Medical physics最新文献

{"title":"Protocol optimization for functional cardiac CT imaging using noise emulation in the raw data domain.","authors":"Zhye Yin, Pengwei Wu, Ashish Manohar, Elliot R McVeigh, Jed D Pack","doi":"10.1002/mp.17088","DOIUrl":"10.1002/mp.17088","url":null,"abstract":"<p><strong>Background: </strong>Four-dimensional (4D) wide coverage computed tomography (CT) is an effective imaging modality for measuring the mechanical function of the myocardium. However, repeated CT measurement across a number of heartbeats is still a concern.</p><p><strong>Purpose: </strong>A projection-domain noise emulation method is presented to generate accurate low-dose (mA modulated) 4D cardiac CT scans from high-dose scans, enabling protocol optimization to deliver sufficient image quality for functional cardiac analysis while using a dose level that is as low as reasonably achievable (ALARA).</p><p><strong>Methods: </strong>Given a targeted low-dose mA modulation curve, the proposed noise emulation method injects both quantum and electronic noise of proper magnitude and correlation to the high-dose data in projection domain. A spatially varying (i.e., channel-dependent) detector gain term as well as its calibration method were proposed to further improve the noise emulation accuracy. To determine the ALARA dose threshold, a straightforward projection domain image quality (IQ) metric was proposed that is based on the number of projection rays that do not fall under the non-linear region of the detector response. Experiments were performed to validate the noise emulation method with both phantom and clinical data in terms of visual similarity, contrast-to-noise ratio (CNR), and noise-power spectrum (NPS).</p><p><strong>Results: </strong>For both phantom and clinical data, the low-dose emulated images exhibited similar noise magnitude (CNR difference within 2%), artifacts, and texture to that of the real low-dose images. The proposed channel-dependent detector gain term resulted in additional increase in emulation accuracy. Using the proposed IQ metric, recommended kVp and mA settings were calculated for low dose 4D Cardiac CT acquisitions for patients of different sizes.</p><p><strong>Conclusions: </strong>A detailed method to estimate system-dependent parameters for a raw-data based low dose emulation framework was described. The method produced realistic noise levels, artifacts, and texture with phantom and clinical studies. The proposed low-dose emulation method can be used to prospectively select patient-specific minimal-dose protocols for functional cardiac CT.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":"4622-4634"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11547861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of pencil ionization chamber with temporal readout capabilities to measure CT beam full width half maximum.","authors":"Muhannad N Fadhel, Kevin Grizzard, Daniel Vergara, Roberto Perez Franco, Anzi Zhao, Matthew Hoerner","doi":"10.1002/mp.17084","DOIUrl":"10.1002/mp.17084","url":null,"abstract":"<p><strong>Background: </strong>Measurement of Computed Tomography (CT) beam width is required by accrediting and regulating bodies for routine physics evaluations due to its direct correlation to patient dose. Current methods for performing CT beam width measurement require special hardware, software, and/or consumable films. Today, most 100-mm pencil chambers with a digital interface used to evaluate Computed Tomography Dose Index (CTDI_vol) have a sufficiently high sampling rate to reconstruct a high-resolution dose profile for any acquisition mode.</p><p><strong>Purpose: </strong>The goal of this study is to measure the CT beam width from the sampled dose profile under a single helical acquisition with the 100-mm pencil chamber used for CTDI_vol measurements.</p><p><strong>Methods: </strong>The dose profiles for different scanners were measured for helical scans with varying collimation settings using a 100-mm pencil chamber placed at the isocenter and co-moving with the patient table. The measured dose profiles from the 100-mm pencil chamber were corrected for table attenuation by extracting a periodic correction function (PCF) to eliminate table interference. The corrected dose profiles were then deconvolved with the response function of the chamber to compute the beam profile. The beam width was defined by the full width half maximum (FWHM) of the resulting beam profile. Reference dose profiles were also measured using Gafchromic film for comparison.</p><p><strong>Results: </strong>The beam widths, estimated using the innovative deconvolution method from the 100-mm pencil chamber, exhibit an average percentage difference of 1.6 ± 1.8 when compared with measurements obtained through Gafchromic film for beam width assessment.</p><p><strong>Conclusion: </strong>The proposed approach to deconvolve the pencil chamber response demonstrates the potential of obtaining the CT beam width at high accuracy without the need of special hardware, software, or consumable films. This technique can improve workflow for routine performance evaluation of CT systems.