In-plane Resolution Research Articles

3D model-based super-resolution motion-corrected cardiac T1 mapping

Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR). Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6–8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6–8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5–2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects. Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%. Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5–2 mm3). The approach led to improvements in the visualization of small structures and precise T1 values.

MR microscopy of the developing upper extremity of the chicken in ovo using 7 Tesla MRI.

MR microscopy (MRM) is known as ultra-high-field (UHF) magnetic resonance imaging with an in-plane spatial resolution of <100 μm, yields highly resolved non-invasive anatomical imaging and allows longitudinal assessment of embryonic avian development. The aim of the present study was to evaluate the feasibility of in vivo anatomical MRI assessment of the developing upper extremity of the chicken. Thirty-eight fertilized chicken eggs were examined at 7 Tesla acquiring high-resolution T2-weighted images with an in-plane resolution of 74 × 74 μm. To reduce motion artefacts, the eggs were moderately cooled before and during MRI. Development of the upper extremity was anatomically and quantitatively assessed. Chondrification and ossification on MRI were correlated with histological examination. MRM allowed the identification of the embryo from stage D5 onwards. First chondrification of the upper extremity was visible at stage D7, and the differentiation of the forearm was possible from stage D9 throughout the developmental period with excellent correlation to histology. MRM also allowed the differentiation between cortical and medullary bone as well as the detection of chondrified areas. UHF MRM allows the in vivo and in ovo evaluation of the upper limb during embryonic development and provides non-invasive longitudinal anatomical information. This technique allows longitudinal studies of the same embryo during the developmental period and may therefore provide further insights into the development of the upper extremity. With improved coil technique and increasing availability of UHF MR systems, there is great potential regarding several research topics in experimental musculoskeletal radiology.

&lt;I&gt;In vivo&lt;/I&gt; mapping of hippocampal venous vasculature and oxygen saturation using dual-echo SWI/QSM at 7 T: a potential marker for neurodegeneration

Background: The current understanding of the venous system in the hippocampus is mostly based on histological and autopsy studies.1 However, the main disadvantage is that it only reveals the anatomy of the vascular system at the post-mortem stage and lacks physiological aspects associated with neuronal metabolism. In vivo characterization of the venous system using susceptibility weighted imaging (SWI) at 7 T could provide valuable information on both venous anatomy and blood oxygen saturation, through high-resolution SWI venography2 and quantitative susceptibility mapping (QSM).3 In this study, we aim to elucidate the hierarchical network of the hippocampal venous system and then test the feasibility of using venous susceptibility to characterize venous oxygenation level changes related to neurodegeneration.&#x0D; Methods: Seven healthy volunteers were recruited for this study. We used high in-plane resolution of flow-compensated dual-echo gradient echo sequence (TE1/TE2/TR=7.5/15/22 ms, voxel size: 0.25*0.25*1 mm). SWI and QSM were then reconstructed using the iterative SWI and mapping (iterative SWIM) algorithm,3 as shown in Figure 1. Hippocampus masks were extracted from the T1-MPRAGE image, which was transformed to SWI space afterwards. To reduce the partial volume effect from the tissue-vessel boundary, we extract the venous susceptibility value from each voxel along the centerline of the vessels.&#x0D; Results: High-resolution in vivo mapping of hippocampal venous vasculature exhibits a high analogy to Duvernoy’s reference4 for hippocampal vascularization. As shown in Figure 1, there is a shape of venous arch near the fimbria of the hippocampus, and small veins extending through the arch are possibly the intrahippocampal veins. The intrahippocampal veins will eventually reach the inferior ventricular vein (IVV) (anteriorly) and medial atrial vein (MAV) (posteriorly), before joining the basal vein of Rosenthal (BVR). For venous susceptibility quantification, Figure 1 shows the representative color-coded QSM for centerline extraction on BVR.&#x0D; Conclusions: Our results showed improved visualization of the micro venous system in the hippocampus using high-resolution 7 T SWI data without the contrast agent.5 In summary, the characterization of venous QSM in major tributaries related to the hippocampus offers a novel perspective on oxygen utilization in the hippocampus, which may be useful for studying age-related dementia. We delineated the hierarchical network of the hippocampus venous system using SWI/QSM at 7 T and extract the venous density and venous susceptibility value in hippocampus-related small veins and major venous tributaries, as an overall measure for venous oxygenation level related to the hippocampus, which may be used as an early marker for hippocampal atrophy in Alzheimer’s disease.

