Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.

PURPOSE: Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine learning has proven its value in medical image segmentation, including the kidneys. This study presents a fully automatic workflow for renal cortex perfusion quantification by including machine learning-based segmentation. METHODS: Fully automatic workflow was achieved by construction of a cascade of 3 U-nets to replace manual segmentation in ASL quantification. All 1.5T ASL-MRI data, including M0 , T1 , and ASL label-control images, from 10 healthy volunteers was used for training (dataset 1). Trained cascade performance was validated on 4 additional volunteers (dataset 2). Manual segmentations were generated by 2 observers, yielding reference and second observer segmentations. To validate the intended use of the automatic segmentations, manual and automatic RBF values in mL/min/100 g were compared. RESULTS: Good agreement was found between automatic and manual segmentations on dataset 1 (dice score = 0.78 ± 0.04), which was in line with inter-observer variability (dice score = 0.77 ± 0.02). Good agreement was confirmed on dataset 2 (dice score = 0.75 ± 0.03). Moreover, similar cortical RBF was obtained with automatic or manual segmentations, on average and at subject level; with 211 ± 31 mL/min/100 g and 208 ± 31 mL/min/100 g (P < .05), respectively, with narrow limits of agreement at -11 and 4.6 mL/min/100 g. RBF accuracy with automated segmentations was confirmed on dataset 2. CONCLUSION: Our proposed method automates ASL quantification without compromising RBF accuracy. With quick processing and without observer dependence, renal ASL-MRI is more attractive for clinical application as well as for longitudinal and multi-center studies.

Magnetic Resonance Imaging Versus Computed Tomography for Three-Dimensional Bone Imaging of Musculoskeletal Pathologies: A Review.

Magnetic resonance imaging (MRI) is increasingly utilized as a radiation-free alternative to computed tomography (CT) for the diagnosis and treatment planning of musculoskeletal pathologies. MR imaging of hard tissues such as cortical bone remains challenging due to their low proton density and short transverse relaxation times, rendering bone tissues as nonspecific low signal structures on MR images obtained from most sequences. Developments in MR image acquisition and post-processing have opened the path for enhanced MR-based bone visualization aiming to provide a CT-like contrast and, as such, ease clinical interpretation. The purpose of this review is to provide an overview of studies comparing MR and CT imaging for diagnostic and treatment planning purposes in orthopedic care, with a special focus on selective bone visualization, bone segmentation, and three-dimensional (3D) modeling. This review discusses conventional gradient-echo derived techniques as well as dedicated short echo time acquisition techniques and post-processing techniques, including the generation of synthetic CT, in the context of 3D and specific bone visualization. Based on the reviewed literature, it may be concluded that the recent developments in MRI-based bone visualization are promising. MRI alone provides valuable information on both bone and soft tissues for a broad range of applications including diagnostics, 3D modeling, and treatment planning in multiple anatomical regions, including the skull, spine, shoulder, pelvis, and long bones. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Synthetic CT for the planning of MR-HIFU treatment of bone metastases in pelvic and femoral bones: a feasibility study.

