Projection Views Research Articles

Selecting projection views based on error equidistribution for computed tomography

Background Nonuniform sampling is a useful technique to optimize the acquisition of projections with a limited budget. Existing methods for selecting important projection views have limitations, such as relying on blueprint images or excessive computing resources. Methods We aim to develop a simple nonuniform sampling method for selecting informative projection views suitable for practical CT applications. The proposed algorithm is inspired by two key observations: projection errors contain angle-specific information, and adding views around error peaks effectively reduces errors and improves reconstruction. Given a budget and an initial view set, the proposed method involves: estimating projection errors based on current set of projection views, adding more projection views based on error equidistribution to smooth out errors, and final image reconstruction based on the new set of projection views. This process can be recursive, and the initial view can be obtained uniformly or from a prior for greater efficiency. Results Comparison with popular view selection algorithms using simulated and real data demonstrates consistently superior performance in identifying optimal views and generating high-quality reconstructions. Notably, the new algorithm performs well in both PSNR and SSIM metrics while being computationally efficient, enhancing its practicality for CT optimization. Conclusions A projection view selection algorithm based on error equidistribution is proposed, offering superior reconstruction quality and efficiency over existing methods. It is ready for real CT applications to optimize dose utilization.

TD-STrans: Tri-domain sparse-view CT reconstruction based on sparse transformer.

Sparse-view computed tomography (CT) speeds up scanning and reduces radiation exposure in medical diagnosis. However, when the projection views are severely under-sampled, deep learning-based reconstruction methods often suffer from over-smoothing of the reconstructed images due to the lack of high-frequency information. To address this issue, we introduce frequency domain information into the popular projection-image domain reconstruction, proposing a Tri-Domain sparse-view CT reconstruction model based on Sparse Transformer (TD-STrans). TD-STrans integrates three essential modules: the projection recovery module completes the sparse-view projection, the Fourier domain filling module mitigates artifacts and over-smoothing by filling in missing high-frequency details; the image refinement module further enhances and preserves image details. Additionally, a multi-domain joint loss function is designed to simultaneously enhance the reconstruction quality in the projection domain, image domain, and frequency domain, thereby further improving the preservation of image details. The results of simulation experiments on the lymph node dataset and real experiments on the walnut dataset consistently demonstrate the effectiveness of TD-STrans in artifact removal, suppression of over-smoothing, and preservation of structural fidelity. The reconstruction results of TD-STrans indicate that sparse transformer across multiple domains can alleviate over-smoothing and detail loss caused by reduced views, offering a novel solution for ultra-sparse-view CT imaging.

Hybrid plug-and-play CT image restoration using nonconvex low-rank group sparsity and deep denoiser priors

Objective. Low-dose computed tomography (LDCT) is an imaging technique that can effectively help patients reduce radiation dose, which has attracted increasing interest from researchers in the field of medical imaging. Nevertheless, LDCT imaging is often affected by a large amount of noise, making it difficult to clearly display subtle abnormalities or lesions. Therefore, this paper proposes a multiple complementary priors CT image reconstruction method by simultaneously considering both the internal prior and external image information of CT images, thereby enhancing the reconstruction quality of CT images.Approach. Specifically, we propose a CT image reconstruction method based on weighted nonconvex low-rank regularized group sparse and deep image priors under hybrid plug-and-play framework by utilizing the weighted nonconvex low rankness and group sparsity of dictionary domain coefficients of each group of similar patches, and a convolutional neural network denoiser. To make the proposed reconstruction problem easier to tackle, we utilize the alternate direction method of multipliers for optimization.Main results. To verify the performance of the proposed method, we conduct detailed simulation experiments on the images of the abdominal, pelvic, and thoracic at projection views of 45, 65, and 85, and at noise levels of1×105and1×106, respectively. A large number of qualitative and quantitative experimental results indicate that the proposed method has achieved better results in texture preservation and noise suppression compared to several existing iterative reconstruction methods.Significance. The proposed method fully considers the internal nonlocal low rankness and sparsity, as well as the external local information of CT images, providing a more effective solution for CT image reconstruction. Consequently, this method enables doctors to diagnose and treat diseases more accurately by reconstructing high-quality CT images.

