Multislice Magnetic Resonance Imaging Research Articles

Brain image segmentation of the corpus callosum by combining Bi-Directional Convolutional LSTM and U-Net using multi-slice CT and MRI

Background and objectiveTraditional disease diagnosis is usually performed by experienced physicians, but misdiagnosis or missed diagnosis still exists. Exploring the relationship between changes in the corpus callosum and multiple brain infarcts requires extracting corpus callosum features from brain image data, which requires addressing three key issues. (1) automation, (2) completeness, and (3) accuracy. Residual learning can facilitate network training, Bi-Directional Convolutional LSTM (BDC-LSTM) can exploit interlayer spatial dependencies, and HDC can expand the receptive domain without losing resolution. MethodsIn this paper, we propose a segmentation method by combining BDC-LSTM and U-Net to segment the corpus callosum from multiple angles of brain images based on computed tomography (CT) and magnetic resonance imaging (MRI) in which two types of sequence, namely T2-weighted imaging as well as the Fluid Attenuated Inversion Recovery (Flair), were utilized. The two-dimensional slice sequences are segmented in the cross-sectional plane, and the segmentation results are combined to obtain the final results. Encoding, BDC- LSTM, and decoding include convolutional neural networks. The coding part uses asymmetric convolutional layers of different sizes and dilated convolutions to get multi-slice information and extend the convolutional layers' perceptual field. ResultsThis paper uses BDC-LSTM between the encoding and decoding parts of the algorithm. On the image segmentation of the brain in multiple cerebral infarcts dataset, accuracy rates of 0.876, 0.881, 0.887, and 0.912 were attained for the intersection of union (IOU), dice similarity coefficient (DS), sensitivity (SE), and predictive positivity value (PPV). The experimental findings demonstrate that the algorithm outperforms its rivals in accuracy. ConclusionThis paper obtained segmentation results for three images using three models, ConvLSTM, Pyramid-LSTM, and BDC-LSTM, and compared them to verify that BDC-LSTM is the best method to perform the segmentation task for faster and more accurate detection of 3D medical images. We improve the convolutional neural network segmentation method to obtain medical images with high segmentation accuracy by solving the over-segmentation problem.

Anatomy of the rabbit inner ear using computed tomography and magnetic resonance imaging.

Anatomically, the inner ear is a highly complex organ of intricate design, composed of a bony labyrinth that encases the same-shaped membranous labyrinth. It is difficult to study the three-dimensional anatomy of the inner ear because the relevant structures are very small and embedded within the petrous temporal bone, one of the densest bones in the body. The current study aimed to provide a detailed anatomic reference for the normal anatomy of the rabbit's inner ear. As a study model, ten healthy adults New Zealand White rabbit heads were used. Six heads were used for macroscopic evaluation of the bony and membranous labyrinths. The remaining four heads were evaluated radiographically, where 3D images were generated of the bony and membranous labyrinths using data sets from computed tomography (CT) and magnetic resonance imaging (MRI), respectively. The anatomical structures were identified and labelled according to NominaAnatomicaVeterinaria (NAV). Our study revealed that CT and MRI are the optimal cross-sectional imaging modalities for investigating such tiny and often inaccessible inner ear structures. As high-quality scanners are not readily available to veterinarians, the CT and MRI images generated by this research were of lower quality; therefore, high-quality dissections were used to identify/support structures seen in these images. In conclusion, this study provides one of the first investigations that uses multislice CT scans and MRI to study the rabbit's inner ear and its correlation with the corresponding anatomical images. Both anatomical, CT and MRI images will serve as a reference for interpreting pathologies relative to the rabbit's inner ear.

AFFIRM: Affinity Fusion-Based Framework for Iteratively Random Motion Correction of Multi-Slice Fetal Brain MRI.

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequential motion from multiple stacks of slices and integrates the features between 2D slices and reconstructed 3D volume using affinity fusion, which resembles the iterations between slice-to-volume registration and volumetric reconstruction in the regular pipeline. The method accurately estimates the motion regardless of brain orientations and outperforms other state-of-the-art learning-based methods on the simulated motion-corrupted data, with a 48.4% reduction of mean absolute error for rotation and 61.3% for displacement. We then incorporated AFFIRM into the multi-resolution slice-to-volume registration and tested it on the real-world fetal MRI scans at different gestation stages. The results indicated that adding AFFIRM to the conventional pipeline improved the success rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.

