Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography.
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ABSTRACT:
SUBMITTER: Papoutsellis E
PROVIDER: S-EPMC8255950 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
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Project description:Currently, a number of examination and imaging techniques exist for diagnosing posterolateral rotatory instability of the elbow. Although the posterolateral rotatory drawer is the primary examination maneuver, other special tests include the lateral pivot-shift test, prone push-up test, chair push-up test, and tabletop push-up test. In addition, posterolateral rotatory instability can be evaluated using radiography, magnetic resonance imaging, dynamic fluoroscopy, or dynamic ultrasound. In this Technical Note, each of these tests is described in detail. Video instruction is also provided to further explain the techniques and provide examples of positive tests.
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Project description:MotivationDriven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets.ResultsWe propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA's tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools.Availability and implementationHOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.
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Project description:BackgroundComputed tomography angiography (CTA) is the recommended diagnostic and follow-up imaging modality for acute aortic dissection (AD). However, the high-contrast medium burden associated with repeated CT aortography follow-ups remains a significant concern. This prospective study aimed to assess whether an ultra-low contrast dose (75% cutoff) aortic CTA protocol on dual-layer spectral CT could achieve comparable image quality with the full dose protocol. We also investigated the image quality of the virtual noncontrast (VNC) images derived from the ultra-low dose protocol.MethodsThis study included 37 consecutive patients who were referred to aortic CTA from May 2022 to August 2022. The enrolled patients underwent full-dose contrast CTA and ultra-low dose (reduced to 25% of conventional) contrast CTA on dual-layer spectral CT in 1 day. Virtual monochromatic images (VMIs) were reconstructed with 40 and 70 keV. The VNC images were reconstructed for both protocols. Objective image quality evaluation, recorded as signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs), was compared between the groups using 1-way analysis of variance and post hoc analysis with Bonferroni correction. Subjective image quality was also compared between the groups. Finally, VNC images derived from the low-dose (VNClow) and full-dose (VNCfull) protocols were compared to the true noncontrast (TNC) images.ResultsNeither CNR nor SNR was lower for the 40-keV images reconstructed from the ultra-low dose group compared to the conventional images. Both were significantly higher than those of the 70-keV images. Regarding subjective image quality, vessel enhancement was not significantly different between the 40-keV VMI and full-dose images [ascending aorta (AAO): 4.37±0.46 vs. 4.57±0.48, P=0.096; brachiocephalic arteries: 4.34±0.45 vs. 4.51±0.49, P=0.152; abdominal aortic side branch: 4.42±0.48 vs. 4.51±0.49, P=0.480]. The VNClow images were similar to the TNC images but significantly different from the VNCfull images (P<0.001).ConclusionsUltra-low contrast aortic CTA with a 75%-reduced iodine dose using dual-layer spectral CT and the derived VNC achieved image quality comparable to that of conventional CTA and TNC images.
| S-EPMC10585532 | biostudies-literature
Project description:We present an ultrahigh-resolution spectral domain optical coherence tomography (OCT) system in 800 nm with a low-noise supercontinuum source (SC) optimized for myocardial imaging. The system was demonstrated to have an axial resolution of 2.72???m with a large imaging depth of 1.78 mm and a 6-dB falloff range of 0.89 mm. The lateral resolution (5.52???m) was compromised to enhance the image penetration required for myocardial imaging. The noise of the SC source was analyzed extensively and an imaging protocol was proposed for SC-based OCT imaging with appreciable contrast. Three-dimensional datasets were acquired ex vivo on the endocardium side of tissue specimens from different chambers of fresh human and swine hearts. With the increased resolution and contrast, features such as elastic fibers, Purkinje fibers, and collagen fiber bundles were observed. The correlation between the structural information revealed in the OCT images and tissue pathology was discussed as well.