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":"4687-4695"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effects of intra-detector Compton scatter on low-frequency DQE for photon-counting CT using edge-on-irradiated silicon detectors.","authors":"Fredrik Grönberg, Zhye Yin, Jonathan S Maltz, Norbert J Pelc, Mats Persson","doi":"10.1002/mp.17122","DOIUrl":"10.1002/mp.17122","url":null,"abstract":"<p><strong>Background: </strong>Edge-on-irradiated silicon detectors are currently being investigated for use in full-body photon-counting computed tomography (CT) applications. The low atomic number of silicon leads to a significant number of incident photons being Compton scattered in the detector, depositing a part of their energy and potentially being counted multiple times. Even though the physics of Compton scatter is well established, the effects of Compton interactions in the detector on image quality for an edge-on-irradiated silicon detector have still not been thoroughly investigated.</p><p><strong>Purpose: </strong>To investigate and explain effects of Compton scatter on low-frequency detective quantum efficiency (DQE) for photon-counting CT using edge-on-irradiated silicon detectors.</p><p><strong>Methods: </strong>We extend an existing Monte Carlo model of an edge-on-irradiated silicon detector with 60 mm active absorption depth, previously used to evaluate spatial-frequency-based performance, to develop projection and image domain performance metrics for pure density and pure spectral imaging tasks with 30 and 40 cm water backgrounds. We show that the lowest energy threshold of the detector can be used as an effective discriminator of primary counts and cross-talk caused by Compton scatter. We study the developed metrics as functions of the lowest threshold energy for root-mean-square electronic noise levels of 0.8, 1.6, and 3.2 keV, where the intermediate level 1.6 keV corresponds to the noise level previously measured on a single sensor element in isolation. We also compare the performance of a modeled detector with 8, 4, and 2 optimized energy bins to a detector with 1-keV-wide bins.</p><p><strong>Results: </strong>In terms of low-frequency DQE for density imaging, there is a tradeoff between using a threshold low enough to capture Compton interactions and avoiding electronic noise counts. For 30 cm water phantom, 4 energy bins, and a root-mean-square electronic noise of 0.8, 1.6, and 3.2 keV, it is optimal to put the lowest energy threshold at 3, 6, and 1 keV, which gives optimal projection-domain DQEs of 0.64, 0.59, and 0.52, respectively. Low-frequency DQE for spectral imaging also benefits from measuring Compton interactions with respective optimal thresholds of 12, 12, and 13 keV. No large dependence on background thickness was observed. For the intermediate noise level (1.6 keV), increasing the lowest threshold from 5 to 35 keV increases the variance in a iodine basis image by 60%-62% (30 cm phantom) and 67%-69% (40 cm phantom), with 8 bins. Both spectral and density DQE are adversely affected by increasing the electronic noise level. Image-domain DQE exhibits similar qualitative behavior as projection-domain DQE.</p><p><strong>Conclusions: </strong>Compton interactions contribute significantly to the density imaging performance of edge-on-irradiated silicon detectors. With the studied detector topology, the benefit of cou","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":"4948-4969"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Points of interest linear attention network for real-time non-rigid liver volume to surface registration.","authors":"Zeming Chen, Beiji Zou, Xiaoyan Kui, Yangyang Shi, Ding Lv, Liming Chen","doi":"10.1002/mp.17108","DOIUrl":"https://doi.org/10.1002/mp.17108","url":null,"abstract":"<p><strong>Background: </strong>In laparoscopic liver surgery, accurately predicting the displacement of key intrahepatic anatomical structures is crucial for informing the doctor's intraoperative decision-making. However, due to the constrained surgical perspective, only a partial surface of the liver is typically visible. Consequently, the utilization of non-rigid volume to surface registration methods becomes essential. But traditional registration methods lack the necessary accuracy and cannot meet real-time requirements.</p><p><strong>Purpose: </strong>To achieve high-precision liver registration with only partial surface information and estimate the displacement of internal liver tissues in real-time.</p><p><strong>Methods: </strong>We propose a novel neural network architecture tailored for real-time non-rigid liver volume to surface registration. The network utilizes a voxel-based method, integrating sparse convolution with the newly proposed points of interest (POI) linear attention module. POI linear attention module specifically calculates attention on the previously extracted POI. Additionally, we identified the most suitable normalization method RMSINorm.</p><p><strong>Results: </strong>We evaluated our proposed network and other networks on a dataset generated from real liver models and two real datasets. Our method achieves an average error of 4.23 mm and a mean frame rate of 65.4 fps in the generation dataset. It also achieves an average error of 8.29 mm in the human breathing motion dataset.