Patient-level grading prediction of prostate cancer from mp-MRI via GMINet

Magnetic resonance imaging (MRI) is considered the best imaging modality for non-invasive observation of prostate cancer. However, the existing quantitative analysis methods still have challenges in patient-level prediction, including accuracy, interpretability, context understanding, tumor delineation dependence, and multiple sequence fusion. Therefore, we propose a topological graph-guided multi-instance network (GMINet) to catch global contextual information of multi-parametric MRI for patient-level prediction. We integrate visual information from multi-slice MRI with slice-to-slice correlations for a more complete context. A novel strategy of attention folwing is proposed to fuse different MRI-based network branches for mp-MRI. Our method achieves state-of-the-art performance for Prostate cancer on a multi-center dataset (N = 478) and a public dataset (N = 204). The five-classification accuracy of Grade Group is 81.1 ± 1.8% (multi-center dataset) from the test set of five-fold cross-validation, and the area under curve of detecting clinically significant prostate cancer is 0.801 ± 0.018 (public dataset) from the test set of five-fold cross-validation respectively. The model also achieves tumor detection based on attention analysis, which improves the interpretability of the model. The novel method is hopeful to further improve the accurate prediction ability of MRI in the diagnosis and treatment of prostate cancer.

P.32 Impact of age on muscle volume and T2-relaxation time during adulthood in mice using quantitative MRI

Overall ageing is associated with a loss of muscle volume and function over time; nevertheless, detailed changes of muscle tissue are incompletely understood. In this preliminary study, magnetic resonance imaging (MRI) was used to assess the effect of age on muscle volume and T2. A cohort of 13 C57Bl/6J Rj male mice was followed from 2.5 to 17 months of age. MRI acquisitions were done at 7 Tesla using a 1H surface cryoprobe. Muscle volume and T2 of the anterior compartment (TA+EDL: tibialis anterior + extensor digitorum longus) were assessed by cross sectional area (CSA) from high-resolution gradient-echo axial images and from a multi-slice multi echo MRI sequence, respectively. Student's paired t test and ANOVA were used for statistical analysis (p < 0.05). Body weight increased up to 12 months and stayed constant until 17 months of age. Muscle volume reached its maximum at 9 months of age. The maximum CSA of TA+EDL muscles, measured on a single slice, decreased significantly from 9 to 17 months of age (8±0.5 mm² at 9 m/o vs 7.1±0.4 mm² at 17 m/o). In parallel, muscle T2 increased significantly from 2.5 to 17 months of age (T2 at 2.5 m/o = 21.5 ±0.2 ms vs T2 at 17 m/o = 22.6±0.3 ms) and from 2.5 to 6 months of age (T2 at 6 m/o = 22.3±0.4 ms). A similar tendency was also observed between 9 and 17 months of age (T2 at 9 m/o = 22± 0.5; p = 0.054). Repeated acquisitions of high-resolution images allowed for longitudinal follow-up of muscle volume from 2.5 up to 17 months of age in mice. The onset of muscle ageing seems to be around 9 months whereas body weight still increases up to 12 months. Muscle T2 increased significantly with age over the timespan of the study. In young ages (2.5 to 6 m/o), T2 increase probably reflects slight changes of histological water compartmentation related to tissue maturation. Ongoing experiments will refine this longitudinal follow-up and provide data at older ages.

Ferumoxytol-enhanced 4D multiphase, steady-state imaging with magnetic resonance in congenital heart disease: ventricular volume and function across 2D and 3D software platforms.

