Voxelwise analysis of diffusion MRI of cervical spinal cord using tract-based spatial statistics

Robust voxelwise analysis using tract-based spatial statistics (TBSS) together with permutation statistical method is standardly used in analyzing diffusion tensor imaging (DTI) of brain. A similar analytical method could be useful when studying DTI of cervical spinal cord.Based on anatomical data of sixty-four healthy volunteers, white (WM) and gray matter (GM) masks were created and subsequently registered into DTI space. Using TBSS, two skeleton types were created (single line and dilated for WM as well as GM). From anatomical data, percentage rates of overlap were calculated for all skeletons in relation to WM and GM masks.Voxelwise analysis of fractional anisotropy values depending on age and sex was conducted. Correlation of fraction anisotropy values with age of subjects was also evaluated. The two WM skeleton types showed a high overlap rate with WM masks (~94%); GM skeletons showed lower rates (56% and 42%, respectively, for single line and dilated). WM and GM areas where fraction anisotropy values differ between sexes were identified (p < .05). Furthermore, using voxelwise analysis such WM voxels were identified where fraction anisotropy values differ depending on age (p < .05) and in these voxels linear dependence of fraction anisotropy and age (r = −0.57, p < .001) was confirmed by regression analysis. This dependence was not proven when using WM anatomical masks (r = −0.21, p = .10).The analytical approach presented shown to be useful for group analysis of DTI data for cervical spinal cord.     

On the down-sampling of diffusion MRI data along the angular dimension

BackgroundIt has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties.Materials and methodsHere we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices.ResultsOur experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index.ConclusionsThese results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.     

Development of hedge operator based fuzzy divergence measure and its application in segmentation of chronic myelogenous leukocytes from microscopic image of peripheral blood smear

This paper introduces a hedge operator based fuzzy divergence measure and its application in segmentation of leukocytes in case of chronic myelogenous leukemia using light microscopic images of peripheral blood smears. The concept of modified discrimination measure is applied to develop the measure of divergence based on Shannon exponential entropy and Yager's measure of entropy. These two measures of divergence are compared with the existing literatures and validated by ground truth images. Finally, it is found that hedge operator based divergence measure using Yager's entropy achieves better segmentation accuracy i.e., 98.29% for normal and 98.15% for chronic myelogenous leukocytes. Furthermore, Jaccard index has been performed to compare the segmented image with ground truth ones where it is found that that the proposed scheme leads to higher Jaccard index (0.39 for normal, 0.24 for chronic myelogenous leukemia).     

Deep learning for radial SMS myocardial perfusion reconstruction using the 3D residual booster U-net

PurposeTo develop an end-to-end deep learning solution for quickly reconstructing radial simultaneous multi-slice (SMS) myocardial perfusion datasets with comparable quality to the pixel tracking spatiotemporal constrained reconstruction (PT-STCR) method.MethodsDynamic contrast enhanced (DCE) radial SMS myocardial perfusion data were obtained from 20 subjects who were scanned at rest and/or stress with or without ECG gating using a saturation recovery radial CAIPI turboFLASH sequence. Input to the networks consisted of complex coil combined images reconstructed using the inverse Fourier transform of undersampled radial SMS k-space data. Ground truth images were reconstructed using the PT-STCR pipeline. The performance of the residual booster 3D U-Net was tested by comparing it to state-of-the-art network architectures including MoDL, CRNN-MRI, and other U-Net variants.ResultsResults demonstrate significant improvements in speed requiring approximately 8 seconds to reconstruct one radial SMS dataset which is approximately 200 times faster than the PT-STCR method. Images reconstructed with the residual booster 3D U-Net retain quality of ground truth PT-STCR images (0.963 SSIM/40.238 PSNR/0.147 NRMSE). The residual booster 3D U-Net has superior performance compared to existing network architectures in terms of image quality, temporal dynamics, and reconstruction time.ConclusionResidual and booster learning combined with the 3D U-Net architecture was shown to be an effective network for reconstructing high-quality images from undersampled radial SMS datasets while bypassing the reconstruction time of the PT-STCR method.     

Determination of edge correspondence using color codes for one-shot shape acquisition

A robust method for measuring 3D shapes is proposed, in which only one stripe pattern image is required. To determine edge correspondence, we match color codes instead of edge codes because the former are more stable and immune to the standard deviation of the Gaussian filter in edge detection and width of the color band. The color code is identified by K-means. This method exhibits huge advantages in adaptability and automation over thresholding techniques. The proposed decoding method is compared with two well-known algorithms, dynamic programming and multi-pass dynamic programming. Using ground truth, we evaluate the performance of the methods in measuring three different objects. Quantitative and qualitative comparisons are shown in the experiments, and results affirm that our method is effective and robust.     