</p><p><strong>Conclusions: </strong>Our network outperforms CNN-based networks and other attention networks in terms of accuracy and inference speed.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.","authors":"Yeyu Cai, Jiayi Liu, HaiTao Yang, Liyun Zheng, Dongmei Wu, Enhua Xiao, Yongming Dai","doi":"10.1002/mp.17126","DOIUrl":"https://doi.org/10.1002/mp.17126","url":null,"abstract":"<p><strong>Background: </strong>Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).</p><p><strong>Purpose: </strong>To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.</p><p><strong>Methods: </strong>Thirty mice with carbon tetrachloride (CCl₄)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm²) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF.</p><p><strong>Results: </strong>According to BIC results, a 4-compartment RSI model (RSI₄) with optimal ADCs of 0.471 × 10^-3, 1.653 × 10^-3, 9.487 × 10^-3, and > 30 × 10^-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C₁ and C₃ compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI₄-C₃ was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI₄-C₃ value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).</p><p><strong>Conclusion: </strong>The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI₄-C₃ showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI₄-C₃ values further improved the discrimination performance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast four-dimensional cone-beam computed tomography reconstruction using deformable convolutional networks.","authors":"Zhuoran Jiang,&nbsp;Yushi Chang,&nbsp;Zeyu Zhang,&nbsp;Fang-Fang Yin,&nbsp;Lei Ren","doi":"10.1002/mp.15806","DOIUrl":"https://doi.org/10.1002/mp.15806","url":null,"abstract":"<p><strong>Background: </strong>Although four-dimensional cone-beam computed tomography (4D-CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D-CBCT scanning time while maintaining high-quality images. Current motion-compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling.</p><p><strong>Purpose: </strong>In this work, we aim to propose an alternative feature-compensated method to realize the fast 4D-CBCT with high-quality images.</p><p><strong>Methods: </strong>We proposed a feature-compensated deformable convolutional network (FeaCo-DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo-DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high-quality images of the target phase. The proposed FeaCo-DCN was evaluated using lung cancer patient data.</p><p><strong>Results: </strong>(1) FeaCo-DCN generated high-quality images with accurate and clear structures for a fast 4D-CBCT scan; (2) 4D-CBCT images reconstructed by FeaCo-DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo-DCN achieved superior performance by all metrics compared to the top-ranked techniques in the AAPM SPARE Challenge.</p><p><strong>Conclusion: </strong>The proposed FeaCo-DCN is effective and efficient in reconstructing 4D-CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image-guided radiotherapy.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"49 10","pages":"6461-6476"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9588592/pdf/nihms-1817259.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41176513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of contouring errors using convolutional neural networks.","authors":"Dong Joo Rhee,&nbsp;Carlos E Cardenas,&nbsp;Hesham Elhalawani,&nbsp;Rachel McCarroll,&nbsp;Lifei Zhang,&nbsp;Jinzhong Yang,&nbsp;Adam S Garden,&nbsp;Christine B Peterson,&nbsp;Beth M Beadle,&nbsp;Laurence E Court","doi":"10.1002/mp.13814","DOIUrl":"https://doi.org/10.1002/mp.13814","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.</p><p><strong>Methods: </strong>An autocontouring tool based on convolutional neural networks (CNN) was developed for 16 normal structures of the head and neck and tested to identify the contour errors from a clinically validated multiatlas-based autocontouring system (MACS). The computed tomography (CT) scans and clinical contours from 3495 patients were semiautomatically curated and used to train and validate the CNN-based autocontouring tool. The final accuracy of the tool was evaluated by calculating the Sørensen-Dice similarity coefficients (DSC) and Hausdorff distances between the automatically generated contours and physician-drawn contours on 174 internal and 24 external CT scans. Lastly, the CNN-based tool was evaluated on 60 patients' CT scans to investigate the possibility to detect contouring failures. The contouring failures on these patients were classified as either minor or major errors. The criteria to detect contouring errors were determined by analyzing the DSC between the CNN- and MACS-based contours under two independent scenarios: (a) contours with minor errors are clinically acceptable and (b) contours with minor errors are clinically unacceptable.