High-resolution ultra-low field magnetic resonance imaging with a high-sensitivity sensing coil.

We present high-resolution magnetic resonance imaging (MRI) at ultra-low field (ULF) with a proton Larmor frequency of around 120 kHz. The key element is a specially designed high-sensitivity sensing coil in the shape of a solenoid with a few millimeter gap between windings to decrease the proximity effect and, hence, increase the coil's quality ( ) factor and sensitivity. External noise is strongly suppressed by enclosing the sensing coil in a copper cylindrical shield, large enough not to negatively affect the coil's factor and sensitivity, measured to be 217 and 0.47 fT/Hz , respectively. To enhance small polarization of proton spins at ULF, a strong pulsed 0.1 T prepolarization field is applied, making the signal-to-noise ratio (SNR) of ULF MRI sufficient for high-quality imaging in a short time. We demonstrate ULF MRI of a copper sulfate solution phantom with a resolution of and SNR of 10. The acquisition time is 6.3 min without averaging. The sensing coil size in the current realization can accommodate imaging objects of 9 cm in size, sufficient for hand, and it can be further increased for human head imaging in the future. Since the in-plane resolution of is typical in anatomical medical imaging, this ULF MRI method can be an alternative low-cost, rapid, portable method for anatomical medical imaging of the human body or animals. This ULF MRI method can supplement other MRI methods, especially when such methods are restricted due to high cost, portability requirement, imaging artifacts, and other factors.

Super-Resolution Photoacoustic Microscopy via Modified Phase Compounding.

Acoustic-resolution photoacoustic micro- scopy (AR-PAM) system can provide 3-D images of facial tissues. The lateral resolution of AR-PAM depends on the numerical aperture (NA) of the acoustic lens and the central frequency of the ultrasonic transducer. There is a trade-off between resolution enhancement and imaging depth. The acoustic beam is tight in the acoustic focal plane but expands in the out-of-focus regions, deteriorating the resolution. High-NA AR-PAM has depth-variant resolution. Synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) concept can compensate for the beam shape and improve the lateral resolution via beamforming. Although, beamforming can enhance the resolution but the lateral resolution in the focal plane is still limited by acoustic diffraction. Structured-illumination can shift the spatial spectrum of an image to low frequencies hence high-frequency contents can be reserved to overcome the diffraction limit. Conventional structured-illumination via using a three-phase-shifting method can improve the resolution by two folds. Here, a modified phase-shifting method is used to generate the second harmonic of the fringes and double the spectral shift. In this idea, higher frequency information compared to the three-phase shifting method can fall into the band-limited system response. The modified phase-shifting method expands the spatial bandwidth and increases the lateral resolution by five folds. The mathematical relations and the theory are discussed in the context. Tungsten filament result shows resolution improvement from 44.6 [Formula: see text] to 11.3 [Formula: see text] by the modified structured illumination. In vivo and ex vivo experimental results validate the system performance.

Patient-Specific Self-Supervised Resolution-Enhancing Network for High-Resolution MR Imaging in MRI-Guided Radiotherapy

<h3>Purpose/Objective(s)</h3> The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiotherapy (RT) due to its superior soft tissue contrast as compared with CT. One common practice in acquiring the MR images is to use thick slices while keeping the in-plane resolution, especially for the large imaging field, to ensure enough coverage and avoid aliasing. However, this approach may lose high-definition information in the through-plane direction, which cannot be resolved through simple interpolation. Therefore, it is essential to develop an efficient and clinically applicable method to generate the high-resolution (HR) information of the MR image in the low-resolution (LR) direction to generate respective HR images. <h3>Materials/Methods</h3> We report a self-supervised deep learning algorithm to enhance image resolution, which does not use any training images with high through-plane resolution, yet can still generate high through-plane resolution images only relying on the acquired images with low through-plane resolution. The workflow consists of training two deep neural networks in parallel that independently predict the HR images in sagittal and coronal planes from images acquired in the axial direction, respectively. Then the final synthesized HR MR images are generated by fusing the two predicted images. We used 250 brain patient MR images obtained from the BraTS2020 dataset to evaluate the proposed workflow. In each direction of MR images, ground truth images were down-sampled by a factor of 3 to simulate low through-plane MR images to test our model. Normalized mean absolute error (NMAE) and peak SNR (PSNR) were used to quantify the performance. Institutional MRIs were also used for a proof-of-concept study of the proposed method since no HR images are available as the ground truth. <h3>Results</h3> The mean NMAE and PSNR were 0.053±0.017 and 34.1±1.7 dB for T1, 0.069±0.025 and 31.3±2.0 dB for contrast-enhanced T1, and 0.068±0.026 and 31.2±1.8 dB for T2, as well as 0.051±0.018 and 34.5±2.1 dB for FLAIR images, demonstrating the accuracy of the proposed method. Qualitative evaluation of the synthetic HR MR images of the clinical collected dataset could also confirm the feasibility of the proposed method. <h3>Conclusion</h3> We have shown a patient-specific deep learning-based method with self-supervised parallel networks to synthesize HR MR images without the need for traditional large training datasets or ground truth images. The results support the potential of using artificial intelligence to improve image resolution beyond the limits of current MRI data acquisition. The self-supervision capability could have broad clinical applications, especially with regards to shortening MR acquisition time in clinical MRI practices (e.g., MRI-guided RT) to enable accelerated MRI without sacrificing the spatial resolution.

