Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations.

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance. A new loss function was introduced to consider the bounding box loss during training with the proposed Masked-Net. Transfer learning from a male pelvis MRI data with a similar field of view was included. The approaches were compared to the 3D U-Net which was widely used in MRI image segmentation. The data used consisted of 52 volumes obtained from 23 patients with stage IB to IVB cervical cancer across a maximum of 7 weeks of RT with manually contoured labels including the bladder, cervix, gross tumour volume, uterus and rectum. The model was trained and tested with a 5-fold cross validation. Outcomes were evaluated based on the Dice Similarity Coefficients (DSC), the Hausdorff Distance (HD) and the Mean Surface Distance (MSD). The proposed method accounted for the small dataset, large variations in OAR shape and tumour sizes with an average DSC, HD and MSD for all anatomical structures of 0.790, 30.19mm and 3.15mm respectively.

Automated anomaly-aware 3D segmentation of bones and cartilages in knee MR images from the Osteoarthritis Initiative.

In medical image analysis, automated segmentation of multi-component anatomical entities, with the possible presence of variable anomalies or pathologies, is a challenging task. In this work, we develop a multi-step approach using U-Net-based models to initially detect anomalies (bone marrow lesions, bone cysts) in the distal femur, proximal tibia and patella from 3D magnetic resonance (MR) images in individuals with varying grades of knee osteoarthritis. Subsequently, the extracted data are used for downstream tasks involving semantic segmentation of individual bone and cartilage volumes as well as bone anomalies. For anomaly detection, U-Net-based models were developed to reconstruct bone volume profiles of the femur and tibia in images via inpainting so anomalous bone regions could be replaced with close to normal appearances. The reconstruction error was used to detect bone anomalies. An anomaly-aware segmentation network, which was compared to anomaly-naïve segmentation networks, was used to provide a final automated segmentation of the individual femoral, tibial and patellar bone and cartilage volumes from the knee MR images which contain a spectrum of bone anomalies. The anomaly-aware segmentation approach provided up to 58% reduction in Hausdorff distances for bone segmentations compared to the results from anomaly-naïve segmentation networks. In addition, the anomaly-aware networks were able to detect bone anomalies in the MR images with greater sensitivity and specificity (area under the receiver operating characteristic curve [AUC] up to 0.896) compared to anomaly-naïve segmentation networks (AUC up to 0.874).

Computed Tomographic Analysis of Position of Mandibular Canal and Mandibular Foramen in Patients with Mandibular Asymmetry.

Introduction: Mandibular osteotomies in facial asymmetry are complicated by the abnormal position and course of inferior alveolar nerve. This manuscript aims to evaluate the possible variations in the preoperative positions of mandibular canal and mandibular foramen in patients with mandibular asymmetry due to condylar hyperplasia or condylar hypoplasia. Materials & Methods: This study included 15 patients with mandibular asymmetry due to condylar hyperplasia or condylar hypoplasia for which bilateral sagittal split osteotomy (BSSO) was performed as a corrective procedure. The presence/absence and extent of postoperative neurosensory deficiency was recorded subjectively and objectively. The measurements were done using multiplanar reconstruction (MPR) of three-dimensional radiographic imaging and were compared to normal subjects. Discussion: The results revealed that the mandibular canal was closer to the buccal cortex on the affected side and the inferior border on both sides in the region of second molar in condylar hyperplasia. In condylar hypoplasia, the canal was nearer to the inferior border and the alveolar crest in relation to second and third molars respectively on the affected and contralateral sides.The mandibular foramen was also more superior to the occlusal plane on both sides in both condylar hyperplasia and hypoplasia. Conclusion: Based on the study outcomes, the authors propose that assessment of the positions of mandibular canal and mandibular foramen is crucial to avoid postoperative neurosensory deficiencies.

Automated volumetric and statistical shape assessment of cam-type morphology of the femoral head-neck region from clinical 3D magnetic resonance images.

