Low-rank reconstruction for simultaneous double half-echo 23Na and undersampled 23Na multi-quantum coherences MRI.

PURPOSE: To develop a new sequence to simultaneously acquire Cartesian sodium (23Na) MRI and accelerated Cartesian single (SQ) and triple quantum (TQ) sodium MRI of in vivo human brain at 7 T by leveraging two dedicated low-rank reconstruction frameworks. THEORY AND METHODS: The Double Half-Echo technique enables short echo time Cartesian 23Na MRI and acquires two k-space halves, reconstructed by a low-rank coupling constraint. Additionally, three-dimensional (3D) 23Na Multi-Quantum Coherences (MQC) MRI requires multi-echo sampling paired with phase-cycling, exhibiting a redundant multidimensional space. Simultaneous Autocalibrating and k-Space Estimation (SAKE) were used to reconstruct highly undersampled 23Na MQC MRI. Reconstruction performance was assessed against five-dimensional (5D) CS, evaluating structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR), and quantification of tissue sodium concentration and TQ/SQ ratio in silico, in vitro, and in vivo. RESULTS: The proposed sequence enabled the simultaneous acquisition of fully sampled 23Na MRI while leveraging prospective undersampling for 23Na MQC MRI. SAKE improved TQ image reconstruction regarding SSIM by 6% and reduced RMSE by 35% compared to 5D CS in vivo. Thanks to prospective undersampling, the spatial resolution of 23Na MQC MRI was enhanced from 8 × 8 × 15 $$ 8\times 8\times 15 $$ mm3 to 8 × 8 × 8 $$ 8\times 8\times 8 $$ mm3 while reducing acquisition time from 2 × 31 $$ 2\times 31 $$ min to 2 × 23 $$ 2\times 23 $$ min. CONCLUSION: The proposed sequence, coupled with low-rank reconstructions, provides an efficient framework for comprehensive whole-brain sodium MRI, combining TSC, T2*, and TQ/SQ ratio estimations. Additionally, low-rank matrix completion enables the reconstruction of highly undersampled 23Na MQC MRI, allowing for accelerated acquisition or enhanced spatial resolution.

Sodium Triple Quantum MR Signal Extraction Using a Single-Pulse Sequence with Single Quantum Time Efficiency.

PURPOSE: Sodium triple quantum (TQ) signal has been shown to be a valuable biomarker for cell viability. Despite its clinical potential, application of Sodium TQ signal is hindered by complex pulse sequences with long scan times. This study proposes a method to approximate the TQ signal using a single excitation pulse without phase cycling. METHODS: The proposed method is based on a single excitation pulse and a comparison of the free induction decay (FID) with the integral of the FID combined with a shifting reconstruction window. The TQ signal is calculated from this FID only. As a proof of concept, the method was also combined with a multi-echo UTE imaging sequence on a 9.4 T preclinical MRI scanner for the possibility of fast TQ MRI. RESULTS: The extracted Sodium TQ signals of single-pulse and spin echo FIDs were in close agreement with theory and TQ measurement by traditional three-pulse sequence (TQ time proportional phase increment [TQTPPI)]. For 2%, 4%, and 6% agar samples, the absolute deviations of the maximum TQ signals between SE and theoretical (time proportional phase increment TQTPPI) TQ signals were less than 1.2% (2.4%), and relative deviations were less than 4.6% (6.8%). The impact of multi-compartment systems and noise on the accuracy of the TQ signal was small for simulated data. The systematic error was <3.4% for a single quantum (SQ) SNR of 5 and at maximum <2.5% for a multi-compartment system. The method also showed the potential of fast in vivo SQ and TQ imaging. CONCLUSION: Simultaneous SQ and TQ MRI using only a single-pulse sequence and SQ time efficiency has been demonstrated. This may leverage the full potential of the Sodium TQ signal in clinical applications.

Comparison of triple quantum (TQ) TPPI and inversion recovery TQ TPPI pulse sequences at 9.4 and 21.1 T.