Iterative Refinement to Improve Data Quality and Label Consistency by Synergizing Parsimony Model Guidance and Physician Interaction

<h3>Purpose/Objective(s)</h3> Clinical data and labeling are usually noisy and uncertain. Modeling based on such inconsistent data risks overfit and may generate faulty insight on prominent features and regression relationships. We hypothesize inter-sample consistency serves as a rational surrogate for data quality and can be used to guide improvement, in the absence of an absolute ground-truth. To this end, we aim to synergize a deep-learning setting and an iterative interactive refinement procedure for label refinement. <h3>Materials/Methods</h3> We proposed a novel approach by guiding physician-approved label perturbation with parsimonious deep network modeling. The proposed procedure alternates between fitting a parsimonious deep network model to human labels and guiding the human observer to review the labels to identify ill-fitted examples to perform label refinement when appropriate. Convergence is claimed when further iteration stops to produce clinically pronounced improvement, as defined from a statistical equivalence test. To evaluate the efficacy of the refinement, input-output agreement index (IOAI) based on partial ranking consistency is also calculated. We took as a use case in segmenting the lumen and vessel wall from MR vessel wall imaging (VWI) from 80 patients with intracranial atherosclerotic disease, on four locations with high likelihood of plaque presence: the intracranial internal carotid artery, the middle cerebral artery, the intracranial vertebral artery, and the basilar artery. Each segment contained 30 contiguous 2D cross-sectional slices with 0.55 mm slice thickness and 0.10 mm in-plane resolution. A lightweight 2.5D segmentation network was used as the low-dimensional model, and equivalence criterion was defined by one-sided superiority threshold of 0.03 in Dice similarity coefficient (DSC) based on reported performance from existing study. Clinical soundness was further assessed with the variation in normalized wall index as a quantitative imaging index, expected to low if segmented structures were piecewise smooth conforming to clinical insight. <h3>Results</h3> 5-fold cross validation based on the 80*4*30 slice samples showed enhanced modeling performance and better conformality to clinical insight with the final labels. In this use case, DSC improved from 0.893±0.108 to 0.938±0.078 for lumen, from 0.806±0.086 to 0.879±0.072 for vessel wall, the total variation in normalized wall index decreased from 0.757±0.181 to 0.586±0.182, and the input-output agreement index increased from 0.523 to 0.556 by the proposed refinement procedure. <h3>Conclusion</h3> This study demonstrates that inconsistency in clinical labels and physiological limitations can be addressed with an iterative interactive process with parsimonious modeling, taking advantage of flexibility from deep networks. The rationale generalizes to other label settings and tasks.

An Across Feature Map Attention-Based Deep Learning Method for Small Liver Tumor Segmentation in CT Scans