Background: Femoroacetabular impingement (FAI) cam morphology is routinely assessed using manual measurements of two-dimensional (2D) alpha angles which are prone to high rater variability and do not provide direct three-dimensional (3D) data on these osseous formations. We present CamMorph, a fully automated 3D pipeline for segmentation, statistical shape assessment and measurement of cam volume, surface area and height from clinical magnetic resonance (MR) images of the hip in FAI patients. Methods: The novel CamMorph pipeline involves two components: (I) accurate proximal femur segmentation generated by combining the 3D U-net to identify both global (region) and local (edge) features in clinical MR images and focused shape modelling to generate a 3D anatomical model for creating patient-specific proximal femur models; (II) patient-specific anatomical information from 3D focused shape modelling to simulate 'healthy' femoral bone models with cam-affected region constraints applied to the anterosuperior femoral head-neck region to quantify cam morphology in FAI patients. The CamMorph pipeline, which generates patient-specific data within 5 min, was used to analyse multi-site clinical MR images of the hip to measure and assess cam morphology in male (n=56) and female (n=41) FAI patients. Results: There was excellent agreement between manual and CamMorph segmentations of the proximal femur as demonstrated by the mean Dice similarity index (DSI; 0.964±0.006), 95% Hausdorff distance (HD; 2.123±0.876 mm) and average surface distance (ASD; 0.539±0.189 mm) values. Compared to female FAI patients, male patients had a significantly larger median cam volume (969.22 vs. 272.97 mm3, U=240.0, P<0.001), mean surface area [657.36 vs. 306.93 mm2, t(95)=8.79, P<0.001], median maximum-height (3.66 vs. 2.15 mm, U=407.0, P<0.001) and median average-height (1.70 vs. 0.86 mm, U=380.0, P<0.001). Conclusions: The fully automated 3D CamMorph pipeline developed in the present study successfully segmented and measured cam morphology from clinical MR images of the hip in male and female patients with differing FAI severity and pathoanatomical characteristics.

CAN3D: Fast 3D medical image segmentation via compact context aggregation.

Direct automatic segmentation of objects in 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying multiple individual structures with complex geometries within a large volume under investigation. Most deep learning approaches address these challenges by enhancing their learning capability through a substantial increase in trainable parameters within their models. An increased model complexity will incur high computational costs and large memory requirements unsuitable for real-time implementation on standard clinical workstations, as clinical imaging systems typically have low-end computer hardware with limited memory and CPU resources only. This paper presents a compact convolutional neural network (CAN3D) designed specifically for clinical workstations and allows the segmentation of large 3D Magnetic Resonance (MR) images in real-time. The proposed CAN3D has a shallow memory footprint to reduce the number of model parameters and computer memory required for state-of-the-art performance and maintain data integrity by directly processing large full-size 3D image input volumes with no patches required. The proposed architecture significantly reduces computational costs, especially for inference using the CPU. We also develop a novel loss function with extra shape constraints to improve segmentation accuracy for imbalanced classes in 3D MR images. Compared to state-of-the-art approaches (U-Net3D, improved U-Net3D and V-Net), CAN3D reduced the number of parameters up to two orders of magnitude and achieved much faster inference, up to 5 times when predicting with a standard commercial CPU (instead of GPU). For the open-access OAI-ZIB knee MR dataset, in comparison with manual segmentation, CAN3D achieved Dice coefficient values of (mean = 0.87 ± 0.02 and 0.85 ± 0.04) with mean surface distance errors (mean = 0.36 ± 0.32 mm and 0.29 ± 0.10 mm) for imbalanced classes such as (femoral and tibial) cartilage volumes respectively when training volume-wise under only 12G video memory. Similarly, CAN3D demonstrated high accuracy and efficiency on a pelvis 3D MR imaging dataset for prostate cancer consisting of 211 examinations with expert manual semantic labels (bladder, body, bone, rectum, prostate) now released publicly for scientific use as part of this work.

Automated 3D Analysis of Clinical Magnetic Resonance Images Demonstrates Significant Reductions in Cam Morphology Following Arthroscopic Intervention in Contrast to Physiotherapy.