PURPOSE: Both sodium T1 triple quantum (TQ) signal and T1 relaxation pathways have a unique sensitivity to the sodium molecular environment. In this study an inversion recovery time proportional phase increment (IRTQTPPI) pulse sequence was investigated for simultaneous and reliable quantification of sodium TQ signal and bi-exponential T1 relaxation times. METHODS: The IRTQTPPI sequence combines inversion recovery TQ filtering and time proportional phase increment. The reliable and reproducible results were achieved by the pulse sequence optimized in three ways: (1) optimization of the nonlinear fit for the determination of both T1-TQ signal and T1 relaxation times; (2) suppression of unwanted signals by assessment of four different phase cycles; (3) nonlinear sampling during evolution time for optimal scan time without any compromises in fit accuracy. The relaxation times T1 and T2 and the TQ signals from IRTQTPPI and TQTPPI were compared between 9.4 and 21.1 T. The motional environment of the sodium nuclei was evaluated by calculation of correlation times and nuclear quadrupole interaction strengths. RESULTS: Reliable measurements of the T1-TQ signals and T1 bi-exponential relaxation times were demonstrated. The fit parameters for all four phase cycles were in good agreement with one another, with a negligible influence of unwanted signals. The agar samples yielded normalized T1-TQ signals from 3% to 16% relative to single quantum (SQ) signals at magnetic fields of both 9.4 and 21.1 T. In comparison, the normalized T2-TQ signal was in the range 15%-35%. The TQ/SQ signal ratio was decreased at 21.1 T as compared with 9.4 T for both T1 and T2 relaxation pathways. The bi-exponential T1 relaxation time separation ranged from 15 to 18 ms at 9.4 T and 15 to 21 ms at 21.1 T. The T2 relaxation time separation was larger, ranging from 28 to 35 ms at 9.4 T and 37 to 40 ms at 21.1 T. CONCLUSION: The IRTQTPPI sequence, while providing a less intensive TQ signal than TQTPPI, allows a simultaneous and reliable quantification of both the T1-TQ signal and T1 relaxation times. The unique sensitivities of the T1 and T2 relaxation pathways to different types of molecular motion provide a deeper understanding of the sodium MR environment.

Feasibility of undersampled spiral trajectories in MREPT for fast conductivity imaging.

PURPOSE: To investigate spiral-based imaging including trajectories with undersampling as a fast and robust alternative for phase-based magnetic resonance electrical properties tomography (MREPT) techniques. METHODS: Spiral trajectories with various undersampling ratios were prescribed to acquire images from an experimental phantom and a healthy volunteer at 3T. The non-Cartesian acquisitions were reconstructed using SPIRiT, and conductivity maps were derived using phase-based cr-MREPT. The resulting maps were compared between different sampling trajectories. Additionally, a conductivity map was obtained using a Cartesian balanced SSFP acquisition from the volunteer to comparatively demonstrate the robustness of the proposed method. RESULTS: The phantom and volunteer results illustrate the benefits of the spiral acquisitions. Specifically, undersampled spiral acquisitions display improved robustness against field inhomogeneity artifacts and lowered SD values with shortened readout times. Furthermore, average of conductivity values measured for the cerebrospinal fluid with the spiral acquisitions were 1.703 S/m, indicating a close agreement with the theoretical values of 1.794 S/m. CONCLUSION: A spiral-based acquisition framework for conductivity imaging with and without undersampling is presented. Overall, spiral-based acquisitions improved robustness against field inhomogeneity artifacts, while achieving whole head coverage with multiple averages in less than a minute.

Multidimensional compressed sensing to advance 23 Na multi-quantum coherences MRI.