OBJECTIVES: Visualization of the bone distribution is an important prerequisite for MRI-guided high-intensity focused ultrasound (MRI-HIFU) treatment planning of bone metastases. In this context, we evaluated MRI-based synthetic CT (sCT) imaging for the visualization of cortical bone. METHODS: MR and CT images of nine patients with pelvic and femoral metastases were retrospectively analyzed in this study. The metastatic lesions were osteolytic, osteoblastic or mixed. sCT were generated from pre-treatment or treatment MR images using a UNet-like neural network. sCT was qualitatively and quantitatively compared to CT in the bone (pelvis or femur) containing the metastasis and in a region of interest placed on the metastasis itself, through mean absolute difference (MAD), mean difference (MD), Dice similarity coefficient (DSC), and root mean square surface distance (RMSD). RESULTS: The dataset consisted of 3 osteolytic, 4 osteoblastic and 2 mixed metastases. For most patients, the general morphology of the bone was well represented in the sCT images and osteolytic, osteoblastic and mixed lesions could be discriminated. Despite an average timespan between MR and CT acquisitions of 61 days, in bone, the average (± standard deviation) MAD was 116 ± 26 HU, MD - 14 ± 66 HU, DSC 0.85 ± 0.05, and RMSD 2.05 ± 0.48 mm and, in the lesion, MAD was 132 ± 62 HU, MD - 31 ± 106 HU, DSC 0.75 ± 0.2, and RMSD 2.73 ± 2.28 mm. CONCLUSIONS: Synthetic CT images adequately depicted the cancellous and cortical bone distribution in the different lesion types, which shows its potential for MRI-HIFU treatment planning. KEY POINTS: • Synthetic computed tomography was able to depict bone distribution in metastatic lesions. • Synthetic computed tomography images intrinsically aligned with treatment MR images may have the potential to facilitate MR-HIFU treatment planning of bone metastases, by combining visualization of soft tissues and cancellous and cortical bone.

Perfusion imaging of neuroblastoma and nephroblastoma in a paediatric population using pseudo-continuous arterial spin-labelling magnetic resonance imaging.

OBJECTIVES: To examine the feasibility of performing ASL-MRI in paediatric patients with solid abdominal tumours. METHODS: Multi-delay ASL data sets were acquired in ten paediatric patients diagnosed with either a neuroblastoma (n = 4) or nephroblastoma (n = 6) during a diagnostic MRI examination at a single visit (n = 4 at initial staging, n = 2 neuroblastoma and n = 2 nephroblastoma patients; n = 6 during follow-up, n = 2 neuroblastoma and n = 4 nephroblastoma patients). Visual evaluation and region-of-interest (ROI) analyses were performed on the processed perfusion-weighted images to assess ASL perfusion signal dynamics in the whole tumour, contralateral kidney, and tumour sub-regions with/without contrast enhancement. RESULTS: The majority of the included abdominal tumours presented with relatively low perfusion-weighted signal (PWS), especially compared with the highly perfused kidneys. Within the tumours, regions with high PWS were observed which, at short PLD, are possibly related to labelled blood inside vessels and at long PLD, reflect labelled blood accumulating inside tumour tissue over time. Conversely, comparison of ASL perfusion-weighted image findings with T1w enhancement after contrast administration showed that regions lacking contrast enhancement also were void of PWS. DISCUSSION: This pilot study demonstrates the feasibility of utilizing ASL-MRI in paediatric patients with solid abdominal tumours and provides a basis for further research on non-invasive perfusion measurements in this study population.

Exploring label dynamics of velocity-selective arterial spin labeling in the kidney.

PURPOSE: Velocity-selective arterial spin labeling (VSASL) has been proposed for renal perfusion imaging to mitigate planning challenges and effects of arterial transit time (ATT) uncertainties. In VSASL, label generation may shift in the vascular tree as a function of cutoff velocity. Here, we investigate label dynamics and especially the ATT of renal VSASL and compared it with a spatially selective pulsed arterial spin labeling technique, flow alternating inversion recovery (FAIR). METHODS: Arterial spin labeling data were acquired in 7 subjects, using free-breathing dual VSASL and FAIR with five postlabeling delays: 400, 800, 1200, 2000, and 2600 ms. The VSASL measurements were acquired with cutoff velocities of 5, 10, and 15 cm/s, with anterior-posterior velocity-encoding direction. Cortical perfusion-weighted signal, temporal SNR, quantified renal blood flow, and arterial transit time were reported. RESULTS: In contrast to FAIR, renal VSASL already showed fairly high signal at the earliest postlabeling delays, for all cutoff velocities. The highest VSASL signal and temporal SNR was obtained with a cutoff velocity of 10 cm/s at postlabeling delay = 800 ms, which was earlier than for FAIR at 1200 ms. Fitted ATT on VSASL was ≤ 0 ms, indicating ATT insensitivity, which was shorter than for FAIR (189 ± 79 ms, P < .05). Finally, the average cortical renal blood flow measured with cutoff velocities of 5 cm/s (398 ± 84 mL/min/100 g) and 10 cm/s (472 ± 160 mL/min/100 g) were similar to renal blood flow measured with FAIR (441 ± 84 mL/min/100 g) (P > .05) with good correlations on subject level. CONCLUSION: Velocity-selective arterial spin labeling in the kidney reduces ATT sensitivity compared with the recommended pulsed arterial spin labeling method, as well as if cutoff velocity is increased to reduce spurious labeling due to motion. Thus, VSASL has potential as a method for time-efficient, single-time-point, free-breathing renal perfusion measurements, despite lower tSNR than FAIR.