<h3>Purpose/Objective(s)</h3> Liver cancer is the second most frequent cancer to cause death in men and sixth for women. Detection of liver cancer early using computed tomography (CT) could assist doctors in making accurate hepatocellular carcinoma evaluation and treatment planning, which prevent millions of patients' death every year. Traditionally, radiologists delineate the liver lesion by manually reading hundreds of CT scans slice by slice, which is an enormous burden and suffers from inter- or intra-operator variations. Therefore, automatic liver tumor segmentation methods are highly demanded in clinical practice. Although many deep convolutional neural network-based techniques for automatic segmentation have been proposed, the small tumor remains challenging to segment due to convolutional and pooling operations that result in the loss of small object information. This study aims to propose a deep learning method for accurate segmentation for small liver tumors. <h3>Materials/Methods</h3> This study included 131 patients with liver cancer. The in-plane resolution of the patients' CTs is from 0.55 mm to 1.0 mm and slice spacing from 0.45 mm to 6.0 mm. We randomly selected 100 CT scans as the training set and others as the testing set. Each CT slice of the testing set was separated into groups according to tumor size as follows: 0.1–2.0, 2.1–5.0, 5.1–10.0, and 10.1–20.0 cm. The CT slice without tumor or tumor size > 20 cm were excluded. This work presents a novel attention-based deep learning method for small liver tumor segmentation, which exploits the relation between small and large tumors by computing the original image of small tumors and the feature maps of large tumors. The relation of small-large tumors can compensate for the information loss of small tumors during the convolutional and pooling operations and improve the performance of small tumor segmentation. <h3>Results</h3> Among 20,693 CT slices of the 31 testing patients, 3.0% CT slices with tumors ≤2 cm, 6.7% ≤5 cm, 10.6% ≤10 cm, and 13.4%≤20 cm. We compared our method with six widely used segmentation models. The results show our model outperforms other methods on all sizes of liver tumors, especially for small size tumors: For the 0.1-2.0 cm liver tumor, it achieved 8.4%, 10.0%, 11.3%, 9.1%, 10.9%, and 9.6% improvement compared to Unet, PAN, DeepLabV3, FPN, LinkNet, and PSPNet, respectively. <h3>Conclusion</h3> The tumors of different sizes share similar imaging characteristics. The information of large tumors can compensate for the information loss of small tumors from the feature propagation processes of deep learning methods. The small-large tumors relation can significantly improve small liver tumor segmentation, which is beneficial for disease diagnosis and treatment planning and has the potential for other types of tumor detection and segmentation.

Assessment of abdominal organ dose and image quality in varying arc trajectory interventional C-arm cone beam CT

PurposeThe aim of this study was to investigate the effect of varying arc exposure trajectory on radiation dose to radiosensitive organs and to assess image quality in abdominal C-arm cone beam computed tomography (CBCT) interventional procedures using a latest generation system. MethodsAn anthropomorphic phantom that simulates the average adult individual was used. Individual-specific Monte Carlo (MC) simulation dosimetry was performed to estimate organ doses (OD) in abdominal C-arm CBCT. Seven examination protocols prescribed by the system for vascular and soft tissue CBCT, were simulated. These protocols are differentiated in the range of the arc exposure trajectory and the level of radiation dose delivered to the patient. OD was estimated for liver, adrenals, kidneys, pancreas, stomach, gall bladder, spleen, bone and skin. Image noise, signal to noise ratio (SNR), contrast to noise ratio (CNR) and in-plane spatial resolution were assessed using CT-specific image quality assessment phantoms. ResultsOD was found to depend on the range of arc trajectory and was higher for posterior located organs. In vascular protocols OD ranged from 4.75 mGy for skin to 0.60 mGy for bone. Image noise was higher in vascular protocols than in soft tissue ones. SNR and CNR were significantly modified among different soft tissue protocols (P < 0.05). In-plane spatial resolution was found 0.80 lp/mm in vascular as opposed to 0.41 lp/mm in soft tissue protocols. ConclusionsThe current results may be used to estimate OD for different examination protocols and enable operators choose the appropriate acquisition protocol on the preprogrammed interventional task.

Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small-animal imager.

