done without any risk of injury to the pulmonary artery. In this video tutorial we demonstrate our technique for a uniportal thoracoscopic fissureless right lower lobectomy in a patient with a dense fissure, using the suction device trick, and we explain the nuances of performing it.

Since the seventies, positron emission tomography (PET) has become an invaluable medical molecular imaging modality with an unprecedented sensitivity at the picomolar level, especially for cancer diagnosis and the monitoring of its response to therapy. More recently, its combination with x-ray computed tomography (CT) or magnetic resonance (MR) has added high precision anatomic information in fused PET/CT and PET/MR images, thus compensating for the modest intrinsic spatial resolution of PET. Nevertheless, a number of medical challenges call for further improvements in PET sensitivity. These concern in particular new treatment opportunities in the context personalized (also called precision) medicine, such as the need to dynamically track a small number of cells in cancer immunotherapy or stem cells for tissue repair procedures. A better signal-to-noise ratio (SNR) in the image would allow detecting smaller size tumours together with a better staging of the patients, thus increasing the chances of putting cancer in complete remission. Moreover, there is an increasing demand for reducing the radioactive doses injected to the patients without impairing image quality., There are three ways to improve PET scanner sensitivity: improving detector efficiency, increasing geometrical acceptance of the imaging device and pushing the timing performance of the detectors. Currently, some pre-localization of the electron-positron annihilation along a line-of-response (LOR) given by the detection of a pair of annihilation photons is provided by the detection of the time difference between the two photons, also known as the time-of-flight (TOF) difference of the photons, whose accuracy is given by the coincidence time resolution (CTR). A CTR of about 10 picoseconds FWHM will ultimately allow to obtain a direct 3D volume representation of the activity distribution of a positron emitting radiopharmaceutical, at the millimetre level, thus introducing a quantum leap in PET imaging and quantification and fostering more frequent use of C-11 radiopharmaceuticals., The present roadmap article toward the advent of 10 ps TOF-PET addresses the status and current/future challenges along the development of TOF-PET with the objective to reach this mythic 10 ps frontier that will open the door to real-time volume imaging virtually without tomographic inversion. The medical impact and prospects to achieve this technological revolution from the detection and image reconstruction point-of-views, together with a few perspectives beyond the TOF-PET application are discussed.

We show that the cone-adapted shearlet coefficients can be computed by means of the limited angle horizontal and vertical (affine) Radon transforms and the one-dimensional wavelet transform. This yields formulas that open new perspectives for the inversion of the Radon transform.

Full angular rotational projections cannot always be acquired in tomographic reconstructions because of the limited space in the experimental setup, leading to the `missing wedge' situation. In this paper, a recovering `missing wedge' discrete algebraic reconstruction technique algorithm (rmwDART) has been proposed to solve the `missing wedge' problem and improve the quality of the three-dimensional reconstruction without prior knowledge of the material component's number or the material's values. By using oversegmentation, boundary extraction and mathematical morphological operations, `missing wedge' artifact areas can be located. Then, in the iteration process, by updating the located areas and regions, high-quality reconstructions can be obtained from the simulations, and the reconstructed images based on the rmwDART algorithm can be obtained from soft X-ray nano-computed tomography experiments. The results showed that there is the potential for discrete tomography.

Radiotherapy to the breast after surgery sometimes requires adjoining nodes to be included in the treatment volume. In these cases, the traditional approach has been a complex 3‐Dimensional Conformal Radiotherapy (3DCRT) beam arrangement which can result in significant dose heterogeneity at the beam junctions. A Volumetric Modulated Arc Therapy (VMAT) beam arrangement has previously been proposed for breast cases, where the chest wall/breast is treated with a limited angle (partial arc) tangential VMAT technique (Virén et al. [2015] Radiat Oncol. 10:79). In our study, this approach is extended to breast and chest wall cases with adjoining nodes by adding a separate conventional VMAT arc field specifically limited to the superior nodes. This VMAT method was implemented using a semiautomated approach on 27 patients, and the resultant plan compared to a monoisocentric 3DCRT plan. Plan statistics, Dose‐Volume Histogram (DVH) analysis and Radiation Oncologist (RO) preference were assessed. When compared to the 3DCRT technique, the VMAT planning method was found to result in better target volume coverage, high doses to organs at risk (OAR) were reduced but greater OAR volumes received low doses. Having said that, the volume receiving low doses with this tangential VMAT technique was less than that of other VMAT planning methods described in the literature, and the integral dose was less than the 3DCRT method. The VMAT technique also resulted in more robust junction doses that the 3DCRT method. RO review found that the VMAT technique was preferred in 81% of cases. Specifically, the VMAT plans were preferred in all categories of patients except left chest wall cases where the intermammary nodes were also treated. The VMAT technique described here is a useful addition to the treatment options available for breast/chest wall and nodal patients.

Small geometric misalignments of micro computed tomography (CT) system will cause geometric artefacts in the reconstructed image. A new correction method of geometrical artefacts based on marker and non-linear optimisation model is proposed. In this method, the simple balls marker and the measured objects are scanned simultaneously, and the geometric parameters of the micro-CT system are precisely estimated by solving the non-linear optimisation model which is based on the scanning data. The geometric artefacts caused by geometric parameters are corrected and the authors can reconstruct the image correctly. In addition to estimating geometric parameters for the traditional scanning mode, the proposed method can also be used for the limited angle CT scanning and the half detector CT scanning. Simulated experiments and real experiments verify that the correction method effectively decrease the geometric artefacts of micro-CT images.

The limited-angle computed tomography (CT) reconstruction problem is an ill-posed inverse problem, and the parameter selection for limited-angle CT iteration reconstruction is a difficult issue in practical application. In this paper, to alleviate the instability of limited-angle CT reconstruction problem and automatize the reconstruction process, we propose an adaptive iteration reconstruction method that the regularization parameter is chosen adaptively via the plot of the normalized wavelet coefficients fitting residual versus that the ℓ 0 {\ell_{0}} regularization part. The experimental results show that the reconstructed images using the method with adapted regularization parameter are almost as good as that using the non-adapted parameter method in terms of visual inspection, in addition, our method has an advantage in adaptively choosing the regularization parameter.

The problem of a space object orbit estimation and determination of region with minimum volume, which contains with guarantee orbit state vector, is considered. Measurements are provided by the telescope and estimation of angle measurements errors are limited in absolute value. The significant dependence of the guaranteed area size on the choice of coordinate system for observations is found, the algorithm for determination of the minimum volume area is suggested based on the simplex-method. In addition the comparison analysis of results of created algorithm with known ones based on various methods (guaranteed and probabilistic) is presented. The developed algorithm is able to give more precise results in several cases in comparison with other known algorithms.

In CT image reconstruction, the limited angle problem is an ill-posed problem. To solve this ill-posed problem, the total variation (TV) regularization has been widely used in image reconstruction. In recent years, an algorithm based on TV regularization and alternating direction multiplier method has been proposed and named as ADTVM. ADTVM can reconstruct high-quality images for the limited angle problem. However, ADTVM just considers the homogeneity of image at different directions. In real application, image possesses different properties at different directions. Therefore, we construct an adaptive weighted TV (AwTV) regularization and propose the ADM-AwTV method on the basis of ADTVM. ADM-AwTV is an iterative image reconstruction method which can reveal the anisotropy of image, adaptively. In each iteration, the weights of different directions can update according to the last reconstruction image. Experiments on two simulation images and one real projection data show that the proposed method achieves better reconstruction results than the other iterative image reconstruction methods such as ART-TV and ADTVM for the limited angle problem.