Multi-organ comparison of flow-based arterial spin labeling techniques: Spatially non-selective labeling for cerebral and renal perfusion imaging.

PURPOSE: Flow-based arterial spin labeling (ASL) techniques provide a transit-time insensitive alternative to the more conventional spatially selective ASL techniques. However, it is not clear which flow-based ASL technique performs best and also, how these techniques perform outside the brain (taking into account eg, flow-dynamics, field-inhomogeneity, and organ motion). In the current study we aimed to compare 4 flow-based ASL techniques (ie, velocity selective ASL, acceleration selective ASL, multiple velocity selective saturation ASL, and velocity selective inversion prepared ASL [VSI-ASL]) to the current spatially selective reference techniques in brain (ie, pseudo-continuous ASL [pCASL]) and kidney (ie, pCASL and flow alternating inversion recovery [FAIR]). METHODS: Brain (n = 5) and kidney (n = 6) scans were performed in healthy subjects at 3T. Perfusion-weighted signal (PWS) maps were generated and ASL techniques were compared based on temporal SNR (tSNR), sensitivity to perfusion changes using a visual stimulus (brain) and robustness to respiratory motion by comparing scans acquired in paced-breathing and free-breathing (kidney). RESULTS: In brain, all flow-based ASL techniques showed similar tSNR as pCASL, but only VSI-ASL showed similar sensitivity to perfusion changes. In kidney, all flow-based ASL techniques had comparable tSNR, although all lower than FAIR. In addition, VSI-ASL showed a sensitivity to B1 -inhomogeneity. All ASL techniques were relatively robust to respiratory motion. CONCLUSION: In both brain and kidney, flow-based ASL techniques provide a planning-free and transit-time insensitive alternative to spatially selective ASL techniques. VSI-ASL shows the most potential overall, showing similar performance as the golden standard pCASL in brain. However, in kidney, a reduction of B1 -sensitivity of VSI-ASL is necessary to match the performance of FAIR.

Feasibility of Velocity-Selective Arterial Spin Labeling in Breast Cancer Patients for Noncontrast-Enhanced Perfusion Imaging.

BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is the most sensitive method for detection of breast cancer. However, due to high costs and retention of intravenously injected gadolinium-based contrast agent, screening with DCE-MRI is only recommended for patients who are at high risk for developing breast cancer. Thus, a noncontrast-enhanced alternative to DCE is desirable. PURPOSE: To investigate whether velocity selective arterial spin labeling (VS-ASL) can be used to identify increased perfusion and vascularity within breast lesions compared to surrounding tissue. STUDY TYPE: Prospective. POPULATION: Eight breast cancer patients. FIELD STRENGTH/SEQUENCE: A 3 T; VS-ASL with multislice single-shot gradient-echo echo-planar-imaging readout. ASSESSMENT: VS-ASL scans were independently assessed by three radiologists, with 3-25 years of experience in breast radiology. Scans were scored on lesion visibility and artifacts, based on a 3-point Likert scale. A score of 1 corresponded to "lesions being distinguishable from background" (lesion visibility), and "no or few artifacts visible, artifacts can be distinguished from blood signal" (artifact score). A distinction was made between mass and nonmass lesions (based on BI-RADS lexicon), as assessed in the standard clinical exam. STATISTICAL TESTS: Intra-class correlation coefficient (ICC) for interobserver agreement. RESULTS: The ICC was 0.77 for lesion visibility and 0.84 for the artifact score. Overall, mass lesions had a mean score of 1.27 on lesion visibility and 1.53 on the artifact score. Nonmass lesions had a mean score of 2.11 on lesion visibility and 2.11 on the artifact score. DATA CONCLUSION: We have demonstrated the technical feasibility of bilateral whole-breast perfusion imaging using VS-ASL in breast cancer patients. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept.