A Monte Carlo simulation of image misalignment effects in diffusion tensor imaging

Diffusion tensor imaging (DTI) and tractography are noninvasive MRI methods, providing an insight on microscopic structural information of anisotropic tissues in vivo. The success of this technique stems on a watchful choice of imaging parameters and post-acquisition reconstruction. In the present work, we have focused on the problem of residual linear image misalignment in the DTI data and its effects on the parameters of the diffusion tensor and fiber tracking in human brain. We demonstrate substantial sensitivity of the reconstructed diffusion tensor and fiber tractography on increasing amplitude of artificially induced random image misalignment in the DTI. We show that already a submillimeter image misalignment in the DTI is an important source of error, which may potentially mask pathological presentations of the diseases and may partially explain variations in the results obtained from the DTI. Finally, we evaluated four implementations of image registrations and demonstrate their variable performance. This further supports the fact that a robust image registration must be performed to ensure reliable and reproducible diffusion tensor mapping and reconstruction of white matter (WM) fibers.     

A combined DTI and structural MRI study in medicated-naïve chronic schizophrenia

Disconnection in white matter (WM) pathway and alterations in gray matter (GM) structure have been hypothesized as pathogenesis in schizophrenia. However, the relationship between the abnormal WM integrity and the alteration of GM in anatomically connected areas remains uncertain. Moreover, the potential influence of antipsychotic medication on WM anisotropy and cortical morphology was not excluded in previous studies. In this study, a total number of 34 subjects were enrolled, including 17 medicated-naïve chronic schizophrenia patients and 17 healthy controls. Tract-based spatial statistics (TBSS) were applied to investigate the level of WM integrity. The FreeSurfer surface-based analysis was used to determine GM volume, cortical thickness and the surface area of GM regions which corresponded to abnormal WM fiber tracts. We observed that patients possessed lower fractional anisotropy (FA) values in the left inferior fronto-occipital fasciculus (IFOF) and left inferior longitudinal fasciculus (ILF), along with smaller GM volume and cortical thinning in temporal lobe than the healthy controls, which reflected the underlying WM and GM disruption that contributed to the disease. In the patient population, the lower connectivity of ILF and IFOF was positively associated with cortical thickness in left lateral orbitofrontal cortex, superior temporal gyrus and lingual gyrus in males, and positively correlated with GM volume in left lateral orbitofrontal cortex in females. On the other hand, it was negatively correlated with cortical area of middle temporal gyrus in males and temporal pole in females respectively, but not when genders were combined. These findings suggested that abnormal WM integrity and anatomical correspondence of GM alterations in schizophrenia were interdependent on gender-separated analysis in patients with schizophrenia. Moreover, combining TBSS and FreeSurfer might be a useful method to provide significant insight into interacting processes related to WM fiber tracts and GM changes in schizophrenia.     

The correlation of fractional anisotropy parameters with Ki-67 index,and the clinical implication in grading of non-enhancing gliomas and neuronal-glial tumors

PurposeTo investigate the correlation between the FA parameters and Ki-67 labeling index, and their diagnostic performance in grading supratentorial non-enhancing gliomas and neuronal-glial tumors (GNGT).MethodsThis institutional review board-approved, Health Insurance Portability and Accountability (HIPAA) compliant retrospective study enrolled 35 patients, including 19 with low grade GNGT and 16 with high grade GNGT. The mean FA, maximal FA and mean maximal FA values derived from diffusion tensor imaging were measured. The correlation between the FA parameters and the Ki-67 labeling index was assessed by Spearman rank test. The receiver operating characteristic curve analysis and multivariate logistic regression analysis were performed to detect the optimal imaging parameters in grading GNGT.ResultsThe three FA parameters of low grade GNGT were significantly lower than the high grade GNGT (p < 0.001). The mean FA, maximal FA and mean maximal FA had significant positive correlation with Ki-67 labeling index (p = 0.001, p < 0.001, p < 0.001 respectively). The maximal FA showed a higher sensitivity and specificity in grading of non-enhancing GNGT with specificity of 78.9%, sensitivity of 100.0%, respectively.ConclusionsThe FA parameters correlated with Ki-67 labeling index, and were useful surrogates in preoperative grading supratentorial non-enhancing GNGT.     