Computed tomography (CT) generates a three-dimensional rendering that can be used to interrogate a given region or desired structure from any orientation. However, in preclinical research, its deployment remains limited due to relatively high upfront costs. Existing integrated imaging systems that provide merged planar X-ray also dwarfs CT popularity in small laboratories due to their addedversatility. In this paper, we sought to generate CT-like data using an existing small-animal X-ray imager with a specialized specimen rotation system, or MiSpinner. This setup conforms to the cone-beam CT (CBCT) geometry, which demands high spatial calibration accuracy. Therefore, a simple but robust geometry calibration algorithm is necessary to ensure that the entire imaging system works properly andaccurately. Because the rotation system is not permanently affixed, we propose a structure tensor-based two-step online (ST-TSO) geometry calibration algorithm. Specifically, two datasets are needed, namely, calibration and actual measurements. A calibration measurement detects the background of the system forward X-ray projections. A study on the background image reveals the characteristics of the X-ray photon distribution, and thus, provides a reliable estimate of the imaging geometry origin. Actual measurements consisted of an X-ray of the intended object, including possible geometry errors. A comprehensive image processing technique helps to detect spatial misalignment information. Accordingly, the first processing step employs a modified projection matrix-based calibration algorithm to estimate the relevant geometric parameters. Predicted parameters are then fine-tuned in a second processing step by an iterative strategy based on the symmetry property of the sum of projections. Virtual projections calculated from the parameters after two-step processing compensate for the scanning errors and are used for CT reconstruction. Experiments on phantom and mouse imaging data were performed to validate the calibrationalgorithm. Once system correction was conducted, CBCT of a CT bar phantom and a cohort of euthanized mice were analyzed. No obvious structure error or spatial artifacts were observed, validating the accuracy of the proposed geometry calibration method. Digital phantom simulation indicated that compared withthe preset spatial values, errors in the final estimated parameters could be reduced to 0.05° difference in dominant angle and 0.5-pixel difference in dominant axis bias. The in-plane resolution view of the CT-bar phantom revealed that the resolution approaches 150 m. A constrained two-step online geometry calibration algorithm has been developed to calibrate an integrated X-ray imaging system, defined by a first-step analytical estimation and a second-step iterative fine-tuning. Test results have validated its accuracy in system correction, thus demonstrating the potential of the described system to be modified and adapted for preclinicalresearch.

3D ultrasonic imaging of surface-breaking cracks using a linear array

Ultrasonic linear arrays have great potential to generate high-quality three-dimensional (3D) images by scanning the array. However, the generated images suffer from low resolution in the elevation plane, limiting the image quality for a reliable 3D Non-Destructive Testing (NDT) inspection. Although several ultrasonic imaging methods have been implemented to inspect different types of defects, there has been limited research to characterise surface-breaking cracks (SBCs) in 3D quantitatively. To improve the characterisation of surface-breaking cracks (SBCs), a 3D hybrid imaging method is proposed by combining the Half Skip Total Focusing Method (HSTFM) and the Synthetic Aperture Focusing Technique (SAFT) using a linear array. This paper proposed the implementation of an array with a reduced element length for full matrix capture (FMC) data acquisition. In conjunction with the hybrid imaging method, a reduced element array enables the utilisation of the information from a broad ultrasonic beam in the elevation direction to achieve improved image resolution. The imaging capability is assessed via a point spread function (PSF) as well as numerical simulations. From the PSF measurements, the image resolution is shown to improve with the smaller element length of the array, which is attributable to the combination of wide beamwidth and hybrid imaging method. Thereafter, experimental validation was performed with arrays of different elevation lengths, where an excellent match with the numerical results was observed. Furthermore, the crack sizing was performed using a 6-dB-drop rule, which assisted in accurately predicting the shape and size of the SBCs and is shown to measure the depth of SBCs with greater confidence. It is shown that a reduced array elevation with the hybrid imaging method and sizing method yields improved image resolution contrary to conventional linear arrays. This approach can offer a significant improvement in manifesting complete comprehension of the spatial defect relationship, enabling NDT engineers to analyse the inspection results quantitatively in 3D for progressive reliability.

Evaluation of a High-Sensitivity Organ-Targeted PET Camera

The aim of this study is to evaluate the performance of the Radialis organ-targeted positron emission tomography (PET) Camera with standardized tests and through assessment of clinical-imaging results. Sensitivity, count-rate performance, and spatial resolution were evaluated according to the National Electrical Manufacturers Association (NEMA) NU-4 standards, with necessary modifications to accommodate the planar detector design. The detectability of small objects was shown with micro hotspot phantom images. The clinical performance of the camera was also demonstrated through breast cancer images acquired with varying injected doses of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) and qualitatively compared with sample digital full-field mammography, magnetic resonance imaging (MRI), and whole-body (WB) PET images. Micro hotspot phantom sources were visualized down to 1.35 mm-diameter rods. Spatial resolution was calculated to be 2.3 ± 0.1 mm for the in-plane resolution and 6.8 ± 0.1 mm for the cross-plane resolution using maximum likelihood expectation maximization (MLEM) reconstruction. The system peak noise equivalent count rate was 17.8 kcps at a 18F-FDG concentration of 10.5 kBq/mL. System scatter fraction was 24%. The overall efficiency at the peak noise equivalent count rate was 5400 cps/MBq. The maximum axial sensitivity achieved was 3.5%, with an average system sensitivity of 2.4%. Selected results from clinical trials demonstrate capability of imaging lesions at the chest wall and identifying false-negative X-ray findings and false-positive MRI findings, even at up to a 10-fold dose reduction in comparison with standard 18F-FDG doses (i.e., at 37 MBq or 1 mCi). The evaluation of the organ-targeted Radialis PET Camera indicates that it is a promising technology for high-image-quality, low-dose PET imaging. High-efficiency radiotracer detection also opens an opportunity to reduce administered doses of radiopharmaceuticals and, therefore, patient exposure to radiation.

Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system. Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey’s honestly significant difference post-hoc tests were utilized for statistical analysis. Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results. Significance. An overall improvement in image quality for metrics most important to IGRT and ART—mainly image noise and contrast—was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.

Volumetric random-access multi-focus scanning based on fast light modulation

Highly efficient scanning of focused light in the three-dimensional space is of great significance in many fields, such as volumetric biomedical imaging, laser beam machining, and optical metrology. Here we propose light-modulation-based volumetric multi-focus scanning (LM-VMS) by generating a complex field superposed of multiple spherical wave components. Each spherical wave component corresponds to a focus, and can be modulated independently by changing its tilt term and defocus term. Therefore, the positions of all the foci can be manipulated in a random-access scan mode. We showed the excellent performance of LM-VMS in a scanning system based on a digital micromirror device. This system had a lateral scanning radius of 2.43 mm and a longitudinal scanning range of 140 mm. Its lateral resolution in the focal plane was 28.1 ± 0.4 μm. Furthermore, we demonstrated that LM-VMS could scan 24 foci simultaneously along independent, arbitrary trajectories at a rate of 22.7 kHz. LM-VMS is a promising solution for optical random-access scan to considerably accelerate the information acquisition and machining in various industrial applications.

Highly accelerated EPI with wave encoding and multi-shot simultaneous multislice imaging.

To introduce wave-encoded acquisition and reconstruction techniques for highly accelerated EPI with reduced g-factor penalty and image artifacts. Wave-EPI involves application of sinusoidal gradients during the EPI readout, which spreads the aliasing in all spatial directions, thereby taking better advantage of 3D coil sensitivity profiles. The amount of voxel spreading that can be achieved by the wave gradients during the short EPI readout period is constrained by the slew rate of the gradient coils and peripheral nerve stimulation monitor. We propose to use a "half-cycle" sinusoidal gradient to increase the amount of voxel spreading that can be achieved while respecting the slew and stimulation constraints. Extending wave-EPI to multi-shot acquisition minimizes geometric distortion and voxel blurring at high in-plane resolutions, while structured low-rank regularization mitigates shot-to-shot phase variations. To address gradient imperfections, we propose to use different point spread functions for the k-space lines with positive and negative polarities, which are calibrated with a FLEET-based reference scan. Wave-EPI enabled whole-brain single-shot gradient-echo (GE) and multi-shot spin-echo (SE) EPI acquisitions at high acceleration factors at 3T and was combined with g-Slider encoding to boost the SNR level in 1 mm isotropic diffusion imaging. Relative to blipped-CAIPI, wave-EPI reduced average and maximum g-factors by up to 1.21- and 1.37-fold at Rin × Rsms =3 × 3, respectively. Wave-EPI allows highly accelerated single- and multi-shot EPI with reduced g-factor and artifacts and may facilitate clinical and neuroscientific applications of EPI by improving the spatial and temporal resolution in functional and diffusion imaging.

Multi-echo balanced SSFP with a sequential phase-encoding order for functional MR imaging at 7T.