OBJECTIVE: Computed tomography scanning of the lumbar spine incurs a radiation dose ranging from 3.5 mSv to 19.5 mSv as well as relevant costs and is commonly necessary for spinal neuronavigation. Mitigation of the need for treatment-planning CT scans in the presence of MRI facilitated by MRI-based synthetic CT (sCT) would revolutionize navigated lumbar spine surgery. The authors aim to demonstrate, as a proof of concept, the capability of deep learning-based generation of sCT scans from MRI of the lumbar spine in 3 cases and to evaluate the potential of sCT for surgical planning. METHODS: Synthetic CT reconstructions were made using a prototype version of the "BoneMRI" software. This deep learning-based image synthesis method relies on a convolutional neural network trained on paired MRI-CT data. A specific but generally available 4-minute 3D radiofrequency-spoiled T1-weighted multiple gradient echo MRI sequence was supplemented to a 1.5T lumbar spine MRI acquisition protocol. RESULTS: In the 3 presented cases, the prototype sCT method allowed voxel-wise radiodensity estimation from MRI, resulting in qualitatively adequate CT images of the lumbar spine based on visual inspection. Normal as well as pathological structures were reliably visualized. In the first case, in which a spiral CT scan was available as a control, a volume CT dose index (CTDIvol) of 12.9 mGy could thus have been avoided. Pedicle screw trajectories and screw thickness were estimable based on sCT findings. CONCLUSIONS: The evaluated prototype BoneMRI method enables generation of sCT scans from MRI images with only minor changes in the acquisition protocol, with a potential to reduce workflow complexity, radiation exposure, and costs. The quality of the generated CT scans was adequate based on visual inspection and could potentially be used for surgical planning, intraoperative neuronavigation, or for diagnostic purposes in an adjunctive manner.

Influence of labeling parameters and respiratory motion on velocity-selective arterial spin labeling for renal perfusion imaging.

PURPOSE: Arterial transit time uncertainties and challenges during planning are potential issues for renal perfusion measurement using spatially selective arterial spin labeling techniques. To mitigate these potential issues, a spatially non-selective technique, such as velocity-selective arterial spin labeling (VSASL), could be an alternative. This article explores the influence of VSASL sequence parameters and respiratory induced motion on VS-label generation. METHODS: VSASL data were acquired in human subjects (n = 15), with both single and dual labeling, during paced-breathing, while essential sequence parameters were systematically varied; (1) cutoff velocity, (2) labeling gradient orientation and (3) post-labeling delay (PLD). Pseudo-continuous ASL was acquired as a spatially selective reference. In an additional free-breathing single VSASL experiment (n = 9) we investigated respiratory motion influence on VS-labeling. Absolute renal blood flow (RBF), perfusion weighted signal (PWS), and temporal signal-to-noise ratio (tSNR) were determined. RESULTS: (1) With decreasing cutoff velocity, tSNR and PWS increased. However, undesired tissue labeling occurred at low cutoff velocities (≤ 5.4 cm/s). (2) Labeling gradient orientation had little effect on tSNR and PWS. (3) For single VSASL high signal appeared in the kidney pedicle at PLD < 800 ms, and tSNR and PWS decreased with increasing PLD. For dual VSASL, maximum tSNR occurred at PLD = 1200 ms. Average cortical RBF measured with dual VSASL (264 ± 34 mL/min/100 g) at a cutoff velocity of 5.4 cm/s, and feet-head labeling was slightly lower than with pseudo-continuous ASL (283 ± 55 mL/min/100 g). CONCLUSION: With well-chosen sequence parameters, tissue labeling induced by respiratory motion can be minimized, allowing to obtain good quality RBF maps using planning-free labeling with dual VSASL.