Keyhole and zero-padding approaches for reduced-encoding diffusion tensor imaging of the mouse brains

Keyhole diffusion tensor imaging (keyhole DTI) was previously proposed in cardiac imaging to reconstruct DTI maps from the reduced phase-encoding images. To evaluate the feasibility of keyhole DTI in brain imaging, keyhole and zero-padding DTI algorithms were employed on in vivo mouse brain. The reduced phase-encoding portion, also termed as the sharing rate, was varied from 50% to 90% of the full k-space. Our data showed that zero-padding DTI resulted in decreased fractional anisotropy (FA) and decreased mean apparent diffusion coefficient (mean ADC) in white matter (WM) regions. Keyhole DTI showed a better edge preservation on mean ADC maps but not on FA maps as compared to the zero-padding DTI. When increasing the sharing rate in keyhole approach, an underestimation of FA and an over- or underestimation of mean ADC were measured in WM depending on the selected reference image. The inconsistency of keyhole DTI may add a challenge for the wide use of this modality. However, with a carefully selected directive diffusion-weighted image to serve as the reference image in the keyhole approach, this study demonstrated that one may obtain DTI indices of reduced-encoding images with high consistency to those derived with full k-space DTI.     

Minimum SNR and acquisition for bias-free estimation of fractional anisotropy in diffusion tensor imaging - a comparison of two analytical techniques and field strengths

Although it is known that low signal-to-noise ratio (SNR) can affect tensor metrics, few studies reporting disease or treatment effects on fractional anisotropy (FA) report SNR; the implicit assumption is that SNR is adequate. However, the level at which low SNR causes bias in FA may vary with tissue FA, field strength and analytical methodology. We determined the SNR thresholds at 1.5 T vs. 3 T in regions of white matter (WM) with different FA and compared FA derived using manual region-of-interest (ROI) analysis to tract-based spatial statistics (TBSS), an operator-independent whole-brain analysis tool. Using ROI analysis, SNR thresholds on our hardware-software magnetic resonance platforms were 25 at 1.5 T and 20 at 3 T in the callosal genu (CG), 40 at 1.5 and 3 T in the anterior corona radiata (ACR), and 50 at 1.5 T and 70 at 3 T in the putamen (PUT). Using TBSS, SNR thresholds were 20 at 1.5 T and 3 T in the CG, and 35 at 1.5 T and 40 at 3 T in the ACR. Below these thresholds, the mean FA increased logarithmically, and the standard deviations widened. Achieving bias-free SNR in the PUT required at least nine acquisitions at 1.5 T and six acquisitions at 3 T. In the CG and ACR, bias-free SNR was achieved with at least three acquisitions at 1.5 T and one acquisition at 3 T. Using diffusion tensor imaging (DTI) to study regions of low FA, e.g., basal ganglia, cerebral cortex, and WM in the abnormal brain, SNR should be documented. SNR thresholds below which FA is biased varied with the analytical technique, inherent tissue FA and field strength. Studies using DTI to study WM injury should document that bias-free SNR has been achieved in the region of the brain being studied as part of quality control.     

Distortion correction for imaging on non-planar surface using freeform lens

The method of designing a freeform lens which can image on a formula describable non-planar surface with low distortion was proposed aimed on distortion correction. In this method, the Snell's law and the correspondence between the coordinates of object and the distortion free image are used to establish the partial differential equation which characterizes the freeform surface, and the partial differential equation can be solved to form the freeform surface. Take projection on spherical surface for example, a freeform lens is designed. After adding this lens to the ordinary projection lens at a certain position, the system (ordinary projection lens and freeform lens) can project an image on sphere with absolute distortion about 2 mm for an observer at half of the projection distance, and the MTF on sphere is analyzed in detail after.     

基于多视场星点椭圆率的望远镜主动准直方法

光学元件的准直失调会引起天文望远镜在观测过程中的像质退化,该问题在大口径快焦比的天文光学系统中更为突出。针对此问题,本文提出一种用于望远镜日常观测过程中的主动准直方法,通过星像解算实时校正副镜位置及姿态达到维持望远镜像质的目的。该方法基于多视场星点椭圆率,利用粒子群优化算法迭代求解望远镜光学元件失调量,从而校正由光学元件失调引起的低阶像差。利用1.6 m多通道测光巡天望远镜光学系统进行模拟仿真,求解副镜失调量残余误差小于1%,在系统设计公差范围以内。利用南极巡天望远镜AST3-3模拟及实验验证,表明该方法可高精度求解望远镜光学元件的失调误差。     