PURPOSE: Sodium (23 Na) multi-quantum coherences (MQC) MRI was accelerated using three-dimensional (3D) and a dedicated five-dimensional (5D) compressed sensing (CS) framework for simultaneous Cartesian single (SQ) and triple quantum (TQ) sodium imaging of in vivo human brain at 3.0 and 7.0 T. THEORY AND METHODS: 3D 23 Na MQC MRI requires multi-echo paired with phase-cycling and exhibits thus a multidimensional space. A joint reconstruction framework to exploit the sparsity in all imaging dimensions by extending the conventional 3D CS framework to 5D was developed. 3D MQC images of simulated brain, phantom and healthy brain volunteers obtained from 3.0 T and 7.0 T were retrospectively and prospectively undersampled. Performance of the CS models were analyzed by means of structural similarity index (SSIM), root mean squared error (RMSE), signal-to-noise ratio (SNR) and signal quantification of tissue sodium concentration and TQ/SQ ratio. RESULTS: It was shown that an acceleration of three-fold, leading to less than 2 × 10 $$ 2\times 10 $$ min of scan time with a resolution of 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ at 3.0 T, are possible. 5D CS improved SSIM by 3%, 5%, 1% and reduced RMSE by 50%, 30%, 8% for in vivo SQ, TQ, and TQ/SQ ratio maps, respectively. Furthermore, for the first time prospective undersampling enabled unprecedented high resolution from 8 × 8 × 20 mm 3 $$ 8\times 8\times 20\;{\mathrm{mm}}^3 $$ to 6 × 6 × 10 mm 3 $$ 6\times 6\times 10\;{\mathrm{mm}}^3 $$ MQC images of in vivo human brain at 7.0 T without extending acquisition time. CONCLUSION: 5D CS proved to allow up to three-fold acceleration retrospectively on 3.0 T data. 2-fold acceleration was demonstrated prospectively at 7.0 T to reach higher spatial resolution of 23 Na MQC MRI.

Improved reproducibility for myocardial ASL: Impact of physiological and acquisition parameters.

PURPOSE: To investigate and mitigate the influence of physiological and acquisition-related parameters on myocardial blood flow (MBF) measurements obtained with myocardial Arterial Spin Labeling (myoASL). METHODS: A Flow-sensitive Alternating Inversion Recovery (FAIR) myoASL sequence with bSSFP and spoiled GRE (spGRE) readout is investigated for MBF quantification. Bloch-equation simulations and phantom experiments were performed to evaluate how variations in acquisition flip angle (FA), acquisition matrix size (AMS), heart rate (HR) and blood T 1 $$ {\mathrm{T}}_1 $$ relaxation time ( T 1 , B $$ {\mathrm{T}}_{1,B} $$ ) affect quantification of myoASL-MBF. In vivo myoASL-images were acquired in nine healthy subjects. A corrected MBF quantification approach was proposed based on subject-specific T 1 , B $$ {\mathrm{T}}_{1,B} $$ values and, for spGRE imaging, subtracting an additional saturation-prepared baseline from the original baseline signal. RESULTS: Simulated and phantom experiments showed a strong dependence on AMS and FA ( R 2 $$ {R}^2 $$ >0.73), which was eliminated in simulations and alleviated in phantom experiments using the proposed saturation-baseline correction in spGRE. Only a very mild HR dependence ( R 2 $$ {R}^2 $$ >0.59) was observed which was reduced when calculating MBF with individual T 1 , B $$ {\mathrm{T}}_{1,B} $$ . For corrected spGRE, in vivo mean global spGRE-MBF ranged from 0.54 to 2.59 mL/g/min and was in agreement with previously reported values. Compared to uncorrected spGRE, the intra-subject variability within a measurement (0.60 mL/g/min), between measurements (0.45 mL/g/min), as well as the inter-subject variability (1.29 mL/g/min) were improved by up to 40% and were comparable with conventional bSSFP. CONCLUSION: Our results show that physiological and acquisition-related factors can lead to spurious changes in myoASL-MBF if not accounted for. Using individual T 1 , B $$ {\mathrm{T}}_{1,B} $$ and a saturation-baseline can reduce these variations in spGRE and improve reproducibility of FAIR-myoASL against acquisition parameters.

Functional lung imaging of 2-year-old children after congenital diaphragmatic hernia repair using dynamic mode decomposition MRI.