ATLAS upgrade for the HL-LHC

While the on-going Run-3 data-taking campaign will provide twice the integrated proton–proton luminosity currently available at the LHC, most of the data expected for the full LHC physics program will only be delivered during the HL-LHC phase. For this, the LHC will undergo an ambitious upgrade program to be able to deliver an instantaneous luminosity of 7.5⋅1034cm−2s−1, allowing the collection of more than 3 ab−1 of data at s=13.6(14)TeV. This unprecedented data sample will allow the ATLAS experiment to perform several precision measurements to constrain the Standard Model (SM) in yet unexplored regions of phase space, in particular in the Higgs sector, only accessible at the LHC. To benefit from such a rich data sample, it is fundamental to upgrade the detector to cope with the challenging experimental conditions that include radiation doses 10 times higher than what ATLAS experienced since its construction and increase in the number of interactions per bunch crossing from the current 50-60 to 200. The upgrade of the ATLAS detector comprises a completely new all-silicon tracker with extended rapidity coverage that will replace the current inner tracker detector; a redesigned trigger and data acquisition system for the calorimeters and muon systems, allowing the implementation of a readout system that can operate complex trigger algorithms at 40 MHz. Finally, a new subsystem called High Granularity Timing Detector will aid the track-vertex association in the forward region by providing timing information to the reconstructed tracks. An important ingredient, relevant to almost all measurements, is the precise determination of the delivered luminosity with systematic uncertainties below the percent level. This challenging task will be achieved by collecting the information from several detector systems using different and complementary techniques. This document will describe the ongoing ATLAS detector upgrade status and the main results obtained with the prototypes, giving a synthetic view of the whole upgrade project.

Constructing the Teaching Practice of the Xinzhuang Eco Museum through the Concept of Green Infrastructure