Compression Helps Deep Learning in Image Classification

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL. Specifically, for each original image, consider its 10 JPEG compressed versions with their quality factor (QF) values from {100,90,80,70,60,50,40,30,20,10}. Under the assumption that the ground truth label of the original image is known at the time of selecting an input, but unknown to the underlying DNN, we present a selector called Highest Rank Selector (HRS). It is shown that HRS is optimal in the sense of achieving the highest Top k accuracy on any set of images for any k among all possible selectors. When the underlying DNN is Inception V3 or ResNet-50 V2, HRS improves, on average, the Top 1 classification accuracy and Top 5 classification accuracy on the whole ImageNet validation dataset by 5.6% and 1.9%, respectively, while reducing the input size in bits dramatically—the compression ratio (CR) between the size of the original images and the size of the selected input images by HRS is 8 for the whole ImageNet validation dataset. When the ground truth label of the original image is unknown at the time of selection, we further propose a new convolutional neural network (CNN) topology which is based on the underlying DNN and takes the original image and its 10 JPEG compressed versions as 11 parallel inputs. It is demonstrated that the proposed new CNN topology, even when partially trained, can consistently improve the Top 1 accuracy of Inception V3 and ResNet-50 V2 by approximately 0.4% and the Top 5 accuracy of Inception V3 and ResNet-50 V2 by 0.32% and 0.2%, respectively. Other selectors without the knowledge of the ground truth label of the original image are also presented. They maintain the Top 1 accuracy, the Top 5 accuracy, or the Top 1 and Top 5 accuracy of the underlying DNN, while achieving CRs of 8.8, 3.3, and 3.1, respectively.     

基于基效应分解的多谱投影序列盲分离算法研究

在常规CT成像系统中,发出的X射线是连续能谱的,导致重建图像出现硬化伪影,影响了材料组分区分,无法进行定量表征。解决这一问题的关键在于实现多能谱CT成像,利用多个窄能谱段或单能量CT图像,提高组分与图像灰度的对应性。相对于传统CT,能谱CT具有更强的组分区分能力,有利于实现物体组分的定量分析。现有的基于光子计数探测器多谱CT在成像时间分辨率和空间分辨率存在局限,基于能谱滤波的多谱CT能谱区分度受限。而基于变电压多谱投影序列盲分离的多谱CT,通过分解连续能谱投影,获取窄能谱投影,进而实现能谱CT成像,确保物质组分与重建图像灰度值的对应性,实用性较强。但是由于X射线能谱和物体组分的未知,在盲分离过程中,衰减系数未知,并且能谱划分是不确定的,导致窄能谱CT重建图像的能量指向性不强,对应能量值与参考能量偏差偏大,影响组分定量分析。因此,针对盲分离中能谱划分不确定性和重建图像能量指向性问题的开展研究。利用衰减系数的光电效应和康普顿效应分解,构建能量约束,消除能量的不确定性,降低分解所得投影重建图像的能量与参考能量值的偏差。在基于以残差的局部方差和最小为优化目标的分解模型中,将分解模型中的衰减系数按光电效应和康普顿效应分解为能量项和材料项,利用能量项的可预知性,依据预先划分的窄能谱段设置其值,固定各分解投影对应的窄能谱段,作为对能量的约束条件。求解所得各分解投影为能量已知投影,对其重建可得到能量确定的各窄能谱段的图像。选择衰减系数相近的硅铝材质构成外硅内铝圆柱体进行实验验证,在有能量约束的求解结果中,硅铝衰减系数与参考值偏差小于无能量约束,所得重建图像中硅铝变化率与理论值趋势较一致,能量指向性强,与参考能量偏差降低。结果表明,所提方法解决了基于变电压序列盲分离多谱CT成像的能量指向问题,能谱分辨率更高,组分表征更准确。     

Reproducibility and variation of diffusion measures in the squirrel monkey brain,in vivo and ex vivo

PurposeAnimal models are needed to better understand the relationship between diffusion MRI (dMRI) and the underlying tissue microstructure. One promising model for validation studies is the common squirrel monkey, Saimiri sciureus. This study aims to determine (1) the reproducibility of in vivo diffusion measures both within and between subjects; (2) the agreement between in vivo and ex vivo data acquired from the same specimen and (3) normal diffusion values and their variation across brain regions.MethodsData were acquired from three healthy squirrel monkeys, each imaged twice in vivo and once ex vivo. Reproducibility of fractional anisotropy (FA), mean diffusivity (MD), and principal eigenvector (PEV) was assessed, and normal values were determined both in vivo and ex vivo.ResultsThe calculated coefficients of variation (CVs) for both intra-subject and inter-subject MD were below 10% (low variability) while FA had a wider range of CVs, 2–14% intra-subject (moderate variability), and 3–31% inter-subject (high variability). MD in ex vivo tissue was lower than in vivo (30%–50% decrease), while FA values increased in all regions (30–39% increase). The mode of angular differences between in vivo and ex vivo PEVs was 12 degrees.ConclusionThis study characterizes the diffusion properties of the squirrel monkey brain and serves as the groundwork for using the squirrel monkey, both in vivo and ex vivo, as a model for diffusion MRI studies.     