Deep learning-based MR-to-CT synthesis: The influence of varying gradient echo-based MR images as input channels.

PURPOSE: To study the influence of gradient echo-based contrasts as input channels to a 3D patch-based neural network trained for synthetic CT (sCT) generation in canine and human populations. METHODS: Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in-phase and opposed-phase images were obtained from a single T1 -weighted multi-echo gradient-echo acquisition. From this set, 6 input configurations were defined, each containing 1 to 4 MR images regarded as input channels. For each configuration, a UNet-derived deep learning model was trained for synthetic CT generation. Reconstructed Hounsfield unit maps were evaluated with peak SNR, mean absolute error, and mean error. Dice similarity coefficient and surface distance maps assessed the geometric fidelity of bones. Repeatability was estimated by replicating the training up to 10 times. RESULTS: Seventeen canines and 23 human subjects were included in the study. Performance and repeatability of single-channel models were dependent on the TE-related water-fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines. CONCLUSION: Significant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In-phase images outperformed opposed-phase images, and Dixon reconstructed multichannel inputs outperformed single-channel inputs.

Comparison of multi-delay FAIR and pCASL labeling approaches for renal perfusion quantification at 3T MRI.

OBJECTIVE: To compare the most commonly used labeling approaches, flow-sensitive alternating inversion recovery (FAIR) and pseudocontinuous arterial spin labeling (pCASL), for renal perfusion measurement using arterial spin labeling (ASL) MRI. METHODS: Multi-delay FAIR and pCASL were performed in 16 middle-aged healthy volunteers on two different occasions at 3T. Relative perfusion-weighted signal (PWS), temporal SNR (tSNR), renal blood flow (RBF), and arterial transit time (ATT) were calculated for the cortex and medulla in both kidneys. Bland-Altman plots, intra-class correlation coefficient, and within-subject coefficient of variation were used to assess reliability and agreement between measurements. RESULTS: For the first visit, RBF was 362 ± 57 and 140 ± 47 mL/min/100 g, and ATT was 0.47 ± 0.13 and 0.70 ± 0.10 s in cortex and medulla, respectively, using FAIR; RBF was 201 ± 72 and 84 ± 27 mL/min/100 g, and ATT was 0.71 ± 0.25 and 0.86 ± 0.12 s in cortex and medulla, respectively, using pCASL. For both labeling approaches, RBF and ATT values were not significantly different between visits. Overall, FAIR showed higher PWS and tSNR. Moreover, repeatability of perfusion parameters was better using FAIR. DISCUSSION: This study showed that compared to (balanced) pCASL, FAIR perfusion values were significantly higher and more comparable between visits.

Enabling free-breathing background suppressed renal pCASL using fat imaging and retrospective motion correction.

PURPOSE: For free-breathing renal perfusion imaging using arterial spin labeling (ASL), retrospective image realignment has been found essential to reduce subtraction artifacts and, independently, background suppression has been demonstrated to reduce physiologic noise. However, negative results on ASL precision and accuracy have been reported for the combination of both. In this study, the effect of background suppression -level in combination with image registration on free-breathing renal ASL signal quality, with registration either on ASL-images themselves or guided by additionally acquired fat-images, was investigated. The results from free-breathing acquisitions were compared with the reference paced-breathing motion compensation strategy. METHODS: Pseudocontinuous ASL (pCASL) data with additional fat-images were acquired from 10 subjects at 1.5T with varying background suppression levels during free-breathing and paced-breathing. Images were registered using the ASL-images themselves (ASLReg) or using their corresponding fat-images (FatReg). Temporal signal-to-noise ratio (tSNR) served to evaluate precision and perfusion weighted signal (PWS) to assess accuracy. RESULTS: In combination with image registration, background suppression significantly improved tSNR by 50% (P < .05). For heavy suppression, ASLReg and FatReg showed similar performance in terms of tSNR and PWS. Background suppression with two inversion pulses induced a small, nonsignificant (P > .05) PWS reduction, but increased PWS accuracy. When applying heavy background suppression, free-breathing acquisitions resulted in similar ASL-quality to paced-breathing acquisitions. CONCLUSION: Background suppression was found beneficial for free-breathing renal pCASL precision without compromising accuracy, despite motion challenges. In combination with ASLReg or FatReg, background suppression enabled clinically viable free-breathing renal pCASL.