OBJECTIVES: To investigate the feasibility of non-contrast-enhanced functional lung imaging in 2-year-old children after congenital diaphragmatic hernia (CDH) repair. METHODS: Fifteen patients after CDH repair were examined using non-contrast-enhanced dynamic magnetic resonance imaging (MRI). For imaging two protocols were used during free-breathing: Protocol A with high temporal resolution and Protocol B with high spatial resolution. The dynamic images were then analysed through a recently developed post-processing method called dynamic mode decomposition (DMD) to obtain ventilation and perfusion maps. The ventilation ratios (VRatio) and perfusion ratios (QRatio) of ipsilateral to contralateral lung were compared to evaluate functional differences. Lastly, DMD MRI-based perfusion results were compared with perfusion parameters obtained using dynamic contrast-enhanced (DCE) MRI to assess agreement between methods. RESULTS: Both imaging protocols successfully generated pulmonary ventilation (V) and perfusion (Q) maps in all patients. Overall, the VRatio and QRatio values were 0.84 ± 0.19 and 0.70 ± 0.24 for Protocol A, and 0.88 ± 0.18 and 0.72 ± 0.23 for Protocol B, indicating reduced ventilation ([Formula: see text]) and perfusion ([Formula: see text]) on the ipsilateral side. Moreover, there is a very strong positive correlation ([Formula: see text]) and close agreement between DMD MRI-based perfusion values and DCE MRI-based perfusion parameters. CONCLUSIONS: DMD MRI can obtain pulmonary functional information in 2-year-old CDH patients. The results obtained with DMD MRI correlate with DCE MRI, without the need for ionising radiation or exposure to contrast agents. While further studies with larger cohorts are warranted, DMD MRI is a promising option for functional lung imaging in CDH patients. CLINICAL RELEVANCE STATEMENT: We demonstrate that pulmonary ventilation and perfusion information can be obtained in 2-year-old patients after CDH repair, without the need for ionising radiation or contrast agents by utilising non-contrast-enhanced MRI acquisitions together with dynamic mode decomposition analysis. KEY POINTS: • Non-contrast-enhanced functional MR imaging is a promising option for functional lung imaging in 2-year-old children after congenital diaphragmatic hernia. • DMD MRI can generate pulmonary ventilation and perfusion maps from free-breathing dynamic acquisitions without the need for ionising radiation or contrast agents. • Lung perfusion parameters obtained with DMD MRI correlate with perfusion parameters obtained using dynamic contrast-enhanced MRI.

VirtMRI: A Tool for Teaching MRI.

Magnetic resonance image formation is not trivial and remains a difficult subject for teaching. Therefore, we saw an urgent need to facilitate teaching by developing a practical and easily accessible MR image generator. Due to the increasing interest in X-nuclei MRI, sodium image generation is also offered. The tool is implemented as a web application that is compatible with all standard desktop browsers and is open source. The user interface focuses on the parameters needed for the creation and display of the resulting images. Available MR sequences range from the standard Spin Echo and Inversion Recovery over steady-state to conventional sodium and more advanced single and triple quantum sequences. Additionally, the user interface has parameters to alter the resolution, the noise, and the k-space sampling. Our software is free to use and specifically suited for teaching purposes.

Prediction of experimental cardiac magnetostimulation thresholds using pig-specific body models.

PURPOSE: Modern high-amplitude gradient systems can be limited by the International Electrotechnical Commission 60601-2-33 cardiac stimulation (CS) limit, which was set in a conservative manner based on electrode experiments and E-field simulations in uniform ellipsoidal body models. Here, we show that coupled electromagnetic-electrophysiological modeling in detailed body and heart models can predict CS thresholds, suggesting that such modeling might lead to more detailed threshold estimates in humans. Specifically, we compare measured and predicted CS thresholds in eight pigs. METHODS: We created individualized porcine body models using MRI (Dixon for the whole body, CINE for the heart) that replicate the anatomy and posture of the animals used in our previous experimental CS study. We model the electric fields induced along cardiac Purkinje and ventricular muscle fibers and predict the electrophysiological response of these fibers, yielding CS threshold predictions in absolute units for each animal. Additionally, we assess the total modeling uncertainty through a variability analysis of the 25 main model parameters. RESULTS: Predicted and experimental CS thresholds agree within 19% on average (normalized RMS error), which is smaller than the 27% modeling uncertainty. No significant difference was found between the modeling predictions and experiments (p < 0.05, paired t-test). CONCLUSION: Predicted thresholds matched the experimental data within the modeling uncertainty, supporting the model validity. We believe that our modeling approach can be applied to study CS thresholds in humans for various gradient coils, body shapes/postures, and waveforms, which is difficult to do experimentally.