Purpose: To obtain automated measurements of cam volume, surface area, and height from baseline (preintervention) and 12-month magnetic resonance (MR) images acquired from male and female patients allocated to physiotherapy (PT) or arthroscopic surgery (AS) management for femoroacetabular impingement (FAI) in the Australian FASHIoN trial. Methods: An automated segmentation pipeline (CamMorph) was used to obtain cam morphology data from three-dimensional (3D) MR hip examinations in FAI patients classified with mild, moderate, or major cam volumes. Pairwise comparisons between baseline and 12-month cam volume, surface area, and height data were performed within the PT and AS patient groups using paired t-tests or Wilcoxon signed-rank tests. Results: A total of 43 patients were included with 15 PT patients (9 males, 6 females) and 28 AS patients (18 males, 10 females) for premanagement and postmanagement cam morphology assessments. Within the PT male and female patient groups, there were no significant differences between baseline and 12-month mean cam volume (male: 1269 vs 1288 mm3, t[16] = -0.39; female: 545 vs 550 mm,3 t[10] = -0.78), surface area (male: 1525 vs 1491 mm2, t[16] = 0.92; female: 885 vs 925 mm,2 t[10] = -0.78), maximum height (male: 4.36 vs 4.32 mm, t[16] = 0.34; female: 3.05 vs 2.96 mm, t[10] = 1.05) and average height (male: 2.18 vs 2.18 mm, t[16] = 0.22; female: 1.4 vs 1.43 mm, t[10] = -0.38). In contrast, within the AS male and female patient groups, there were significant differences between baseline and 12-month cam volume (male: 1343 vs 718 mm3, W = 0.0; female: 499 vs 240 mm3, t[18] = 2.89), surface area (male: 1520 vs 1031 mm2, t(34) = 6.48; female: 782 vs 483 mm2, t(18) = 3.02), maximum-height (male: 4.3 vs 3.42 mm, W = 13.5; female: 2.85 vs 2.24 mm, t(18) = 3.04) and average height (male: 2.17 vs 1.52 mm, W = 3.0; female: 1.4 vs 0.94 mm, W = 3.0). In AS patients, 3D bone models provided good visualization of cam bone mass removal postostectomy. Conclusions: Automated measurement of cam morphology from baseline (preintervention) and 12-month MR images demonstrated that the cam volume, surface area, maximum-height, and average height were significantly smaller in AS patients following ostectomy, whereas there were no significant differences in these cam measures in PT patients from the Australian FASHIoN study. Level of Evidence: Level II, cohort study.

Surgical Risk Assessment in Patients with Chronic Liver Diseases.

Chronic liver diseases (CLD) is one of the leading causes of morbidity and mortality. The overall life span of patients with CLD has increased and so is the number of surgical procedures these patients undergo. Pathophysiological and hemodynamic changes in cirrhosis make these patients more susceptible to hypotension and hypoxia during surgery. They also have a high risk of drug induced liver injury, renal dysfunction and post-operative liver decompensation. Patients with CLD planned for elective or semi-elective surgery should undergo detailed preoperative risk assessment. Patients should be evaluated for the presence of clinically significant portal hypertension and cirrhosis. In the absence of both cirrhosis and clinically significant portal hypertension, patients with CLD can undergo surgery with minimal or low risk. Various risk assessment tools available for patients with advanced CLD are-CTP score, MELD Score, Mayo risk score, VOCAL-Penn score. A Child class C and/or Mayo risk score >15 in general is associated with high risk of post-operative mortality and elective surgery should be deferred in these patients. In patients with Child class, A and MELD 10-15 surgery is permissible with caution (except liver resection and cardiac surgery) while in Child A and MELD <10 surgery is well tolerated. VOCAL-Penn score is a new promising tool and can be the better alternative of CTP, MELD, and Mayo risk score models but more prospective studies with large patients' population are warranted. Certain surgeries like Hepatic resection, intraabdominal, and cardiothoracic have higher risk than abdominal wall hernia repair and orthopedic surgery. Laparoscopic approaches have better outcomes and less risk of liver failure than open surgery. Minimally invasive alternatives like colonic stent placement in case of obstruction can be considered in high-risk cases. Perioperative optimization and management of ascites, HE, bleeding, liver decompensation, and nutrition should be done with multidisciplinary approach. Patients with cirrhosis undergoing high risk elective surgery can develop liver failure in post-operative period and should be evaluated and counseled for liver transplantation if not contraindicated.