<p>天主教輔仁大學自2020年起推動大學社會責任計畫，帶領校內各科系所師生以不同專業共同關懷新莊、五股及泰山等地，持續提升大學對區域發展之社會貢獻，藉此落實深耕地方、人才培育以及環境永續之目標。本文為景觀系教師參與新莊好學環境永續計畫之初步成果。台灣人朗朗上口「一府、二鹿、三艋舺」，卻鮮少聽聞「一府、二鹿、三新莊」，然而清領時期，新莊繁盛之際曾有「千帆林立新莊港，市肆聚千家燈火」之稱譽。根據1935年的登記資料，新莊後村圳灌溉面積超過台北瑠公圳灌溉面積約1/4，然現今瑠公圳廣為人知，後村圳卻少人提及。近年新莊地區因都市重劃工程，使原本農業、工業地景逐漸消逝，取而代之的是都市重劃後的棋盤式街道分區，有形及無形文化資產已逐漸式微。輔大學生對老新莊文化普遍不認識，尤其是出生於新莊區的學生，文化扎根力道微弱。位於塭仔圳重劃區緊鄰輔大的公七與公八之加蓋水路，為超過兩百五十年之清領時期古圳路，大新莊的老農業地景受工業與都市化、重劃填埋。本計畫意識新莊古圳地景具傳承教育意義，更是輔大重要的社會責任，因此推動與新北市政府共同修復經流輔大區域之後村圳，留存於後世，並期望透過輔大USR傳達新莊水文化精神，喚起臺灣人對新莊與水環境之重視。解決方法是藉本大學社會責任計畫帶領學生以綠色基礎設施概念建構新五泰地區之網絡，先期以輔大校園與周邊新闢公園定位為網絡中心，再以重劃區中的道路、行道樹、新舊水路系統作為建構廊道設施的概念，達到文教區永續發展與文化傳承之目標，同時協助新莊區民找回土地認同與價值，藉由塭仔圳重劃區內的舊圳路、公園綠地及溪流藍帶強化綠色基礎設施的功能，建構蘊含豐富文化的｢大新莊生活環境博物園區｣目標。</p> <p> </p><p>Since 2020, Fu Jen Catholic University has been implementing USR initiatives. These initiatives have rallied faculty and students from various departments within the university, harnessing their skills to collaboratively address the issues confronting Xinzhuang, Wugu and Taishan. Through ongoing efforts to contribute to regional development, the university aims to concretely pursue the goals of strengthening our local connections, nurturing talents and promoting environmental sustainability. The essay presents the preliminary outcomes of a professor from the Department of Landscape Architecture participating in the Xinzhuang Sustainable Learning Environment Project. The saying “First-Tainan, Second-Lukang, Third-Monga” is widely recognized in Taiwan, however, the saying “First-Tainan, Second-Lukang, Third- Xinzhuang” is less familiar. In fact, during the Qing Dynasty, Xinzhuang enjoyed prosperity and earned itself the epithet “A thousand ships grace Xinzhuang Port, a thousand lights and lanterns illuminate the marketplace.” Based on registration records from 1935, it’s noted that the irrigation area of the Hòu Cun Zùn (後村圳) in Xinzhuang surpassed that of Taipei’s Liugong Canal by approximately a quarter. However, while the Liugong Canal is widely recognized today, the Hòu Cun Zùn remains relatively obscure. In recent years, urban redevelopment projects in the Xinzhuang area have led to the gradual disappearance of its original agricultural and industrial landscapes, replaced by grid-like street zoning typical of urban renewal projects. As a consequence, both tangible and intangible cultural assets have dwindled. There’s a general lack of awareness among Fu Jen Catholic University students regarding the cultural heritage of Xinzhuang, particularly among those born in the district. This has resulted in a weakened cultural foundation. Situated in the Wenzaijun urban land consolidation redevelopment zone adjacent to Fu Jen University, the covered waterways of Gongqi and Gongba, dating back over 250 years to the Qing Dynasty, have witnessed the transformation of the old agricultural landscape of greater Xinzhuang due to industrialization, urbanization, and urban renewal. Recognizing the educational significance of Xinzhuang ’s ancient waterway landscape, this project views it as a vital university social responsibility of Fu Jen Catholic University. Consequently, it advocates for collaborative restoration efforts with the New Taipei City Government to preserve the historical waterway (Houcunzun) flowing through the Fu Jen University area for future generations. Through the Fu Jen Catholic University Social Responsibility Program, the project aims to impart the spirit of Xinzhuang ’s water culture, thereby raising awareness among the people about the importance of Xinzhuang and its water environment. The proposed solution is to utilize the university’s social responsibility program to lead students in constructing a network for the Xinzhuang-Wugu-Taishan districts based on the concept of green infrastructure. Initially, the Fu Jen University campus and surrounding newly developed parks will be designated as the nexus of the network. Then, the roads, roadside trees, existing and new waterway systems in the redevelopment area will be used to conceptualize the construction of corridor facilities. This approach aims to achieve the goals of sustainable development and cultural heritage preservation in the cultural and educational district. Additionally, the initiative will aid residents of Xinzhuang in reclaiming their local identity and value. By revitalizing old waterways, parks, and streams in the Wenzaizun urban land consolidation redevelopment zone, the project aims to enhance the functionality of green infrastructure and establish the "Greater Xinzhuang Eco-Museum" to showcase the area’s rich cultural heritage.</p> <p> </p>

PENTAGON: Physics-enhanced neural network for volumetric flame chemiluminescence tomography.

This study proposes a physics-enhanced neural network, PENTAGON, as an inference framework for volumetric tomography applications. By leveraging the synergistic combination of data-prior and forward-imaging model, we can accurately predict 3D optical fields, even when the number of projection views decreases to three. PENTAGON is proven to overcome the generalization limitation of data-driven deep learning methods due to data distribution shift, and eliminate distortions introduced by conventional iteration algorithms with limited projections. We evaluated PENTAGON using numerical and experimental results of a flame chemiluminescence tomography example. Results showed that PENTAGON can potentially be generalized for inverse tomography reconstruction problems in many fields.

Automatic localization of anatomical landmarks in head cine fluoroscopy images via deep learning.

Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient-procedure-specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures. To propose a deep learning (DL) approach for the automatic localization of 3D anatomical landmarks on randomly collimated and magnified 2D head fluoroscopy images. The model was developed with datasets comprising 800000 pseudo 2D synthetic images (mixture of vessel-enhanced and non-enhancement), each with 55 annotated anatomical landmarks (two are landmarks for eye lenses), generated from 135 retrospectively collected head computed tomography (CT) volumetric data. Before training, dynamic random cropping was performed to mimic the varied field-size collimation in FGI procedures. Gaussian-distributed additive noise was applied to each individual image to enhance the robustness of the DL model in handling image degradation that may occur during clinical image acquisition in a clinical environment. The model was trained with 629370 synthetic images for approximately 275000 iterations and evaluated against a synthetic image test set and a clinical fluoroscopy test set. The model shows good performance in estimating in- and out-of-image landmark positions and shows feasibility to instantiate the skull shape. The model successfully detected 96.4% and 92.5% 2D and 3D landmarks, respectively, within a 10mm error on synthetic test images. It demonstrated an average of 3.6±2.3mm mean radial error and successfully detected 96.8% 2D landmarks within 10mm error on clinical fluoroscopy images. Our deep-learning model successfully localizes anatomical landmarks and estimates the gross shape of skull structures from collimated 2D projection views. This method may help identify the exposure region required for patient-specific organ dosimetry in FGIs procedures.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model’s capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method’s effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs).

We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released.

Bringing Project Management to life: The value of experiential learning through Virtual tours and engagement with Project professionals

This study explores the significance of experiential learning in project management education by utilising virtual tours and engaging with industry professionals. In collaboration with an external organisation, we developed a virtual tour video that showcases real world project management practices. The video provides a comprehensive view of a major project, featuring insights from more than 40 professionals. Inside the classroom, we combined this virtual tour with direct interactions with project professionals from the organisation. Students gained firsthand perspectives on how to apply theoretical concepts in practice. Feedback indicated that the virtual tour and engagement with professionals provided valuable real-world context, effectively bridging theory, and application. Students appreciated the interactive and well-structured experience. To enhance its impact further, we suggest diversifying perspectives, extending its duration, and striking a balance between theory and practical content.

Development of a 1.0 KVA Fuelless Generator

A 1.0 KVA fuelless generator has been developed. The crave for sustainable energy solution has led to the exploration of emerging technologies targeted at reducing reliance on fossil fuels and reducing environmental impact. One such technology is the development of fuelless generators, which harness renewable energy sources or utilize unconventional mechanisms to generate electricity. This power producing mechanism is aimed to develop a targeted 1KVA power capacity through innovative design strategies. Detailed graphical modeling of the orthographic projection and isometric views brought about enhanced machine components development for stability and reliability. Careful selection of conductor materials and design considerations ensured efficient power transmission, minimizing losses within the system. Integration of power factor correction capacitors and advanced control algorithms contributed to achieving a power factor close to unity, optimizing energy utilization. Comprehensive performance testing validated the functionality and reliability of the developed generator under various load conditions. The development of a fuelless generator with a battery power capacity of 0.85 Kw, torque of 14.48 Nm, resistance of 48.35 ohms, current of 4.55 Amperes and a power factor of 0.85 signifies a significant breakthrough in sustainable energy generation. The performance test showed that that an input response brought about an increase in load (W). The success of developing the generator not only demonstrates the feasibility of clean and sustainable energy solutions but also underscores the potential for further advancements in fuelless generator technology towards a more reviving energy prospect.

Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging.