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions - A comparative study on generalizability.

Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks' performance and the networks' generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value < 0.05) and nine networks improved the pre-registration Dice coefficient of the patient dataset significantly and are therefore able to generalize to the new datasets used in our experiments. Many different machine learning-based methods have been proposed for affine multimodal medical image registration, but few are generalizable to new data and applications. It is therefore necessary to conduct further research in order to develop medical image registration techniques that can be applied more widely.

Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion.

PURPOSE: To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non-contrast-enhanced acquisitions. METHODS: Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms as well as in vivo dynamic acquisitions of healthy volunteers at 1.5T. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values. The proposed method was evaluated based on estimated signal amplitude in functional maps across varying number of measurements. RESULTS: Quantitative assessments performed on phantoms and in vivo measurements indicate that DMD is capable of successfully obtaining pulmonary functional maps. Specifically, compared to FD and MP methods, DMD is able to reduce variations in estimated amplitudes across different number of measurements. This improvement is evident in the fractional ventilation and normalized perfusion maps obtain from phantom simulations with frequency variations and noise, as well as in the maps obtained from in vivo measurements. CONCLUSIONS: A robust method for accurately estimating pulmonary ventilation and perfusion related signal changes in dynamic acquisitions is presented. The proposed method uses DMD to obtain functional maps reliably, while reducing amplitude variations caused by differences in number of measurements.

Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline.

Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80-0.93, 0.78-0.89, and 0.64-0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.

Measurement of magnetostimulation thresholds in the porcine heart.

PURPOSE: Powerful MRI gradient systems can surpass the International Electrotechnical Commission (IEC) 60601-2-33 limit for cardiac stimulation (CS), which was determined by simple electromagnetic simulations and electrode stimulation experiments. Only a few canine studies measured magnetically induced CS thresholds in vivo and extrapolating them to human safety limits can be challenging. METHODS: We measured cardiac magnetostimulation thresholds in 10 healthy, anesthetized pigs using capacitors discharged into a flat spiral coil to produce damped sinusoidal waveforms with effective stimulus duration ts,eff  = 0.45 ms. Electrocardiography (ECG), blood pressure, and peripheral oximetry signals were recorded to determine threshold coil currents yielding cardiac capture. Dixon and CINE MR volumes from each animal were segmented to generate porcine-specific electromagnetic models to calculate dB/dt and E-field values in the porcine heart at threshold. For comparison, we also simulated maximum dB/dt and E-field values created by three MRI gradient systems in the heart of a human body model. RESULTS: The average dB/dt threshold estimated in the porcine heart was 1.66 ± 0.23 kT/s, which is 11-fold greater than the IEC dB/dt limit at ts,eff  = 0.45 ms, and 31-fold greater than the maximum value created by the investigated MRI gradients in the human heart. The average E-field threshold estimated in the porcine heart was 92.9 ± 13.5 V/m, which is 6-fold greater than the IEC E-field limit at ts,eff  = 0.45 ms and 37-fold greater than the maximum gradient-induced E-field in the human heart. CONCLUSION: This first measurement of cardiac magnetostimulation thresholds in pigs indicates that the IEC cardiac safety limit is conservative for the investigated stimulus duration (ts,eff  = 0.45 ms).

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

Phase-cycled balanced SSFP imaging for non-contrast-enhanced functional lung imaging.

PURPOSE: To introduce phase-cycled balanced SSFP (bSSFP) acquisition as an alternative in Fourier decomposition MRI for improved robustness against field inhomogeneities. METHODS: Series 2D dynamic lung images were acquired in 5 healthy volunteers at 1.5 T and 3 T using bSSFP sequence with multiple RF phase increments and compared with conventional single RF phase increment acquisitions. The approach was evaluated based on functional map homogeneity analysis, while ensuring image and functional map quality by means of SNR and contrast-to-noise ratio analyses. RESULTS: At both field strengths, functional maps obtained with phase-cycled acquisitions displayed improved robustness against local signal losses compared with single-phase acquisitions. The coefficient of variation (mean ± SD, across volunteers) measured in the ventilation maps resulted in 29.7 ± 2.6 at 1.5 T and 37.5 ± 3.1 at 3 T for phase-cycled acquisitions, compared with 39.9 ± 5.2 at 1.5 T and 49.5 ± 3.7 at 3 T for single-phase acquisitions, indicating a significant improvement ( p<0.05$$ p<0.05 $$ ) in ventilation map homogeneity. CONCLUSIONS: Phase-cycled bSSFP acquisitions improve robustness against field inhomogeneity artifacts and significantly improve ventilation map homogeneity at both field strengths. As such, phase-cycled bSSFP may serve as a robust alternative in lung function assessments.