</p><p><strong>Results: </strong>The average DSC and Hausdorff distance of our CNN-based tool was 98.4%/1.23 cm for brain, 89.1%/0.42 cm for eyes, 86.8%/1.28 cm for mandible, 86.4%/0.88 cm for brainstem, 83.4%/0.71 cm for spinal cord, 82.7%/1.37 cm for parotids, 80.7%/1.08 cm for esophagus, 71.7%/0.39 cm for lenses, 68.6%/0.72 for optic nerves, 66.4%/0.46 cm for cochleas, and 40.7%/0.96 cm for optic chiasm. With the error detection tool, the proportions of the clinically unacceptable MACS contours that were correctly detected were 0.99/0.80 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively. The proportions of the clinically acceptable MACS contours that were correctly detected were 0.81/0.60 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively.</p><p><strong>Conclusion: </strong>Our CNN-based autocontouring tool performed well on both the publically available and the internal datasets. Furthermore, our results show that CNN-based algorithms are able to identify ill-defined contours from a clinically validated and used multiatlas-based autocontouring tool. Therefore, our CNN-based tool can effectively perform automatic verification of MACS contours.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"46 11","pages":"5086-5097"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/mp.13814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intercomparison of MR-informed PET image reconstruction methods.","authors":"James Bland, Abolfazl Mehranian, Martin A Belzunce, Sam Ellis, Casper da Costa-Luis, Colm J McGinnity, Alexander Hammers, Andrew J Reader","doi":"10.1002/mp.13812","DOIUrl":"10.1002/mp.13812","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [&lt;sup&gt;18&lt;/sup&gt; F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [&lt;sup&gt;18&lt;/sup&gt; F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the ot","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"46 11","pages":"5055-5074"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation of dental cone-beam CT scans affected by metal artifacts using a mixed-scale dense convolutional neural network.","authors":"Jordi Minnema,&nbsp;Maureen van Eijnatten,&nbsp;Allard A Hendriksen,&nbsp;Niels Liberton,&nbsp;Daniël M Pelt,&nbsp;Kees Joost Batenburg,&nbsp;Tymour Forouzanfar,&nbsp;Jan Wolff","doi":"10.1002/mp.13793","DOIUrl":"https://doi.org/10.1002/mp.13793","url":null,"abstract":"<p><strong>Purpose: </strong>In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this study aimed to develop a mixed-scale dense convolutional neural network (MS-D network) for bone segmentation in CBCT scans affected by metal artifacts.</p><p><strong>Method: </strong>Training data were acquired from 20 dental CBCT scans affected by metal artifacts. An experienced medical engineer segmented the bony structures in all CBCT scans using global thresholding and manually removed all remaining noise and metal artifacts. The resulting gold standard segmentations were used to train an MS-D network comprising 100 convolutional layers using far fewer trainable parameters than alternative convolutional neural network (CNN) architectures. The bone segmentation performance of the MS-D network was evaluated using a leave-2-out scheme and compared with a clinical snake evolution algorithm and two state-of-the-art CNN architectures (U-Net and ResNet). All segmented CBCT scans were subsequently converted into standard tessellation language (STL) models and geometrically compared with the gold standard.</p><p><strong>Results: </strong>CBCT scans segmented using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean Dice similarity coefficients of 0.87 ± 0.06, 0.87 ± 0.07, 0.86 ± 0.05, and 0.78 ± 0.07, respectively. The STL models acquired using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean absolute deviations of 0.44 mm ± 0.13 mm, 0.43 mm ± 0.16 mm, 0.40 mm ± 0.12 mm and 0.57 mm ± 0.22 mm, respectively. In contrast to the MS-D network, the ResNet introduced wave-like artifacts in the STL models, whereas the U-Net incorrectly labeled background voxels as bone around the vertebrae in 4 of the 9 CBCT scans containing vertebrae.</p><p><strong>Conclusion: </strong>The MS-D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"46 11","pages":"5027-5035"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/mp.13793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical Note: PYRO-NN: Python reconstruction operators in neural networks.","authors":"Christopher Syben,&nbsp;Markus Michen,&nbsp;Bernhard Stimpel,&nbsp;Stephan Seitz,&nbsp;Stefan Ploner,&nbsp;Andreas K Maier","doi":"10.1002/mp.13753","DOIUrl":"https://doi.org/10.1002/mp.13753","url":null,"abstract":"<p><strong>Purpose: </strong>Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems.</p><p><strong>Methods: </strong>PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems.</p><p><strong>Results: </strong>The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at https://github.com/csyben/PYRO-NN.</p><p><strong>Conclusions: </strong>PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":"46 11","pages":"5110-5115"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/mp.13753","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49686811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}