We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner. We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1cm off isocenter). The acquisition modes were 120kV EID CT, 120kV high-definition (HD) EID CT, and 120kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity. Deep silicon PCD CT had statistically smaller ( P <0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity ( P <0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively. Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Robust residual-guided iterative reconstruction for sparse-view CT in small animal imaging

Objective. We introduce a robust image reconstruction algorithm named residual-guided Golub–Kahan iterative reconstruction technique (RGIRT) designed for sparse-view computed tomography (CT), which aims at high-fidelity image reconstruction from a limited number of projection views. Approach. RGIRT utilizes an inner-outer dual iteration framework, with a flexible least square QR (FLSQR) algorithm implemented in the inner iteration and a restarted iterative scheme applied in the outer iteration. The inner FLSQR employs a flexible Golub–Kahan bidiagonalization method to reduce the size of the inverse problem, and a weighted generalized cross-validation method to adaptively estimate the regularization hyper-parameter. The inner iteration efficiently yields the intermediate reconstruction result, while the outer iteration minimizes the residual and refines the solution by using the result obtained from the inner iteration. Main results. The reconstruction performance of RGIRT is evaluated and compared to other reference methods (FBPConvNet, SART-TV, and FLSQR) using projection data from both numerical phantoms and real experimental Micro-CT data. The experimental findings, from testing various numbers of projection views and different noise levels, underscore the robustness of RGIRT. Meanwhile, theoretical analysis confirms the convergence of residual for our approach. Significance. We propose a robust iterative reconstruction algorithm for x-ray CT scans with sparse views, thereby shortening scanning time and mitigating excessive ionizing radiation exposure to small animals.

Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering.

In the past, guided image filtering (GIF)-based methods often utilized total variation (TV)-based methods to reconstruct guidance images. And they failed to reconstruct the intricate details of complex clinical images accurately. Toaddress these problems, we propose a new sparse-view CT reconstruction method based on group-based sparse representation using weighted guided image filtering. In each iteration of the proposed algorithm, the result constrained by the group-based sparse representation (GSR) is used as the guidance image. Then, the weighted guided image filtering (WGIF) was used to transfer the important features from the guidance image to the reconstruction of the SART method. Three representative slices were tested under 64 projection views, and the proposed method yielded the best visual effect. For the shoulder case, the PSNR can achieve 48.82, which is far superior to other methods. The experimental results demonstrate that our method is more effective in preserving structures, suppressing noise, and reducing artifacts compared to other methods.

Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction

Objective. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the inadequate number of projection views, an analytic reconstruction method utilizing filtered backprojection results in severe streaking artifacts. Recently, deep learning (DL) strategies employing image-domain networks have demonstrated remarkable performance in eliminating the streaking artifact caused by analytic reconstruction methods with sparse projection views. Nevertheless, it is difficult to clarify the theoretical justification for applying DL to sparse view computed tomography (CT) reconstruction, and it has been understood as restoration by removing image artifacts, not reconstruction. Approach. By leveraging the theory of deep convolutional framelets (DCF) and the hierarchical decomposition of measurement, this research reveals the constraints of conventional image and projection-domain DL methodologies, subsequently, the research proposes a novel dual-domain DL framework utilizing hierarchical decomposed measurements. Specifically, the research elucidates how the performance of the projection-domain network can be enhanced through a low-rank property of DCF and a bowtie support of hierarchical decomposed measurement in the Fourier domain. Main results. This study demonstrated performance improvement of the proposed framework based on the low-rank property, resulting in superior reconstruction performance compared to conventional analytic and DL methods. Significance. By providing a theoretically justified DL approach for sparse-view CT reconstruction, this study not only offers a superior alternative to existing methods but also opens new avenues for research in medical imaging. It highlights the potential of dual-domain DL frameworks to achieve high-quality reconstructions with lower radiation doses, thereby advancing the field towards safer and more efficient diagnostic techniques. The code is available at https://github.com/hanyoseob/HDD-DL-for-SVCT.

AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging.

Typical tomographic image reconstruction methods require that the imaged object is static and stationary during the time window to acquire a minimally complete data set. The violation of this requirement leads to temporal-averaging errors in the reconstructed images. For a fixed gantry rotation speed, to reduce the errors, it is desired to reconstruct images using data acquired over a narrower angular range, i.e., with a higher temporal resolution. However, image reconstruction with a narrower angular range violates the data sufficiency condition, resulting in severe data-insufficiency-induced errors. The purpose of this work is to decouple the trade-off between these two types of errors in contrast-enhanced computed tomography (CT) imaging. We demonstrated that using the developed data consistency constrained deep temporal extrapolation method (AIRPORT), the entire time-varying imaged object can be accurately reconstructed with 40 frames-per-second temporal resolution, the time window needed to acquire a single projection view data using a typical C-arm cone-beam CT system. AIRPORT is applicable to general non-sparse imaging tasks using a single short-scan data acquisition.