Vibration analysis of rail grinding using a twin-wheel grinder

Grinding is the final process of machining a rail. Conventionally, the rail’s surfaces are ground by a single-wheel grinder. The vibrations caused by the grinding process can greatly influence the final surface roughness and dimensional accuracy of the rail. This research investigates performance achieved by using two grinding wheels simultaneously and symmetrically on two opposite surfaces of a rail. In practical terms, the feed force from the two grinding wheels cannot be aligned perfectly, and the imbalance and/or imperfect roundness of the grinding wheels will certainly result in vibrations during the grinding process. This study applies an impedance method to determine rail vibration and the grinding instability, such as chatter caused by feed force misalignment and grinding wheel imbalance. When compared to conventional single-wheel grinding, the results indicate twin-wheel grinding reduces rail vibration, leading to low incidence of grinding chatter and better grinding performance. However, feed force misalignment between the two grinding wheels can lead to increased chatter, and both resonance and chatter may occur at lower grinding speeds as feed force misalignment increases. Results also show that feed force misalignment has a greater effect on rail vibration and chatter than imbalance asynchronization between the two grinding wheels.     

Understanding changes in DTI metrics in patients with different stages of neurocysticercosis

Diffusion tensor imaging (DTI) was performed on 25 patients with neurocysticercosis (NCC). The aim of this study was to investigate the changes in DTI measures during the evolutionary course of NCC lesions from vesicular to calcified stage in the brain. DTI measures were quantified from the NCC lesions of all patients. On the basis of conventional imaging findings, NCC lesions were classified into vesicular, vesicular colloidal, granular nodular and calcified stages. Significant inverse correlation was observed between the evolutionary stage of NCC lesion and mean diffusivity (MD; r=−0.748, P<0.001) and spherical anisotropy (CS; r=−0.585, P<.001) values. Significant direct correlations were observed between evolutionary stages of NCC lesion and mean fractional anisotropy (FA; r=0.575, P<0.001), linear anisotropy (CL; r=0.478, p<0.001) and planar anisotropy (CP; r=0.561, p<0.001) values. Successive decrease in MD values calculated from NCC lesions was observed, moving from vesicular to granular nodular stage. On FA, CL and CP maps, a significant increase in signal intensity value was observed in calcified as compared to other stages. We conclude that DTI measures may indicate the evolutionary changes in NCC from vesicular to calcified stage.     

Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T₁-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T₁-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.     

p-Harmonic morphisms,biharmonic morphisms,and nonharmonic biharmonic maps

We study the transformation of a p-harmonic morphism into a q-harmonic morphism via biconformal change of the domain metric and/or conformal change of the codomain metric. As an application of p-harmonic morphisms, we characterize a twisted product among doubly twisted products and a warped product among twisted products using p-harmonicity of their projection maps. We describe those p-harmonic morphisms which are also biharmonic morphisms and give a complete classification of polynomial biharmonic morphisms between Euclidean spaces. Finally, we show that a horizontally homothetic harmonic morphism with harmonic energy density pulls back a nonharmonic biharmonic map to a nonharmonic biharmonic map and that totally geodesic immersing the target manifold of a nonharmonic biharmonic map into an ambient manifold produces a new nonharmonic biharmonic map. These methods are used to construct many examples of nontrivial biharmonic maps.     

A Semantic Approach to the Completeness Problem in Quantum Mechanics

The old Bohr–Einstein debate about the completeness of quantum mechanics (QM) was held on an ontological ground. The completeness problem becomes more tractable, however, if it is preliminarily discussed from a semantic viewpoint. Indeed every physical theory adopts, explicitly or not, a truth theory for its observative language, in terms of which the notions of semantic objectivity and semantic completeness of the physical theory can be introduced and inquired. In particular, standard QM adopts a verificationist theory of truth that implies its semantic nonobjectivity; moreover, we show in this paper that standard QM is semantically complete, which matches Bohr's thesis. On the other hand, one of the authors has provided a Semantic Realism (or SR) interpretation of QM that adopts a Tarskian theory of truth as correspondence for the observative language of QM (which was previously mantained to be impossible); according to this interpretation QM is semantically objective, yet incomplete, which matches EPR's thesis. Thus, standard QM and the SR interpretation of QM come to opposite conclusions. These can be reconciled within an integrationist perspective that interpretes non-Tarskian theories of truth as theories of metalinguistic concepts different from truth.