To develop a 2D multi-echo passband balanced SSFP (bSSFP) sequence using an echo-train readout with a sequential phase-encoding order (sequential multi-echo bSSFP), and evaluate its performance in fast functional brain imaging at 7 T. As images of sequential multi-echo bSSFP exhibit multiple ghosts due to periodic k-space modulations, a GRAPPA-based reconstruction method was proposed to eliminate ghosting artifacts. MRI experiments were performed to compare the image quality of multi-echo bSSFP and conventional single-echo bSSFP. Submillimeter-resolution fMRI using a checkerboard visual stimulus was conducted to compare the activation characteristics of multi-echo bSSFP, conventional single-echo bSSFP and standard gradient-echo EPI (GE-EPI). A higher mean structural similarity index was found between images of single-echo bSSFP and multi-echo bSSFP with a shorter echo train length (ETL). Multi-echo bSSFP (ETL=3) showed higher temporal SNR (tSNR) values than GRAPPA-accelerated single-echo bSSFP (R=2). In submillimeter-resolution fMRI experiments, multi-echo bSSFP (ETL=3) approached the imaging speed of GRAPPA-accelerated single-echo bSSFP (R=2), but without tSNR penalty and reduced activation due to acceleration. The median t-value and the number of significantly activated voxels were comparable between GE-EPI and multi-echo bSSFP (ETL=3) that provides virtually distortion-free functional images and inherits the activation patterns of conventional bSSFP. Sequential multi-echo bSSFP (ETL=3) is suitable for fast fMRI with submillimeter in-plane resolution, and offers an option to accelerate bSSFP imaging without tSNR penalty like parallel imaging.

2D–3D cascade network for glioma segmentation in multisequence MRI images using multiscale information

Background and objectiveGlioma segmentation is an important procedure for the treatment plan and follow-up evaluation of patients with glioma. UNet-based networks are widely used in medical image segmentation tasks and have achieved state-of-the-art performance. However, context information along the third dimension is ignored in 2D convolutions, whereas difference between z-axis and in-plane resolutions is large in 3D convolutions. Moreover, an original UNet structure cannot capture fine details because of the reduced resolution of feature maps near bottleneck layers. MethodsTo address these issues, a novel 2D–3D cascade network with multiscale information module is proposed for the multiclass segmentation of gliomas in multisequence MRI images. First, a 2D network is applied to fully exploit potential intra-slice features. A variational autoencoder module is incorporated into 2D DenseUNet to regularize a shared encoder, extract useful information, and represent glioma heterogeneity. Second, we integrated 3D DenseUNet with the 2D network in cascade mode to extract useful inter-slice features and alleviate the influence of large difference between z-axis and in-plane resolutions. Moreover, a multiscale information module is used in the 2D and 3D networks to further capture the fine details of gliomas. Finally, the whole 2D–3D cascade network is trained in an end-to-end manner, where the intra-slice and inter-slice features are fused and optimized jointly to take full advantage of 3D image information. ResultsOur method is evaluated on publicly available and clinical datasets and achieves competitive performance in these two datasets. ConclusionsThese results indicate that the proposed method may be a useful tool for glioma segmentation.

Diffusion Tensor Imaging of a Median Nerve by Magnetic Resonance: A Pilot Study.

The magnetic resonance Diffusion Tensor Imaging (DTI) is a powerful extension of Diffusion Weighted Imaging (DWI) utilizing multiple bipolar gradients, allowing for the evaluation of the microstructural environment of the highly anisotropic tissues. DTI was predominantly used for the assessment of the central nervous system (CNS), but with the advancement in magnetic resonance (MR) hardware and software, it has now become possible to image the peripheral nerves which were difficult to evaluate previously because of their small caliber. This study focuses on the assessment of the human median peripheral nerve ex vivo by DTI microscopy at 9.4 T magnetic field which allowed the evaluation of diffusion eigenvalues, the mean diffusivity and the fractional anisotropy at 35 μm in-plane resolution. The resolution was sufficient for clear depiction of all nerve anatomical structures and therefore further image analysis allowed the obtaining of average values for DT parameters in nerve fascicles (intrafascicular region and perineurium) as well as in the surrounding epineurium. The results confirmed the highest fractional anisotropy of 0.33 and principal diffusion eigenvalue of 1.0 × 10−9 m2/s in the intrafascicular region, somewhat lower values of 0.27 and 0.95 × 10−9 m2/s in the perineurium region and close to isotropic with very slow diffusion (0.15 and 0.05 × 10−9 m2/s) in the epineurium region.