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies that can help improve the quality of the acquired images for such patients, in terms of the ability to detect myocardial perfusion defects. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. Studies have shown that in MPS, different projection angles usually contain varying amounts of information for the detection task. However, current clinical protocols spend the same time at each projection angle. In this work, we evaluated whether an acquisition protocol that is optimized for each patient could improve performance on the task of defect detection on reconstructed images for patients with outlier anatomical characteristics. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates the theory of ideal observers with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that performance on the task of detecting myocardial perfusion defects is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance on reconstructed images by anthropomorphic observers for patients with outlier anatomical characteristics. Thus, our findings motivate further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities.

Extreme Few-View Tomography without Training Data.

There are fewer than 10 projection views in extreme few-view tomography. The state-of-the-art methods to reconstruct images with few-view data are compressed sensing based. Compressed sensing relies on a sparsification transformation and total variation (TV) norm minimization. However, for the extreme few-view tomography, the compressed sensing methods are not powerful enough. This paper seeks additional information as extra constraints so that extreme few-view tomography becomes possible. In transmission tomography, we roughly know the linear attenuation coefficients of the objects to be imaged. We can use these values as extra constraints. Computer simulations show that these extra constraints are helpful and improve the reconstruction quality.

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography.

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches. We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm. DE CBCT projections of quantitative BME phantoms (water containers 100-165mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60kV low energy (LE) beam and a 120kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160mm) was investigated. The MC-based scatter correction delivered>69% reduction of cupping artifacts for moderate to wide collimations (>80mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160mm beam collimations. The variability with respect to object position in the FOV was modest (<15% coefficient of variation). The areas under the ROC curve were>0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema. Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

The Atlas of the Inferior Mesenteric Artery and Vein under Maximum-Intensity Projection and Three-Dimensional Reconstruction View

(1) Background: Understanding vascular patterns is crucial for minimizing bleeding and operating time in colorectal surgeries. This study aimed to develop an anatomical atlas of the inferior mesenteric artery (IMA) and vein (IMV). (2) Methods: A total of 521 patients with left-sided colorectal cancer were included. IMA and IMV patterns were identified using maximum-intensity projection (MIP) and three-dimensional (3D) reconstruction techniques. The accuracy of these techniques was assessed by comparing them with surgical videos. We compared the amount of bleeding and operating time for IMA ligation across different IMA types. (3) Results: Most patients (45.7%) were classified as type I IMA, followed by type II (20.7%), type III (22.6%), and type IV (3.5%). Newly identified type V and type VI patterns were found in 6.5% and 1% of patients, respectively. Of the IMVs, 49.9% drained into the superior mesenteric vein (SMV), 38.4% drained into the splenic vein (SPV), 9.4% drained into the SMV–SPV junction, and only 2.3% drained into the first jejunal vein (J1V). Above the root of the left colic artery (LCA), 13.1% of IMVs had no branches, 50.1% had one, 30.1% had two, and 6.7% had three or more branches. Two patients had two main IMV branches, and ten had IMVs at the edge of the mesocolon with small branches. At the IMA root, 37.2% of LCAs overlapped with the IMV, with 34.0% being lateral, 16.9% distal, 8.7% medial, and both the marginal type of IMV and the persistent descending mesocolon (PDM) type represented 1.4%. MIP had an accuracy of 98.43%, and 3D reconstruction had an accuracy of 100%. Blood loss and operating time were significantly higher in the complex group compared to the simple group for IMA ligation (p &lt; 0.001). (4) Conclusions: A comprehensive anatomical atlas of the IMA and IMV was provided. Complex IMA patterns were associated with increased bleeding and operating time.