BackgroundQuantitative ventricular volumetry and function are important in the management of congenital heart disease (CHD). Ferumoxytol-enhanced (FE) 4D multiphase, steady state imaging with contrast enhancement (MUSIC) enables high-resolution, 3D cardiac phase-resolved magnetic resonance imaging (MRI) of the beating heart and extracardiac vessels in a single acquisition and without concerns about renal impairment. We aim to evaluate the semi-automatic quantification of ventricular volumetry and function of 4D MUSIC MRI using 2D and 3D software platforms.MethodsThis HIPAA-compliant and IRB-approved study prospectively recruited 50 children with CHD (3 days to 18 years) who underwent 4D MUSIC MRI at 3.0T between 2013–2017 for clinical indications. Each patient was either intubated in the neonatal intensive care unit (NICU) or underwent general anesthesia at MRI suite. For 2D analysis, we reformatted MUSIC images in Digital Imaging and Communications in Medicine (DICOM) format into ventricular short-axis slices with zero interslice gap. For 3D analysis, we imported DICOMs into a commercially available 3D software platform. Using semi-automatic thresholding, we quantified biventricular volume and ejection fraction (EF). We assessed the bias between MUSIC-derived 2D vs. 3D measurements and correlation between MUSIC vs. conventional 2D balanced steady-state free precession (bSSFP) cine images. We evaluated intra- and inter-observer agreement.ResultsThere was a high degree of correlation between MUSIC-derived volumetric and functional measurements using 2D vs. 3D software (r=0.99, P<0.001). Volumes derived using 3D software platforms were larger than 2D by 0.2 to 2.0 mL/m2 whereas EF measurements were higher by 1.2–3.0%. MUSIC volumetric and functional measures derived from 2D and 3D software platforms corresponded highly with those derived from multi-slice SSFP cine images (r=0.99, P<0.001). The mean difference in volume for reformatted 4D MUSIC relative to bSSFP cine was 1.5 to 3.9 mL/m2. Intra- and inter-observer reliability was excellent.ConclusionsAccurate and reliable ventricular volumetry and function can be derived from FE 4D MUSIC MRI studies using commercially available 2D and 3D software platforms. If fully validated in multicenter studies, the FE 4D-MUSIC pulse sequence may supercede conventional multislice 2D cine cardiovascular MRI acquisition protocols for functional evaluation of children with complex CHD.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.

Correlation between computed tomography, magnetic resonance imaging and cross-sectional anatomy of the head of the guinea pig (Cavia porcellus, Linnaeus 1758).

The current work aimed to study the anatomical features of the guinea pig's head by two medical imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI), and their correlation with the anatomical cross-sectional images. Six adult healthy guinea pigs were used in the present study. Two heads were imaged by CT scanner and then by MRI. The examined heads were cut sagittally and transversely, and two skulls were macerated. The anatomical features were identified on the anatomical sections and compared with the tomographic and MRI images obtained. Data were presented as three-dimensional reconstructed images of the head. In addition, representative combinations of the sagittal and transverse anatomical sections and the corresponding CT scans and MRI images were also presented. Reconstruction of CT images enabled the visualization of different bony structures and airways of the guinea pig head. In addition, skull bones were easily visualized on CT scans, while different parts of the brain were identified on MRI images. Air cavities could be identified by their different contrast on the CT scans and their low intensity on MRI images. The study showed that guinea pig had poorly developed paranasal sinus system represented by rostral and caudal maxillary sinuses. On the contrary, the guinea pig had two tympanic bullae: a small dorsal and a large ventral bulla. In conclusion, this study provides one of the first investigations that uses the multislice CT scans and MRI to study the guinea pig's head and their correlation with the corresponding anatomical sections.

Radial Undersampling-Based Interpolation Scheme for Multislice CSMRI Reconstruction Techniques.

Magnetic Resonance Imaging (MRI) is an important yet slow medical imaging modality. Compressed sensing (CS) theory has enabled to accelerate the MRI acquisition process using some nonlinear reconstruction techniques from even 10% of the Nyquist samples. In recent years, interpolated compressed sensing (iCS) has further reduced the scan time, as compared to CS, by exploiting the strong interslice correlation of multislice MRI. In this paper, an improved efficient interpolated compressed sensing (EiCS) technique is proposed using radial undersampling schemes. The proposed efficient interpolation technique uses three consecutive slices to estimate the missing samples of the central target slice from its two neighboring slices. Seven different evaluation metrics are used to analyze the performance of the proposed technique such as structural similarity index measurement (SSIM), feature similarity index measurement (FSIM), mean square error (MSE), peak signal to noise ratio (PSNR), correlation (CORR), sharpness index (SI), and perceptual image quality evaluator (PIQE) and compared with the latest interpolation techniques. The simulation results show that the proposed EiCS technique has improved image quality and performance using both golden angle and uniform angle radial sampling patterns, with an even lower sampling ratio and maximum information content and using a more practical sampling scheme.

Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction

Resting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruction from simultaneous multislice (SMS) magnetic resonance imaging (MRI) with k-space undersampling (sometimes temporal undersampling), while using prior models on the signal. We propose a novel algorithmic framework to reconstruct R-fMRI (SMS with controlled aliasing) that has, both, k-space undersampling and temporal undersampling. We propose a coupled spatiotemporal sparse model, incorporating (i) a robust spatially-regularized temporal-dictionary prior and (ii) a spatiotemporal wavelet prior, which we fit efficiently using variational Bayesian expectation maximization with nested minorization (VBEMNM). We show that our framework has the potential to enable higher spatial resolution without increasing scan time in R-fMRI that has inherently weak signals and is therefore prone to large physiological fluctuations, acquisition noise, and imaging artifacts. Qualitative and quantitative evaluation on retrospectively undersampled brain R-fMRI shows that estimates of resting-state networks from our framework and the boost in temporal stability given by our framework compares favourably to existing methods for R-fMRI reconstruction.

Objective assessment of patellar maltracking with 3\xa0T dynamic magnetic resonance imaging: feasibility of a robust and reliable measuring technique

Patellofemoral maltracking predisposes the patellofemoral joint to instability. The purpose of this study was to provide a reliable dynamic magnetic resonance imaging (MRI) measuring technique for patellofemoral maltracking, and to investigate the influence of anatomical risk factors (AF) on patellar maltracking. Ten patients (2 males,8 females, average 19 years) with clinical maltracking and 20 controls (10 males,10 females,average 28 years) were examined with a dynamic, multi-slice gradient-echo MRI sequence, during repetitive flexion(40°) and full extension, in an open-chain-active-movement. In a 30-s time frame three simultaneous transverse slices were acquired.Dynamic mediolateral translation and dynamic tilt were measured at defined positions, by two independent examiners. Reproducibility was tested in a set of five knees. Common AFs for patellar maltracking (tibial-tuberosity-to-trochlear-groove-(TT-TG)-distances, trochlea-sulcus-angle, trochlea-sulcus-depth, lateral-trochlear-inclination and Caton-Deschamps-Index) were analyzed in consensus, using standard static MRI sequences. In patients, dynamic mediolateral translation was significantly greater in patients (12.4 ± 6.9 mm vs. − 0.1 ± 2.3 mm, p < 0.001) and the patella was positioned significantly more laterally (17.5 ± 6.9 mm vs. 3.1 ± 2.4 mm, p < 0.001) compared to controls. During movement, the patella tilted 16.3 ± 13.1° laterally in patients and 1.9 ± 4.3° medially in controls (dynamic tilt) (p < 0.002). All AFs were significantly different between patients and controls. Pathological TT-TG-distances, Caton-Deschamps-Indices and trochlea-sulcus-angles strongly correlated with dynamic patellar translation and dynamic patellar tilt (p < 0.001). In the patient population, the primary pathologies for maltracking were lateralized-tibial-tubercle (n = 5), trochlea dysplasia n = 2, patella alta (n = 3). Interrater-reliability for translation and tilt-measurement was excellent (0.971/0.976, 95% CIs 0.939–0.986/0.950–0.988). Dynamic MRI reliably differentiates between abnormal and physiological patellar tracking. Dynamic tracking and tilt strongly correlate with measurable AFs, which reinforces their clinical use and validates the presented technique.

Clinical Analysis of Magnetic Nanoparticle Contrast Agent in the Diagnosis of Occult Fracture by Multislice Spiral CT and MRI

To investigate the differences in the diagnosis and treatment of occult fractures between multi-slice spiral CT (CT) and magnetic resonance imaging (MRI) using I/Fe₃O₄ nanometer contrast agent. This retrospective study analyzed the clinical data of 60 patients with occult fractures and compared the diagnosis results of multislice CT (observation group) and MRI (control group). All the contrast agents used were I/Fe₃O₄ nanometer contrast agents. In the control group, 55 cases (91.67%) were detected on T1-weighted imaging versus 58 cases (96.67) on T2-weighted imaging. In the observation group, 47 cases (78.33%) were diagnosed. The intergroup difference was significant (P < 0.05). Clinically, I/Fe₃O₄ nanometer contrast agent can be used to affect imaging. The detection rate of MRI is higher than that of CT, and the ability to obtain detailed data of occult fractures is better.

Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks.

Segmentation of multiple organs-at-risk (OARs) is essential for magnetic resonance (MR)-only radiation therapy treatment planning and MR-guided adaptive radiotherapy of abdominal cancers. Current practice requires manual delineation that is labor-intensive, time-consuming, and prone to intra- and interobserver variations. We developed a deep learning (DL) technique for fully automated segmentation of multiple OARs on clinical abdominal MR images with high accuracy, reliability, and efficiency. We developed Automated deep Learning-based abdominal multiorgan segmentation (ALAMO) technique based on two-dimensional U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multiview. The model takes in multislice MR images and generates the output of segmentation results. 3.0-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were used in our study and split into 66 for training, 16 for validation, and 20 for testing. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. An experienced radiologist manually labeled each OAR, followed by reediting, if necessary, by a senior radiologist, to create the ground-truth. The performance was measured using volume overlapping and surface distance. The ALAMO technique generated segmentation labels in good agreement with the manual results. Specifically, among the ten OARs, nine achieved high dice similarity coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completed within 1min for a three-dimensional volume of 320×288×180. Overall, the ALAMO model matched the state-of-the-art techniques in performance. The proposed ALAMO technique allows for fully automated abdominal MR segmentation with high accuracy and practical memory and computation time demands.

Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme

Compressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples. Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii) inherently slow data acquisition process. In multi-slice MRI, strong inter-slice correlation has been exploited for further scan time reduction through interpolated compressed sensing (iCS). In this paper, a novel fast interpolated compressed sensing (FiCS) technique is proposed based on 2D variable density under-sampling (VRDU) scheme. The 2D-VRDU scheme improves the result by sampling the high energy central part of the k-space slices. The novel interpolation technique takes two consecutive slices and estimates the missing samples of the target slice (T slice) from its left slice (L slice). Compared to the previous methods, slices recovered with the proposed FiCS technique have a maximum correlation with their corresponding original slices. The proposed FiCS technique is evaluated by using both subjective and objective assessment. In subjective assessment, our proposed technique shows less partial volume loss compared to existing techniques. For objective assessment different performance metrics, such as structural similarity index measurement (SSIM), peak signal to noise ratio (PSNR), mean square error (MSE) and correlation, are used and compared with existing interpolation techniques. Simulation results on knee and brain dataset shows that the proposed FiCS technique has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content.

Accelerating multi-slice spatiotemporally encoded MRI with simultaneous echo refocusing

Single-shot spatiotemporally encoded (SPEN) ultrafast magnetic resonance imaging (MRI) is of great value to both scientific research and clinical application owing to its capability for delivering MR images with greater robustness to magnetic field inhomogeneity and chemical-shift displacement effects than conventional methods like EPI due to high effective phase-encoded bandwidth. Many SPEN MRI methods have been developed, among which multi-slice SPEN MRI arises as a promising supplement to ultrafast multi-slice sampling. In this work, we propose a new multi-slice SPEN MRI method, termed multi-echo segmented SPEN (ME-SeSPEN) method, which produces multiple images within a single train of echoes and successively samples widely separated slices. The resulting images were reconstructed using de-convolution super-resolved algorithm. The robustness and efficiency of the proposed method were demonstrated by phantom, lemon and in vivo experiments in comparison with spin-echo EPI, spin-echo simultaneous echo refocusing (SER), and segmented SPEN (SeSPEN) MRI. The results indicate that the new method effectively shortens the sampling time (20% reduction practically in comparison with SeSPEN when two slices are simultaneously sampled). ME-SeSPEN also reduces eddy current effects while maintaining the benefits of SPEN MRI, such as similar robustness to field inhomogeneity, spatial resolution and signal-to-noise ratio to SeSPEN MRI. The new method will promote the versatility of multi-slice MRI in practical applications.

Study on the diagnosis of small hepatocellular carcinoma caused by hepatitis B cirrhosis via multi-slice spiral CT and MRI.