CT-based study of vertebral and intravertebral rotation in right thoracic adolescent idiopathic scoliosis.

PURPOSE: To define the longitudinal rotation axis around which individual vertebrae rotate, and to establish the various extra- and intravertebral rotation patterns in thoracic adolescent idiopathic scoliosis (AIS) patients, for better understanding of the 3D development of the rotational deformity. METHODS: Seventy high-resolution CT scans from an existing database of thoracic AIS patients (Cobb angle: 46°-109°) were included to determine the vertebral axial rotation, rotation radius, intravertebral axial rotation, and local mechanical torsion for each spinal level, using previously validated image processing techniques. RESULTS: For all levels, the longitudinal rotation axis, from which the vertebrae rotate away from the midline, was localized posterior to the spine. The axis became closer to the spine at the apex: apex, r = 11.5 ± 5.1 cm versus two levels above (radius = 15.8 ± 8.5 cm; p < 0.001) and beneath (radius = 14.2 ± 8.2 cm; p < 0.001). The vertebral axial rotation, intravertebral axial rotation, and local mechanical torsion of the vertebral bodies were largest at the apex (21.9° ± 7.4°, 8.7° ± 13.5° and 3.0° ± 2.5°) and decreased toward the neutral, junctional zones (p < 0.001). CONCLUSION: In AIS, the vertebrae rotate away around an axis that is localized posterior to the spine. The distance between this axis and the spine is minimal at the apex and increases gradually to the neutral zones. The vertebral axial rotation is accompanied by smaller amounts of intravertebral rotation and local mechanical torsion, which increases toward the apical region. The altered morphology and alignment are important for a better understanding of the 3D pathoanatomical development of AIS and better therapeutic planning for bracing and surgical intervention. These slides can be retrieved under Electronic Supplementary Material.

Fluid filling of the digestive tract for improved proton resonance frequency shift-based MR thermometry in the pancreas.

PURPOSE: To demonstrate that fluid filling of the digestive tract improves the performance of respiratory motion-compensated proton resonance frequency shift (PRFS)-based magnetic resonance (MR) thermometry in the pancreas. MATERIALS AND METHODS: In seven volunteers (without heating), we evaluated PRFS thermometry in the pancreas with and without filling of the surrounding digestive tract. All data acquisition was performed at 1.5T, then all datasets were analyzed and compared with three different PRFS respiratory motion-compensated thermometry methods: gating, multibaseline, and referenceless. The temperature precision of the different methods was evaluated by assessing temperature standard deviation over time, while a simulation experiment was used to study the accuracy of the methods. RESULTS: Without fluid intake, errors in temperature precision in the pancreas up to 10°C were observed for all evaluated methods. After liquid intake, temperature precision improved to median values between 1.8 and 2.9°C. The simulations showed that gating had the lowest accuracy, with errors up to 7°C. Multibaseline and referenceless thermometry performed better, with a median error in the pancreas between -3 and +3°C after fluid intake, for all volunteers. CONCLUSION: Preparation of the digestive tract near the pancreas by filling it with fluid improved MR thermometry precision and accuracy for all common respiratory motion-compensated methods evaluated. These improvements are attributed to reducing field inhomogeneity in the pancreas. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:692-701.

Arterial and portal venous liver perfusion using selective spin labelling MRI.