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Project description:PurposeCompare side-to-side differences for knee kinematics between anatomic single-bundle (SB) and anatomic double-bundle (DB) ACLR during downhill running at 6 and 24 months post ACLR using high-accuracy dynamic stereo X-ray imaging. It was hypothesized that anatomic DB ACLR would better restore tibio-femoral kinematics compared to SB ACLR, based on comparison to the contralateral, uninjured knee.MethodsActive individuals between 14 and 50 years of age that presented within 12 months of injury were eligible to participate. Individuals with prior injury or surgery of either knee, greater than a grade 1 concomitant knee ligament injury, or ACL insertion sites less than 14 mm or greater than 18 mm were excluded. Subjects were randomized to undergo SB or DB ACLR with a 10 mm-wide quadriceps tendon autograft harvested with a patellar bone block and were followed for 24 months. Dynamic knee function was assessed during treadmill downhill running using a dynamic stereo X-ray tracking system at 6 and 24 months after surgery. Three-dimensional tibio-femoral kinematics were calculated and compared between limbs (ACLR and uninjured contralateral) at each time point.ResultsFifty-seven subjects were randomized (29 DB) and 2-year follow-up was attained from 51 (89.5%). No significant differences were found between SB and DB anatomic ACLR for any of the primary kinematic variables.ConclusionsContrary to the study hypothesis, double-bundle reconstruction did not show superior kinematic outcomes compared to the single-bundle ACLR. While neither procedure fully restored normal knee kinematics, both anatomic reconstructions were similarly effective for restoring near-normal dynamic knee function. The findings of this study indicate both SB and DB techniques can be used for patients with average size ACL insertion sites.Level of evidenceLevel I.
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Project description:BackgroundMulti-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension.MethodsIn this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity.ResultsNumerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s.ConclusionsTo further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.
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Project description:IMPORTANCE Evidence is mounting that achromatopsia is a progressive retinal degeneration, and treatments for this condition are on the horizon. OBJECTIVES To categorize achromatopsia into clinically identifiable stages using spectral-domain optical coherence tomography and to describe fundus autofluorescence imaging in this condition. DESIGN, SETTING, AND PARTICIPANTS A prospective observational study was performed between 2010 and 2012 at the Edward S. Harkness Eye Institute, New York-Presbyterian Hospital. Participants included 17 patients (aged 10-62 years) with full-field electroretinography-confirmed achromatopsia. MAIN OUTCOMES AND MEASURES Spectral-domain optical coherence tomography features and staging system, fundus autofluorescence and near-infrared reflectance features and their correlation to optical coherence tomography, and genetic mutations served as the outcomes and measures. RESULTS Achromatopsia was categorized into 5 stages on spectral-domain optical coherence tomography: stage 1 (2 patients [12%]), intact outer retina; stage 2 (2 patients [12%]), inner segment ellipsoid line disruption; stage 3 (5 patients [29%]), presence of an optically empty space; stage 4 (5 patients [29%]), optically empty space with partial retinal pigment epithelium disruption; and stage 5 (3 patients [18%]), complete retinal pigment epithelium disruption and/or loss of the outer nuclear layer. Stage 1 patients showed isolated hyperreflectivity of the external limiting membrane in the fovea, and the external limiting membrane was hyperreflective above each optically empty space. On near infrared reflectance imaging, the fovea was normal, hyporeflective, or showed both hyporeflective and hyperreflective features. All patients demonstrated autofluorescence abnormalities in the fovea and/or parafovea: 9 participants (53%) had reduced or absent autofluorescence surrounded by increased autofluorescence, 4 individuals (24%) showed only reduced or absent autofluorescence, 3 patients (18%) displayed only increased autofluorescence, and 1 individual (6%) exhibited decreased macular pigment contrast. Inner segment ellipsoid line loss generally correlated with the area of reduced autofluorescence, but hyperautofluorescence extended into this region in 2 patients (12%). Bilateral coloboma-like atrophic macular lesions were observed in 1 patient (6%). Five novel mutations were identified (4 in the CNGA3 gene and 1 in the CNGB3 gene). CONCLUSIONS AND RELEVANCE Achromatopsia often demonstrates hyperautofluorescence suggestive of progressive retinal degeneration. The proposed staging system facilitates classification of the disease into different phases of progression and may have therapeutic implications.
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Project description:Optical coherence tomography angiography relies on motion for contrast and requires at least two data acquisitions per pointwise scanning location. We present a method termed spectral contrast optical coherence tomography angiography using visible light that relies on the spectral signatures of blood for angiography from a single scan using endogenous contrast. We demonstrate the molecular sensitivity of this method, which enables lymphatic vessel, blood, and tissue discrimination.
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Project description:Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.
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