LED-Based Photo-CIDNP Hyperpolarization Enables 19F MR Imaging and 19F NMR Spectroscopy of 3-Fluoro-DL-tyrosine at 0.6 T

Although 19F has high potential to serve as a background-free molecular marker in bioimaging, the molar amount of marker substance is often too small to enable 19F MR imaging or 19F NMR spectroscopy with a sufficiently high signal-to-noise ratio (SNR). Hyperpolarization methods such as parahydrogen-based hyperpolarization or dynamic nuclear polarization (DNP) can significantly improve the SNR, but require expensive and complex sample preparation and the removal of toxic catalysts and solvents. Therefore, we used the biologically compatible model of the fluorinated amino acid 3-Fluoro-DL-tyrosine with riboflavin 5ʹ-monophosphate (FMN) as a chromophore dissolved in D2O with 3.4% H2Odest., allowing to transform light energy into hyperpolarization of the 19F nucleus via photo-chemically induced dynamic nuclear polarization (photo-CIDNP). We used a low-cost high-power blue LED to illuminate the sample replacing traditionally used laser excitation, which is both potentially harmful and costly. For the first time, we present results of hyperpolarized 19F MRI and 19F NMR performed with a low-cost 0.6 T benchtop MRI system. The device allowed simultaneous dual-channel 1H/19F NMR. 19F imaging was performed with a (0.94 mm)2 in-plane resolution. This enabled the spatial resolution of different degrees of hyperpolarization within the sample. We estimated the photo-CIDNP-based 19F signal enhancement at 0.6 T to be approximately 465. FMN did not bleach out even after multiple excitations, so that the signal-to-noise ratio could be further improved by averaging hyperpolarized signals. The results show that the easy-to-use experimental setup has a high potential to serve as an efficient preclinical tool for hyperpolarization studies in bioimaging.

Image Quality of High-Resolution 3-Dimensional Neck MRI Using CAIPIRINHA-VIBE and GRASP-VIBE: An Intraindividual Comparative Study.

Acquiring high-quality magnetic resonance imaging (MRI) of the head and neck region is often challenging due to motion and susceptibility artifacts. This study aimed to compare image quality of 2 high-resolution three-dimensional (3D) MRI sequences of the neck, controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-volumetric interpolated breath-hold examination (VIBE), and golden-angle radial sparse parallel imaging (GRASP)-VIBE. One hundred seventy-three patients indicated for contrast-enhanced neck MRI examination were scanned using 3 T scanners and both CAIPIRINHA-VIBE and GRASP-VIBE with nearly isotropic 3D acquisitions (&lt;1 mm in-plane resolution with analogous acquisition times). Patients' MRI scans were independently rated by 2 radiologists using a 5-grade Likert scale for overall image quality, artifact level, mucosal and lesion conspicuity, and fat suppression degree at separate anatomical regions. Interobserver agreement was calculated using the Cohen κ coefficient. The quality ratings of both sequences were compared using the Mann-Whitney U test. Nonuniformity and contrast-to-noise ratio values were measured in all subjects. Separate MRI scans were performed twice for each sequence in a phantom and healthy volunteer without contrast injection to calculate the signal-to-noise ratio (SNR). The scores of overall image quality, overall artifact level, motion artifact level, and conspicuity of the nasopharynx, oropharynx, oral cavity, hypopharynx, and larynx were all significantly higher in GRASP-VIBE than in CAIPIRINHA-VIBE (all P 's &lt; 0.001). Moderate to substantial interobserver agreement was observed in overall image quality (GRASP-VIBE κ = 0.43; CAIPIRINHA-VIBE κ = 0.59) and motion artifact level (GRASP-VIBE κ = 0.51; CAIPIRINHA-VIBE κ = 0.65). Lesion conspicuity was significantly higher in GRASP-VIBE than in CAIPIRINHA-VIBE ( P = 0.005). The degree of fat suppression was weaker in the lower neck regions in GRASP-VIBE (3.90 ± 0.72) than in CAIPIRINHA-VIBE (4.97 ± 0.21) ( P &lt; 0.001). The contrast-to-noise ratio at hypopharyngeal level was significantly higher in GRASP-VIBE (6.28 ± 4.77) than in CAIPIRINHA-VIBE (3.14 ± 9.95) ( P &lt; 0.001). In the phantom study, the SNR of GRASP-VIBE was 12 times greater than that of CAIPIRINHA-VIBE. The in vivo SNR of the volunteer MRI scan was 13.6 in CAIPIRINHA-VIBE and 20.7 in GRASP-VIBE. Both sequences rendered excellent images for head and neck MRI scans. GRASP-VIBE provided better image quality, as well as mucosal and lesion conspicuities, with less motion artifacts, whereas CAIPIRINHA-VIBE provided better fat suppression in the lower neck regions.