Development of an abdominal phantom for the validation of an oligometastatic disease diagnosis workflow.

PURPOSE: The liver is a common site for metastatic disease, which is a challenging and life-threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)-guided biopsy for subsequent molecular subtyping. By combining morphological, molecular, and functional information about the tumor, a patient-specific treatment planning is possible. We designed and manufactured an abdominal liver phantom that we used to demonstrate multimodal image acquisition, image processing, and biopsy of the OMD diagnosis workflow. METHODS: The anthropomorphic abdominal phantom contains a rib cage, a portal vein, lungs, a liver with six lesions, and a hepatic vessel tree. This phantom incorporates three different lesion types with varying visibility under computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography CT (PET-CT), which reflects clinical reality. The phantom is puncturable and the size of the corpus and the organs is comparable to those of a real human abdomen. By using several modern additive manufacturing techniques, the manufacturing process is reproducible and allows to incorporate patient-specific anatomies. As a first step of the OMD diagnosis workflow, a preinterventional CT, MRI, and PET-CT data set of the phantom was acquired. The image information was fused using image registration and organ information was extracted via image segmentation. A CBCT-guided needle puncture experiment was performed, where all six liver lesions were punctured with coaxial biopsy needles. RESULTS: Qualitative observation of the image data and quantitative evaluation using contrast-to-noise ratio (CNR) confirms that one lesion type is visible only in MRI and not CT. The other two lesion types are visible in CT and MRI. The CBCT-guided needle placement was performed for all six lesions, including those visible only in MRI and not CBCT. This was possible by successfully merging multimodal preinterventional image data. Lungs, bones, and liver vessels serve as realistic inhibitions during needle path planning. CONCLUSIONS: We have developed a reusable abdominal phantom that has been used to validate a standardized OMD diagnosis workflow. Utilizing the phantom, we have been able to show that a multimodal imaging pipeline is advantageous for a comprehensive detection of liver lesions. In a CBCT-guided needle placement experiment we have punctured lesions that are invisible in CBCT using registered preinterventional MRI scans for needle path planning.

Feature-based CBCT self-calibration for arbitrary trajectories.

PURPOSE: Development of an algorithm to self-calibrate arbitrary CBCT trajectories which can be used to reduce metal artifacts. By using feature detection and matching we want to reduce the amount of parameters for the BFGS optimization and thus reduce the runtime. METHODS: Each projection is 2D-3D registered on a prior image with AKAZE feature detection and brute force matching. Translational misalignment is calculated directly from the misalignment of feature positions, rotations are aligned using a minimization algorithm that fits a quartic function and determines the minimum of this function. EVALUATION: We did three experiments to compare how well the algorithm can handle noise on the different degrees of freedom. Our algorithms are compared to Broyden-Fletcher-Goldfarb-Shanno (BFGS) minimizer with Normalized Gradient Information (NGI) objective function, and BFGS with distance between features objective function using SSIM, nRMSE, and the Dice coefficient of segmented metal object. RESULTS: Our algorithm (Feature ORiented Calibration for Arbitrary Scan Trajectories with Enhanced Reliability (FORCASTER)) performs on par with the state-of-the-art algorithms (BFGS with NGI objective). nRMSE: FORCASTER = 0.3390, BFGS+NGI = 0.3441; SSIM: FORCASTER = 0.83, BFGS + NGI = 0.79; Dice: FORCASTER = 0.86, BFGS + NGI = 0.87. CONCLUSION: The proposed algorithm can determine the parameters of the projection orientations for arbitrary trajectories with calibration quality comparable to state-of-the-art algorithms, but faster and with higher tolerance to errors in the initially guessed parameters.

Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization.

Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.