The present study compared the diagnostic accuracy of multi-slice spiral computed tomography (CT) and magnetic resonance imaging (MRI) on small hepatocellular carcinoma (SHCC) caused by hepatitis B cirrhosis. A total of 160 patients with hepatitis B cirrhosis were selected between January 2012 and April 2016, and 183 SHCC lesions were included in the present retrospective study. Patients were divided into the SHCC group (T stage) and the micro hepatocellular carcinoma (MHCC) group (T1 stage). There were a total of 129 SHCC lesions and 54 MHCC lesions identified. All patients underwent multiphasic CT and MRI imaging. The liver acquisition with volume acquisition (LAVA) technique was utilized for MRI. Furthermore, SPSS 20.0 was used for statistical analyses. LAVA in the arterial phase and CT in the arterial phase revealed significantly higher diagnostic rates for the diagnoses of 183 lesions. In addition, standard CT scan exhibited significantly reduced diagnostic rates in SHCC lesions. Results indicated that LAVA in the equilibrium phase had the lowest diagnostic rate in MHCC lesions, which was statistically significant (P<0.05). Overall, the diagnostic rate of CT (79.63%) for MHCC was significantly lower than that of MRI (96.29%) (P<0.05). However, the diagnostic rate of CT for SHCC (96.12%) was significantly higher than that for MHCC (79.63%) (P<0.05). MRI-LAVA in the arterial phase has the highest diagnostic rate for SHCC and MHCC. However, the diagnostic capability of MRI for MHCC lesions is superior to that of CT.

Improved simultaneous multislice magnetic resonance imaging using total variation regularization**Project supported by the National Natural Science Foundation of China (Grant No. 61671026) and the Natural Science Foundation of Beijing, China (Grant No. 7162112).

Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) for simultaneous multislice imaging has been proposed recently, which combines multiband excitation and phase cycling techniques to reduce scan time and improve subsequent imaging reconstruction. In this work, the total variation (TV) regularization method is used to further improve CAIPIRINHA. The TV regularization uses an edge-preserving prior, which establishes a relationship between neighboring pixels for image reconstruction. It reduces artifacts and suppresses noise amplification simultaneously. The results are presented with a standard eight-channel head coil with an acceleration factor of 4, where the TV-regularized CAIPIRINHA generates an improved reconstruction as compared with a typical nonregularized CAIPIRINHA.

Magnetic resonance image reconstruction using fast interpolated compressed sensing

Application of the compressed sensing (CS) in magnetic resonance imaging (MRI) significantly reduces the scan time as it is capable of recovering images at diagnostic resolution from a few random measurements in the k-space. In 2D multi-slice MRI, a strong inter-slice correlation exists which promotes further reduction in the scan time. In this paper, first, a fast interpolation technique is proposed to estimate missing samples in the k-space of some highly undersampled slices from neighboring low undersampled slices. Next, a novel wavelet tree sparsity based CS reconstruction method is developed for the reconstruction of images from raw 2D multi-slice data. The combination of fast interpolation and CS reconstruction gives accurate reconstruction of multi-slice MR images at significantly less computational time. Simulation results show that the proposed method gives better preservation of edges and other image details with less computational time than some of the recently developed fast CS reconstruction techniques.

A specific absorption rate reduction method for simultaneous multislice magnetic resonance imaging.

This study proposes a modified Shinnar-Le Roux method to synthesize the excitation radio frequency (RF) pulse for a 2D gradient echo (GRE) based simultaneous multi-slice (SMS) magnetic resonance imaging (MRI) with features of low specific absorption rate (SAR) and small out-of-slice ripple. This synthesis method for SMS RF pulses employs thinner slice bandwidth and lower multislice offset frequencies to reduce SAR values and adopts a weighted Parks-McClellan algorithm to reduce sidelobes. Formulas for estimating relative SAR values of the SMS pulses are also introduced. Relative SAR values and out-of-slice ripples of the proposed and typical RF pulses with different parameters are presented. In simultaneous 5-slice phantom and 3-slice human brain imaging, SMS pulses synthesized with the proposed method achieve 32% and 28% SAR values of standard pulses while providing similar image qualities. Typical RF pulses such as sinc x cos can also take advantage of the proposed method and offer lower SAR values for SMS imaging. The RF pulse synthesized using the proposed method features low SAR, small sidelobes, and consistent image quality for 2D GRE-based SMS MRI. This method is applicable to the synthesis of typical SMS RF pulses for significant SAR reduction.

3D MODELLING AND RAPID PROTOTYPING FOR CARDIOVASCULAR SURGICAL PLANNING – TWO CASE STUDIES

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice.