CT MR Imaging Research Articles

Metal artifacts with dental implants: Evaluation using a dedicated CT/MR oral phantom with registration of the CT and MR images

The aims of this study were to develop a computed tomography/magnetic resonance (CT/MR) oral phantom with insertable dental implants and to register CT/MR images to generate artifact-free MR images for patients undergoing teeth restorations. All measurements were done using a human MR scanner with spin echo (SE) and gradient echo (GRE) sequences image scan together with CT image. The metal regions and normal teeth parts are extracted with a suitable threshold from an initial image reconstructed with artifact from the CT images. Corrected metal projection regions of MR images and CT images are fused to produce artifact-free MR image that include dental restorations. After CT/MR registration, artifact size presented differences on the x- (SE, 12.0 mm; GRE, 18.0 mm) and y- (SE, 24.0 mm; GRE, 36.6 mm). When comparing the dental restoration with normal teeth, the structural similarity index metric (SSIM) of GRE 50 was lower than for the GRE 8 sequence and the SSIM of SE 145 shown higher than for the SE 490 sequence. The dedicated phantom provides a useful tool in head and neck research for multi-modality images. Therefore, CT/MR image-based approach for ground truth and registration offers visualization in diagnostic system and radiation treatment planning system.

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning

Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could make the segmentation of tumors more accurate. This paper investigates CNN-based methods for automated nasopharyngeal carcinoma (NPC) segmentation using computed tomography (CT) and magnetic resonance (MR) images. Specially, a multi-modality convolutional neural network (M-CNN) is designed to jointly learn a multi-modal similarity metric and segmentation of paired CT-MR images. By jointly optimizing the similarity learning error and the segmentation error, the feature learning processes of both modalities are mutually guided. In doing so, the segmentation sub-networks are able to take advantage of the other modality’s information. Considering that each modality possesses certain distinctive characteristics, we combine the higher-layer features extracted by a single-modality CNN (S-CNN) and M-CNN to form a combined CNN (C-CNN) for each modality, which is able to further utilize the complementary information of different modalities and improve the segmentation performance. The proposed M-CNN and C-CNN were evaluated on 90 CT-MR images of NPC patients. Experimental results demonstrate that our methods achieve improved segmentation performance compared to their counterparts without multi-modal information fusion and the existing CNN-based multi-modality segmentation methods.

Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Magnetic resonance (MR) images have distinctive advantages in radiation treatment (RT) planning due to their superior, anatomic and functional information compared with computed tomography (CT). For the RT dose calculation, MR images cannot be directly used because of the lack of electron density information. To address this issue, we propose to generate pseudo-CT (pCT) in terms of multiple matching Dixon MR images to support MR-only RT, particularly in the challenging body section of the abdomen. To this end, we design the dedicated multichannel residual conditional generative adversarial network (MCRCGAN). The significance of our efforts is three-fold: 1) The MCRCGAN organically incorporates multiple theories and techniques, such as multichannel residual network (ResNet) and conditional generative adversarial network (cGAN), which facilitate its more authentic pCT generation than many existing methods. 2) The usage of residual modules effectively deepens the network without performance degradation, and the multichannel ResNet helps to simultaneously capture the substance of images, as extensively as possible, which is implicitly contained in the multiple different MR images of the same subject. 3) Due to the designed dedicated network structure, the MCRCGAN is capable of generating satisfactory pCTs under the condition of limited training data as well as prompt prediction response. Experimental studies on ten patients' paired MR-CT images demonstrate the effectiveness of our proposed MCRCGAN model on both the pCT generation quality and the performance stability.

CT and MRI of Adrenal Masses

The adrenal glands are common sites of disease in the human body1 and are well visualized on abdominal CT scans and MR images. With the increased use of cross-sectional imaging, there has been increased detection of adrenal masses.2 The incidence of adrenal masses in patients with no history of malignancy is reported in 5% of all abdominal CT examinations3 , 4 and in 9% to 13% of patients imaged for a known malignancy.3

Combination of free-breathing radial 3D fat-suppressed T1-weighted gradient-echo sequence with diffusion weighted images: Potential for differentiating malignant from benign peripheral solid pulmonary masses

ObjectivesHigh resolution CT is the most commonly used radiological method for differentiating benign from malignant peripheral solid pulmonary masses, however, some of them are not easily diagnosed by morphology alone. Furthermore, due to the radiation dose, it is unsuitable for patients with disorders requiring repeated examinations over prolonged periods. The aims of this study were to evaluate whether a combination of diffusion-weighted images (DWI) and free-breathing radial 3D fat-suppressed T1-weighted gradient echo (radial volumetric interpolated breath-hold examination, radial VIBE) sequence can enable discrimination between benign from malignant peripheral solid pulmonary masses. Materials and methodsBoth chest CT scan and MR imaging with radial VIBE and DWI were obtained from 47 patients; 30 males and 17 females (mean age 64 years old; age range 48–83 years old). Benign and malignant peripheral solid pulmonary masses were conclusively identified by pathology results. Two radiologists independently reviewed all the images and record radiological features including morphological signs on radial VIBE, CT images, and ADC value. Receiver operating characteristic (ROC) was used to analyze the capability of radial VIBE as well as DWI to distinguish malignant from benign peripheral solid pulmonary masses. ResultsIn 77% of patients, malignant peripheral solid pulmonary masses were found. Morphological signs of mediastinal lymph node enlargement and lobulation were more easily found in malignant masses in both radial VIBE (mediastinal lymph node enlargement: p = 0.033, lobulation: p = 0.039) and CT (mediastinal lymph node enlargement: p = 0.004, lobulation: p = 0.012). The ADC value were also significant difference between benign and malignant groups (p = 0.001). Combined ADC value with radial VIBE was a most specific test than routine-dose CT (86.1% vs 75%, p < 0.001), but less sensitive than routine-dose CT (81.8% vs 90.9%; p < 0.001) for malignant peripheral solid pulmonary masses detection. Diagnostic accuracy was 89% for combining ADC value with radial VIBE, and 85% for routine-dose CT. ConclusionsCombination of morphological signs and ADC value seems to improve differentiating malignant from benign peripheral solid pulmonary masses. Especially in patients unable to endure radiation exposure, suspend respiration, radial VIBE provides similar morphological signs displaying to those on routine-dose CT.

Bone Subtraction Iodine Imaging Using Area Detector CT for Evaluation of Skull Base Invasion by Nasopharyngeal Carcinoma.

Conventional CT has generally lower detectability of bone marrow invasion than MR imaging due to lower tissue contrast. The purpose of this study was to compare the diagnostic performance of conventional CT alone or in combination with bone subtraction iodine imaging using area detector CT for the evaluation of skull base invasion in patients with nasopharyngeal carcinoma. Forty-four consecutive patients who underwent contrast-enhanced CT using 320-row area detector CT and contrast-enhanced MR imaging for nasopharyngeal carcinoma staging between April 2012 and November 2017 were enrolled in this retrospective study. Bone subtraction iodine images were generated by subtracting pre- and postcontrast volume scans using a high-resolution deformable registration algorithm. Two blinded observers evaluated skull base invasion at multiple sites (sphenoid body, clivus, bilateral base of the pterygoid process, and petrous bone) using conventional CT images alone or in combination with bone subtraction iodine images. Examination of MR and CT images by an experienced neuroradiologist was the reference standard for evaluating sensitivity, specificity, and area under the receiver operating characteristic curve. Twenty-six patients (59%) showed skull base invasion at 84 sites on the reference standard. Conventional CT plus bone subtraction iodine images showed higher sensitivity (92.9% versus 78.6%, P = .02) and specificity (95.6% versus 86.1%, P = .01) than conventional CT images alone for evaluating skull base invasion. The area under the receiver operating characteristic curve for conventional CT plus bone subtraction iodine (0.98) was significantly larger (P < .001) than the area under the receiver operating characteristic curve for conventional CT alone (0.90). Conventional CT plus bone subtraction iodine performs more closely to the accuracy of combining CT and MR imaging compared with conventional CT alone.

Generating the synthetic CT (sCT) and synthetic MR (sMR: sT1w/sT2w) images of the brain using atlas based method

Introduction: Radiation therapy planning (RTP) is one of the clinical applications in which both CT scan and MRI are used. MR and CT images are applied to determine the target volume and calculation of dose distribution, respectively. In addition, using two imaging modalities increases the department workload and cost. In this study, an algorithm was presented to create synthetic CT (sCT) and synthetic MR (sMR: sT1w/sT2w) images consequently CT or MR images are not taken from patient. Materials and Methods: The synthetic CT (sCT) and synthetic MR (sMR: sT1w/sT2w) images was generated by atlas based method. The dataset consisted of MR and CT paired images from 17 brain RT patients. To generate sCT/sMR image, first each MR/CT atlas was registered to the MR/CT target image, the resulting transformation was applied to the corresponding CT/MR atlas which created the set of deformed images. Then, deformed images were fused to generate a single sCT/sMR. The sCT/sMR was compared with the real CT/MR images using the MAE. Results: The evaluations performed by MAE showed a good agreement between the sCT/sMR and real CT/MR images. The results represented 72±11.3 HU, 51±7.3 image intensity and 64±7.3 image intensity for sCT, sT1w and sT2w, respectively. Conclusion: This method showed that sT1w images have lesser MAE with respect sT2w. As a result, CT in transferring the brain tissues geometry to sT1w is more successful than sT2w. Also, the sCT images based T1w are in better agreement with real CT than sCT images based T2w.

Technical Note: U-net-generated synthetic CT images for magnetic resonance imaging-only prostate intensity-modulated radiation therapy treatment planning.

Clinical implementation of magnetic resonance imaging (MRI)-only radiotherapy requires a method to derive synthetic CT image (S-CT) for dose calculation. This study investigated the feasibility of building a deep convolutional neural network for MRI-based S-CT generation and evaluated the dosimetric accuracy on prostate IMRT planning. A paired CT and T2-weighted MR images were acquired from each of 51 prostate cancer patients. Fifteen pairs were randomly chosen as tested set and the remaining 36 pairs as training set. The training subjects were augmented by applying artificial deformations and feed to a two-dimensional U-net which contains 23 convolutional layers and 25.29 million trainable parameters. The U-net represents a nonlinear function with input an MR slice and output the corresponding S-CT slice. The mean absolute error (MAE) of Hounsfield unit (HU) between the true CT and S-CT images was used to evaluate the HU estimation accuracy. IMRT plans with dose 79.2Gy prescribed to the PTV were applied using the true CT images. The true CT images then were replaced by the S-CT images and the dose matrices were recalculated on the same plan and compared to the one obtained from the true CT using gamma index analysis and absolute point dose discrepancy. The U-net was trained from scratch in 58.67h using a GP100-GPU. The computation time for generating a new S-CT volume image was 3.84-7.65s. Within body, the (mean±SD) of MAE was (29.96±4.87) HU. The 1%/1mm and 2%/2mm gamma pass rates were over 98.03% and 99.36% respectively. The DVH parameters discrepancy was less than 0.87% and the maximum point dose discrepancy within PTV was less than 1.01% respect to the prescription. The U-net can generate S-CT images from conventional MR image within seconds with high dosimetric accuracy for prostate IMRT plan.

A momentum-based diffeomorphic demons framework for deformable MR-CT image registration

Neuro-navigated procedures require a high degree of geometric accuracy but are subject to geometric error from complex deformation in the deep brain—e.g. regions about the ventricles due to egress of cerebrospinal fluid (CSF) upon neuroendoscopic approach or placement of a ventricular shunt. We report a multi-modality, diffeomorphic, deformable registration method using momentum-based acceleration of the Demons algorithm to solve the transformation relating preoperative MRI and intraoperative CT as a basis for high-precision guidance. The registration method (pMI-Demons) extends the mono-modality, diffeomorphic form of the Demons algorithm to multi-modality registration using pointwise mutual information (pMI) as a similarity metric. The method incorporates a preprocessing step to nonlinearly stretch CT image values and incorporates a momentum-based approach to accelerate convergence. Registration performance was evaluated in phantom and patient images: first, the sensitivity of performance to algorithm parameter selection (including update and displacement field smoothing, histogram stretch, and the momentum term) was analyzed in a phantom study over a range of simulated deformations; and second, the algorithm was applied to registration of MR and CT images for four patients undergoing minimally invasive neurosurgery. Performance was compared to two previously reported methods (free-form deformation using mutual information (MI-FFD) and symmetric normalization using mutual information (MI-SyN)) in terms of target registration error (TRE), Jacobian determinant (J), and runtime. The phantom study identified optimal or nominal settings of algorithm parameters for translation to clinical studies. In the phantom study, the pMI-Demons method achieved comparable registration accuracy to the reference methods and strongly reduced outliers in TRE (p 0.001 in Kolmogorov–Smirnov test). Similarly, in the clinical study: median TRE = 1.54 mm (0.83–1.66 mm interquartile range, IQR) for pMI-Demons compared to 1.40 mm (1.02–1.67 mm IQR) for MI-FFD and 1.64 mm (0.90–1.92 mm IQR) for MI-SyN. The pMI-Demons and MI-SyN methods yielded diffeomorphic transformations (J > 0) that preserved topology, whereas MI-FFD yielded unrealistic (J < 0) deformations subject to tissue folding and tearing. Momentum-based acceleration gave a ~35% speedup of the pMI-Demons method, providing registration runtime of 10.5 min (reduced to 2.2 min on GPU), compared to 15.5 min for MI-FFD and 34.7 min for MI-SyN. The pMI-Demons method achieved registration accuracy comparable to MI-FFD and MI-SyN, maintained diffeomorphic transformation similar to MI-SyN, and accelerated runtime in a manner that facilitates translation to image-guided neurosurgery.

Feasibility of MR-Generated Synthetic CT Images for Planning Stereotactic Radiosurgery of Brain Metastases

Frameless Linac-based stereotactic radiosurgery (SRS) treatment planning typically involves spatial registration of MR and CT images. A solely MR-guided workflow would eliminate the burdens of CT scanning and associated time as well as alignment errors, but would require dose calculations and patient positioning based on MR-derived information. The feasibility of MR-guided SRS of brain metastases was investigated. Specifically, it was hypothesized that synthetic CT images (MRCTs) could be used to generate plans and reference images for positioning that would not differ from SRS using a traditional CT-guided technique. MR and CT images were acquired for 19 patients undergoing SRS for brain metastases. Following rigid image alignment, PTV and OAR contours were delineated. MRCT image volumes were generated using a voxel-based probabilistic classification technique and aligned rigidly to their respective CT scans. The CT structures were transferred onto the MRCT volumes. CT-based plans were created with the intent to deliver 99.5% of the prescription dose to the PTV and avoid excess doses to the OARs. The fluences from the CT plans then were used for dose calculations on a “maskless” CT plan (in which the attenuation of the immobilization device was removed) and MRCT images. PTV D99.5 and OAR D0.1 cc values were compared. OARs (brainstem, optic nerves/chiasm, eyes, lenses, cochleae) typically defined on CT were drawn on the source MR (T1- and T2-weighted image volumes) images and compared to the original CT volumes. Cone beam CT (CBCT) volumes used for positioning at treatment were aligned to the MRCT and CT volumes, and resulting transforms compared. A total of 43 PTVs were studied with a mean prescription dose of 21.1 Gy (range 12 to 30 Gy). The mean difference in PTV D99.5 between the CT versus maskless CT was 0.0 % (range -0.0 to 0.1 %) and CT versus MRCT was 0.0 % (range -0.1 to 0.0 %) of the prescription doses. Dose metrics for a total of 185 OARs were calculated. OAR D0.1 cc difference between the CT versus maskless CT was 0.0 Gy (range -0.1 to 0.0 Gy) and CT versus MRCT was 0.0 Gy (range -0.2 to 0.7 Gy). For OAR volumes larger than 1 cc (n=64), the mean Dice coefficient between CT and MR structures was 0.82 (range 0.17 to 0.93). Mean differences in alignment between the CBCT volumes and MRCT and CT volumes resulted in isocenter positioning differences of 0.0 mm (range -0.2 to 0.1 mm), 0.0 mm (range -0.5 to 0.4 mm), and 0.1 mm (range -0.2 to 0.5 mm) in the LR, AP, and CC directions. The accuracy of delineation, calculated doses, and patient positioning indicate that MR-guided frameless SRS is feasible. Future investigations will explore generation of reference image volumes for alignment that combine MR and synthesized CT contrasts. Supported by NIH R01EB016079.

CT versus MR Imaging in Estimating Cochlear Radiation Dose during Gamma Knife Surgery for Vestibular Schwannomas.

Leksell stereotactic radiosurgery is an effective option for patients with vestibular schwannomas. Some centers use a combination of stereotactic CT fused with stereotactic MR imaging to achieve an optimal target definition as well as minimize the radiation dose delivered to adjacent structures that correlate with hearing outcomes. The present prospective study was designed to determine whether there is cochlear dose variability between MR imaging and CT. Fifty consecutive patients underwent stereotactic radiosurgery for vestibular schwannomas. Dose-planning was performed using high-definition fused stereotactic MR imaging and stereotactic CT images. The 3D cochlear volume was determined by delineating the cochlea on both CT and T2-weighted MR imaging. The mean radiation dose, maximum dose, and 3- and 4.20-Gy cochlear volumes were identified using standard Leksell Gamma Knife software. The median mean radiation dose delivered to the cochlea was 3.50 Gy (range, 1.20-6.80 Gy) on CT and 3.40 Gy (range, 1-6.70 Gy) on MR imaging (concordance correlation coefficient = 0.86, r 2 = 0.9, P ≤ .001). The median maximum dose delivered to the cochlea was 6.7 Gy on CT and 6.6 Gy on MR imaging (concordance correlation coefficient = 0.89, r 2 = 0.90, P ≤ .001). Dose-volume histograms generated from CT and MR imaging demonstrated a strong level of correlation in estimating the 3- and 4.20-Gy volumes (concordance correlation coefficient = 0.81, r 2 = 0.82, P ≤ .001 and concordance correlation coefficient = 0.87, r 2 = 0.89, P ≤ .001). Both MR imaging and CT provide similar cochlear dose parameters. Despite the reported superiority of CT in identifying bony structures, high-definition MR imaging alone is sufficient to identify the radiation doses delivered to the cochlea.

Multi-segmental spine image registration supporting image-guided interventions of spinal metastases

BackgroundRadiofrequency ablation was introduced recently to treat spinal metastases, which are among the most common metastases. These minimally-invasive interventions are most often image-guided by flat-panel CT scans, withholding soft tissue contrast like MR imaging. Image fusion of diagnostic MR and operative CT images could provide important and useful information during interventions. MethodDiagnostic MR and interventional flat-panel CT scans of 19 patients, who underwent radiofrequency ablations of spinal metastases were obtained. Our presented approach piecewise rigidly registers single vertebrae using normalized gradient fields and embeds them within a fused image. Registration accuracy was determined via Euclidean distances between corresponding landmark pairs of ground truth data. ResultsOur method resulted in an average registration error of 2.35mm. An optimal image fusion performed by landmark registrations achieved an average registration error of 1.70mm. Additionally, intra- and inter-reader variability was determined, resulting in mean distances of corresponding landmark pairs of 1.05mm (MRI) and 1.03mm (flat-panel CT) for the intra-reader variability and 1.36mm and 1.28mm for the inter-reader variability, respectively. ConclusionsOur multi-segmental approach with normalized gradient fields as image similarity measure can handle spine deformations due to patient positioning and avoid time-consuming manually performed registration. Thus, our method can provide practical and applicable intervention support without significantly delaying the clinical workflow or additional workload.

Cervico-medullary compression ratio: A novel radiological parameter correlating with clinical severity in Chiari type 1 malformation

ObjectivesChiari malformation type 1 (CM-1) is associated with cough headache, intracranial hypertension, cerebellar and spinal cord symptoms/signs. Herniated cerebellar tonsil length (HCTL) is widely used radiological parameter to determine the severity of CM-1, but with limited utility due to its weak correlation with some clinico-radiological findings. In this study, we aimed to evaluate a novel, practical parameter (cervico-medullary compression ratio; “CMCR”) for its relationship with clinico-radiological findings in CM-1. Patients and methodsThirty-five adult patients (17 F, 18 M) with CM-1 were included in this retrospective study. Head CT and craniospinal MR images were assessed. CMCR was calculated as the ratio of herniated cerebellar tonsil surface area to foramen magnum surface area, and HCTL was measured. These two parameters were correlated with clinical and radiological findings. ResultsThe mean CMCR was 0.60 ± 0.15 and mean HCTL was 8.91 ± 3.4 mm with no significant difference between gender and age groups for both parameters. For cough headache (0.64 ± 0.14 vs 0.52 ± 0.15, p = 0.043) and syringomyelia (0.67 ± 0.11 vs 0.56 ± 0.16, p = 0.039), only CMCR; for intracranial hypertension (CMCR: 0.64 ± 0.14 vs 0.55 ± 0.16, p = 0.049; HCTL: 9.66 ± 3.59 mm vs 7.79 ± 3.03 mm; p = 0.045) and cerebellar symptoms (CMCR: 0.65 ± 0.14 vs 0.54 ± 0.16, p = 0.048; HCTL: 10.4 ± 3.5 mm vs 7.4 ± 2.8 mm, p = 0.041), both CMCR and HTCL were significantly different between patients with and without respective findings. However, neither CMCR nor HTCL was different between patients with and without spinal cord symptoms and hydrocephalus. ConclusionCMCR is a superior numerical parameter than HCTL for the assessment of clinical severity in CM-1 cases and needs further validation with larger studies.

Ripplet domain fusion approach for CT and MR medical image information

Multimodal medical image fusion (MIF) plays an important role as an assistant for medical professionals by providing a better visualization of diagnostic information using different imaging modalities. The process of image fusion helps the radiologists in the precise diagnosis of several critical diseases and its treatment. In this paper, the proposed framework presents a fusion approach for multimodal medical images that utilize both the features extracted by the discrete ripplet transform (DRT) and pulse coupled neural network. The DRT having different features and a competent depiction of the image coefficients provides several directional high-frequency subband coefficients. The DRT decomposition can preserve more detailed information present in the reference images and further enhance the visualization of the fused images. Firstly, the DRT is applied to decompose the reference images into several low and high-frequency subimage coefficients that are fused by computing the novel sum modified Laplacian and novel modified spatial frequency motivated pulse coupled neural model. This model is used to preserve the redundant information also. Finally, fused images are reconstructed by applying the inverse DRT. The performance of the proposed fusion approach is validated by extensive simulation on the different CT-MR image datasets. Experimental results demonstrate that the proposed method provides the better fused images in terms of visual quality along with the quantitative measures as compared to several existing fusion approaches.

Optimum Wavelet-Based Homomorphic Medical Image Fusion Using Hybrid Genetic–Grey Wolf Optimization Algorithm

Medical image fusion techniques have been widely used in various clinical applications. Generalized homomorphic filters have Fourier domain features of input image. In multimodal medical image fusion discrete wavelet transform-based techniques provides more features and is performed over Fourier spectrum. In this paper, we proposed a homomorphic wavelet fusion which is called optimum homomorphic wavelet fusion (OHWF) using hybrid genetic–grey wolf optimization (HG-GWO) Algorithm. In OHWF, which consists of logarithmic and wavelet domain information of input images. The wavelet-based homomorphic fusion consists of multilevel decomposition features of input image. In our proposal, the approximation coefficients of modality1 (anatomical structure) and optimum scaled detailed coefficients of modality2 are given to adder1. In adder 2, the optimum scaled detailed coefficients of modality 1 and approximation coefficients of modality 2 are added together. The resultants of adder 1 and adder 2 are fused together using pixel based averaging rule. First, the proposed fusion approach is validated for MR-SPECT, MR-PET, MR-CT, and MR T1-T2 image fusion using various fusion evaluation indexes. Later, the conventional grey wolf optimization is modified with genetic operator. Experimental results show that the proposed approach outperforms state-of-the-art fusion algorithms in terms of both structural and the functional information in the fused image.

I093] Mr-guided radiotherapy at the LMU in Munich: Preliminary studies

Purpose The clinical introduction of an MR-guided radiotherapy unit offers great potential to improve future patient treatment but is at the same time associated with several challenges from the perspective of medical physics. Before the final implementation of an MR-Linac in our clinical environment several preliminary studies are performed to prepare the introduction of the new system into the routine patient care as well as covering medical physics related research aspects. Methods One focus of the projects is a comparison of the adaptive workflow based on daily MR and daily cone beam CT imaging. In order to mimic the MR-guided radiotherapy workflow an MR-compatible flat table top is equipped with immobilization devices for offline MR imaging in treatment position. A flexible, modular phantom is developed based on lego bricks, enabling fast distortion phantom prototyping and consequently distortion measurements. The effect of magnetic fields on dose measurements with thermoluminescent dosimeters is evaluated. Image registration methods are applied to time-resolved 4D MR and 4D CT images of patients as well as to breathing phantoms evaluating how to include 4D data into the online adaptive treatment workflow. Results The preliminary results of the projects mentioned above will be presented and discussed. This includes a comparison of CT- and MR-based internal target volume concepts as a surrogate for reproducibility of breathing motion of the patient, the first results of the 4D phantom studies and the TLD measurements in a magnetic field. Conclusions Several studies are performed at the university hospital of the LMU in Munich to pave the way for a smooth introduction of the MR-Linac into clinical routine and into research in MR-guided radiotherapy.

Clinical Outcomes of Endovascular Treatment within 24 Hours in Patients with Mild Ischemic Stroke and Perfusion Imaging Selection

Endovascular thrombectomy has been accepted as the standard of care for patients with acute ischemic stroke. Our aim was to investigate the clinical outcomes of patients with mild ischemic stroke with acute proximal large-vessel occlusion after endovascular treatment within 24 hours of symptom onset. Between January 2014 and August 2017, ninety-three Chinese patients with mild ischemic stroke (NIHSS scores, 0-8) and large-vessel occlusion with endovascular treatment were retrospectively enrolled from 7 comprehensive stroke centers. They were divided into 2 groups: ≤6 hours and 6-24 hours from symptom onset to groin puncture. We analyzed their modified Rankin Scale scores at 90 days, symptomatic intracranial hemorrhage at 48 hours, and mortality during 90 days. Multivariable linear regression analysis was used to identify predictors for NIHSS shift after discharge. Twenty-nine patients received endovascular treatment within 6-24 hours after symptom onset and had an imaging mismatch based on perfusion CT or diffusion-weighted MR imaging. There were no substantial differences between the 2 groups in 90-day functional independence (P = .54) and the risks of the combination of symptomatic intracranial hemorrhage and death (P = .72). Two significant indicators of NIHSS shift were 48-hour symptomatic intracranial hemorrhage (unstandardized β = 7.28; 95% CI, 3.48-11.1; P < .001) and baseline systolic blood pressure (unstandardized β = 0.08; 95% CI, 0.03-0.14; P = .005). Patients with mild ischemic stroke and large-vessel occlusion in the anterior circulation, an imaging mismatch, and endovascular treatment within 6-24 hours of initial symptoms showed no heterogeneity in the efficacy and safety outcome compared with those treated ≤6 hours from symptom onset.