PURPOSE: To investigate the feasibility of selective arterial and portal venous liver perfusion imaging with spin labelling (SL) MRI, allowing separate labelling of each blood supply. METHODS: The portal venous perfusion was assessed with a pulsed EPISTAR technique and the arterial perfusion with a pseudo-continuous sequence. To explore precision and reproducibility, portal venous and arterial perfusion were separately quantified in 12 healthy volunteers pre- and postprandially (before and after meal intake). In a subgroup of 6 volunteers, the accuracy of the absolute portal perfusion and its relative postprandial change were compared with MRI flow measurements of the portal vein. RESULTS: The portal venous perfusion significantly increased from 63 ± 22 ml/100g/min preprandially to 132 ± 42 ml/100g/min postprandially. The arterial perfusion was lower with 35 ± 22 preprandially and 22 ± 30 ml/100g/min postprandially. The pre- and postprandial portal perfusion using SL correlated well with flow-based perfusion (r(2) = 0.71). Moreover, postprandial perfusion change correlated well between SL- and flow-based quantification (r(2) = 0.77). The SL results are in range with literature values. CONCLUSION: Selective spin labelling MRI of the portal venous and arterial blood supply successfully quantified liver perfusion. This non-invasive technique provides specific arterial and portal venous perfusion imaging and could benefit clinical settings where contrast agents are contraindicated. KEY POINTS: • Perfusion imaging of the liver by Spin Labelling MRI is feasible • Selective Spin Labelling MRI assessed portal venous and arterial liver perfusion separately • Spin Labelling based portal venous liver perfusion showed significant postprandial increase • Spin Labelling based portal perfusion correlated well with phase-contrast based portal perfusion • This non-invasive technique could benefit settings where contrast agents are contraindicated.

Liver perfusion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI): comparison of enhancement in Gd-BT-DO3A and Gd-EOB-DTPA in normal liver parenchyma.

PURPOSE: Within-patient comparison of the enhancement patterns of normal liver parenchyma after gadobutrol and gadoxetate disodium, with emphasis on the start of hepatocytic uptake of gadoxetate disodium. MATERIALS AND METHODS: Twenty-one patients (12 female, 9 male) without chronic liver disease underwent 1.5-T contrast-enhanced MRI twice, once with an extracellular contrast agent (gadobutrol) and once with a hepatospecific agent (gadoxetate disodium), using a T1-weighted keyhole sequence. Fifteen whole-liver datasets were acquired up to 5 min for both contrast agents and two additional datasets, up to 20 min, for gadoxetate. Signal intensities (SI) of the parenchyma, aorta and portal vein were measured and analysed relative to pre-contrast parenchymal SI. RESULTS: After gadoxetate, in 29% of the patients the parenchymal SI decreased by ≥5% after the initial vascular-phase-induced peak, while in the other 71% the parenchymal SI remained stable or gradually increased until up to 20 min after the initial peak. The hepatocytic gadoxetate uptake started at a mean of 37.8 s (SD 14.7 s) and not later than 76 s after left ventricle enhancement. CONCLUSION: Parenchymal enhancement due to hepatocytic uptake of gadoxetate can start as early as in the late arterial phase. This may confound the assessment of lesion appearance as compared to extracellular contrast such as gadobutrol. KEY POINTS: Gadoxetate-enhanced liver MRI results in early enhancement of normal parenchyma in patients The start of the hepatobiliary phase coincides with the late arterial phase. This may confound the assessment of lesion appearance compared to extracellular contrast. Different parenchymal enhancement patterns after gadoxetate were found for normal parenchyma.

Magnetic resonance imaging-based bone imaging of the lower limb: Strategies for generating high-resolution synthetic computed tomography.

This study aims at assessing approaches for generating high-resolution magnetic resonance imaging- (MRI-) based synthetic computed tomography (sCT) images suitable for orthopedic care using a deep learning model trained on low-resolution computed tomography (CT) data. To that end, paired MRI and CT data of three anatomical regions were used: high-resolution knee and ankle data, and low-resolution hip data. Four experiments were conducted to investigate the impact of low-resolution training CT data on sCT generation and to find ways to train models on low-resolution data while providing high-resolution sCT images. Experiments included resampling of the training data or augmentation of the low-resolution data with high-resolution data. Training sCT generation models using low-resolution CT data resulted in blurry sCT images. By resampling the MRI/CT pairs before the training, models generated sharper images, presumably through an increase in the MRI/CT mutual information. Alternatively, augmenting the low-resolution with high-resolution data improved sCT in terms of mean absolute error proportionally to the amount of high-resolution data. Overall, the morphological accuracy was satisfactory as assessed by an average intermodal distance between joint centers ranging from 0.7 to 1.2 mm and by an average intermodal root-mean-squared distances between bone surfaces under 0.7 mm. Average dice scores ranged from 79.8% to 87.3% for bony structures. To conclude, this paper proposed approaches to generate high-resolution sCT suitable for orthopedic care using low-resolution data. This can generalize the use of sCT for imaging the musculoskeletal system, paving the way for an MR-only imaging with simplified logistics and no ionizing radiation.

MRI-based synthetic CT in the detection of knee osteoarthritis: Comparison with CT.

Magnetic resonance Imaging is the gold standard for assessment of soft tissues; however, X-ray-based techniques are required for evaluating bone-related pathologies. This study evaluated the performance of synthetic computed tomography (sCT), a novel MRI-based bone visualization technique, compared with CT, for the scoring of knee osteoarthritis. sCT images were generated from the 3T T1-weighted gradient-echo MR images using a trained machine learning algorithm. Two readers scored the severity of osteoarthritis in tibiofemoral and patellofemoral joints according to OACT, which enables the evaluation of osteoarthritis, from its characteristics of joint space narrowing, osteophytes, cysts and sclerosis in CT (and sCT) images. Cohen's κ was used to assess the interreader agreement for each modality, and intermodality agreement of CT- and sCT-based scores for each reader. We also compared the confidence level of readers for grading CT and sCT images using confidence scores collected during grading. Inter-reader agreement for tibiofemoral and patellofemoral joints were almost-perfect for both modalities (κ = 0.83-0.88). The intermodality agreement of osteoarthritis scores between CT and sCT was substantial to almost-perfect for tibiofemoral (κ = 0.63 and 0.84 for the two readers) and patellofemoral joints (κ = 0.78 and 0.81 for the two readers). The analysis of diagnosis confidence scores showed comparable visual quality of the two modalities, where both are showing acceptable confidence levels for scoring OA. In conclusion, in this single-center study, sCT and CT were comparable for the scoring of knee OA.

Efficient cascaded V-net optimization for lower extremity CT segmentation validated using bone morphology assessment.

Semantic segmentation of bone from lower extremity computerized tomography (CT) scans can improve and accelerate the visualization, diagnosis, and surgical planning in orthopaedics. However, the large field of view of these scans makes automatic segmentation using deep learning based methods challenging, slow and graphical processing unit (GPU) memory intensive. We investigated methods to more efficiently represent anatomical context for accurate and fast segmentation and compared these with state-of-the-art methodology. Six lower extremity bones from patients of two different datasets were manually segmented from CT scans, and used to train and optimize a cascaded deep learning approach. We varied the number of resolution levels, receptive fields, patch sizes, and number of V-net blocks. The best performing network used a multi-stage, cascaded V-net approach with 1283 -643 -323 voxel patches as input. The average Dice coefficient over all bones was 0.98 ± 0.01, the mean surface distance was 0.26 ± 0.12 mm and the 95th percentile Hausdorff distance 0.65 ± 0.28 mm. This was a significant improvement over the results of the state-of-the-art nnU-net, with only approximately 1/12th of training time, 1/3th of inference time and 1/4th of GPU memory required. Comparison of the morphometric measurements performed on automatic and manual segmentations showed good correlation (Intraclass Correlation Coefficient [ICC] >0.8) for the alpha angle and excellent correlation (ICC >0.95) for the hip-knee-ankle angle, femoral inclination, femoral version, acetabular version, Lateral Centre-Edge angle, acetabular coverage. The segmentations were generally of sufficient quality for the tested clinical applications and were performed accurately and quickly compared to state-of-the-art methodology from the literature.