Characteristics and management of pyogenic liver abscess: A European experience.

Pyogenic liver abscess (PLA) are space-occupying lesions in the liver associated with high morbidity and mortality. The aim of this study is to review an Italian hospital experience in epidemiological, clinical patterns, and management of PLA.We performed a retrospective, descriptive case series at a single center assessing demographic characteristics, presentation patterns, etiological factors, microbiological etiology, and management for patients treated for PLA between 2000 and 2016.Around 109 patients were identified. The majority of patients presented with fever (73%); right upper abdominal pain in 63.3%, vomiting and nausea in 28.4%. The most common laboratory abnormality among included items was increased C-reactive protein and fibrinogen blood levels, respectively, in 98% and 93.9% of cases. Abdominal ultrasound was the diagnostic investigation in 42.4% of cases; CT scan and MR imaging were performed in 51.1% and 3.3% of cases respectively. We observed blood or pus culture study in 99 cases of which only 53.5% came with positive microbial reports. The most common organism identified was Escherichia coli (26.5%), followed by Streptococcus spp (13.2%). Early antibiotic treatment started on all patients and 66.7% of cases required different approaches, Ultrasound or CT-guided needle aspiration of PLA was performed in 13 patients (11%) and percutaneous abscess drainage was performed on 72 patients (67%).PLA is a diagnostically challenging problem due to nonspecific presenting characteristics. The microbiological yield identified was a typical European spectrum with a preponderance of Escherichia coli infections. Once recognized, percutaneous drainage and antibiotic treatment are the mainstay of management for PLA.

Subject specific finite element mesh generation of the pelvis from biplanar x-ray images: application to 120 clinical cases

Several Finite Element (FE) models of the pelvis have been developed to comprehensively assess the onset of pathologies and for clinical and industrial applications. However, because of the difficulties associated with the creation of subject-specific FE mesh from CT scan and MR images, most of the existing models rely on the data of one given individual. Moreover, although several fast and robust methods have been developed for automatically generating tetrahedral meshes of arbitrary geometries, hexahedral meshes are still preferred today because of their distinct advantages but their generation remains an open challenge. Recently, approaches have been proposed for fast 3D reconstruction of bones based on X-ray imaging. In this study, we adapted such an approach for the fast and automatic generation of all-hexahedral subject-specific FE models of the pelvis based on the elastic registration of a generic mesh to the subject-specific target in conjunction with element regularity and quality correction. The technique was successfully tested on a database of 120 3D reconstructions of pelvises from biplanar X-ray images. For each patient, a full hexahedral subject-specific FE mesh was generated with an accurate surface representation.

CT and MR image information fusion scheme using a cascaded framework in ripplet and NSST domain

The fusion of multimodal medical information is considered as an assisted approach for the medical professionals. Computed tomography and magnetic resonance (CT–MR) medical image fusion are able to help the radiologist in precise diagnosis of disease and deciding the required treatment in accord with the patient's condition. Therefore, a cascaded framework is proposed in this study that presents a fusion approach for multimodal medical information in ripplet transform (RT) and non-subsampled shearlet (NSST) domain. The RT and NSST having different features are utilised in a cascade manner that provides several directional decomposition coefficients and increases shift invariance information in the fused images. At the first stage decomposition, a biologically inspired neural model, motivated by novel sum-modified Laplacian and spatial frequency is utilised to fuse the low- and high-frequency coefficients, respectively, and the max fusion rule based on regional energy is utilised at stage 2. This model is used to preserve the redundant information also. The fusion performance is also validated by extensive simulations performed on different CT–MR image datasets of different diseases. Experimental results demonstrate that the proposed method provides better fused images in terms of visual quality along with the quantitative indices compared with several existing fusion approaches.