Bilateral chylothorax: A late complication of Kawashima procedure despite normal pulmonary pressures.

INTRODUCTION: Chylothorax, a relatively rare congenital heart disease early postoperative complication, is occurring more frequently due to complexity of cardiac surgeries. PRESENTATION OF CASE: We present a 9-month-old boy who had hypoplastic left heart (HLH) syndrome with interrupted inferior vena cava (IVC) and bilateral superior vena cava (SVC) palliated with left sided modified Blalock-Taussig (MBT) shunt during neonatal period and second stage palliation with left sided bidirectional glen (BDG) procedure and right sided Kawashima procedure develop bilateral chylothorax two weeks after discharge. DISCUSSION: This is the first reported case in the literature of a patient who developed chylothorax with relatively low Fontan systemic venous pressures after a Kawashima procedure. Clinically important chylothorax may be a marker of poor long-term outcomes, demonstrating an inability to handle overwhelming lymphatic congestion. CONCLUSION: Early diagnosis of chylothorax in complex cardiac surgeries may permit successful conservative management.

Stapedotomy using skeeter drill and self-retaining ear canal retractor: a single surgeon's 1000-plus surgical procedure experience.

OBJECTIVE: This study aimed to evaluate the long-term hearing outcomes in stapedotomy surgery using skeeter oto-drill and to assess safety in difficult situations. METHOD: A retrospective study was conducted with 944 patients who underwent 1007 stapedotomy procedures over 16 years, performed by a single surgeon using a trans-canal approach and a self-retaining ear canal retractor. Hearing thresholds were calculated over four frequencies. Air-bone conduction hearing thresholds were obtained at 1, 5 and 10 years post-operatively and compared to the pre-operative records. RESULTS: Out of 1007 operated ears with one year follow up, 98.61 per cent of cases showed a negligible air-bone gap of equal to or less than 5 dB, 1.19 per cent of cases showed an air-bone gap equal to or more than 5 dB but less than 10 dB, and only 0.2 per cent of cases showed an air-bone gap of more than 10 dB. CONCLUSION: In this study, using skeeter drill with a 0.6 mm diamond burr to make the fenestra was constant in all the cases and one of the safest techniques, showing persistent long-term hearing results.

Phosphonomethyl iminodiacetic acid functionalized metal organic framework supported PAN composite beads for selective removal of La(III) from wastewater: Adsorptive performance and column separation studies.

The rare earth elements being toxic in nature are being accumulated in water bodies as their industrial usage is growing exponentially, thus their efficient separation holds an immense significance. Herein, ligand functionalized metal organic framework (MOF), Phosphonomethyl iminodiacetic acid coordinated at Fe-BTC, was synthesized post-synthetically and incorporated subsequently in polyacrylonitrile polymer to prepare the composite beads via nonsolvent induced-phase-inversion technique for selective adsorption of La(III) from the wastewater in batch and dynamic column mode. XPS NMR, and FTIR were used to establish the interaction between functionalized ligand and unsaturated metal nodes of MOF. The adsorption capacity was 232.5 mg/g and 77.51 mg/g at 298 K of the functionalized MOF and composite beads respectively. Adsorption kinetics followed a pseudo-second order rate equation, and isotherm indicated the best fitting with Langmuir model. The dynamic behavior of the adsorption column packed with MOF/Polymer beads was fairly described by the Thomas model. The breakthrough time of 23.2 h could be attained with 12 cm of bed height and 10 ml/min of flow rate. These MOF/Polymer beads shown the selectivity of La over transitional metals were recycled over 5 times with about 15% loss of adsorption capacity. The findings provide suggestive insights of the potential use of functionalized MOF towards the separation of the rare earth element.

Deep learning in magnetic resonance image reconstruction.

Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state-of-the-art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi-contrast imaging. Publications with deep learning-based image reconstruction for MR imaging were identified from the literature (PubMed and Google Scholar), and a comprehensive description of each of the works was provided. A detailed comparison that highlights the differences, the data used and the performance of each of these works were also made. A discussion of the potential use cases for each of these methods is provided. The sparse image reconstruction methods were found to be most popular in using deep learning for improved performance, accelerating acquisitions by around 4-8 times. Multi-contrast image reconstruction methods rely on at least one pre-acquired image, but can achieve 16-fold, and even up to 32- to 50-fold acceleration depending on the set-up. Parallel imaging provides frameworks to be integrated in many of these methods for additional speed-up potential. The successful use of compressed sensing techniques and multi-contrast imaging with deep learning and parallel acquisition methods could yield significant MR acquisition speed-ups within clinical routines in the near future.

Fast geometric distortion correction using a deep neural network: Implementation for the 1 Tesla MRI-Linac system.

PURPOSE: Combining high-resolution magnetic resonance imaging (MRI) with a linear accelerator (Linac) as a single MRI-Linac system provides the capability to monitor intra-fractional motion and anatomical changes during radiotherapy, which facilitates more accurate delivery of radiation dose to the tumor and less exposure to healthy tissue. The gradient nonlinearity (GNL)-induced distortions in MRI, however, hinder the implementation of MRI-Linac system in image-guided radiotherapy where highly accurate geometry and anatomy of the target tumor is indispensable. METHODS: To correct the geometric distortions in MR images, in particular, for the 1 Tesla (T) MRI-Linac system, a deep fully connected neural network was proposed to automatically learn the intricate relationship between the undistorted (theoretical) and distorted (real) space. A dataset, consisting of spatial samples acquired by phantom measurement that covers both inside and outside the working diameter of spherical volume (DSV), was utilized for training the neural network, which offers the ability to describe subtle deviations of the GNL field within the entire region of interest (ROI). RESULTS: The performance of the proposed method was evaluated on MR images of a three-dimensional (3D) phantom and the pelvic region of an adult volunteer scanned in the 1T MRI-Linac system. The experimental results showed that the severe geometric distortions within the entire ROI had been successfully corrected with an error less than the pixel size. Also, the presented network is highly efficient, which achieved significant improvement in terms of computational efficiency compared to existing methods. CONCLUSIONS: The feasibility of the presented deep neural network for characterizing the GNL field deviations in the 1T MRI-Linac system was demonstrated in this study, which shows promise in facilitating the MRI-Linac system to be routinely implemented in real-time MRI-guided radiotherapy.

Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI.

Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is0.72±0.15for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.

Ganglioglioma in children and young adults: single institution experience and review of the literature.

BACKGROUND: Ganglioglioma (GG) is a rare mixed glial-neuronal neoplasm accounting for 0.5-5% of all pediatric central nervous system (CNS) tumors. Rarity of this tumor has precluded defining robust treatment guidelines. This retrospective study evaluates the prognostic factors and outcomes of this rare neoplasm. PATIENTS AND METHODS: Retrospective analysis of 55 patients with GG was conducted to describe clinical findings, and outcomes. Kaplan-Meier survival and Cox-regression analyses were performed to assess the overall survival (OS) and progression-free survival (PFS). RESULTS: The mean age at diagnosis was 11.8 years (range 1-21 years) with a median follow-up period of 9.5 years. 53 patients (92.7%) had low grade GG and 2 patients had anaplastic GG. 25 patients had tumor progression, whose median PFS was 12 years. Six patients with low grade GG progressed to a higher grade, with median survival of 9.1 month after transformation. The 5 and 10 year PFS were 65 and 57%, respectively. The 5 and 10 year OS was 96 and 86% respectively. 8 of the 19 (42%) samples tested demonstrated positivity for the BRAF V600E mutation. Multivariate Cox regression analyses showed location and extent of resection were significant factors for PFS and presence of metastatsis attained significance for OS. CONCLUSION: This is the one of the largest retrospective study of pediatric GG. Identifying clinical variables, which could stratify these tumors into low- and high-risk groups might help to profile a risk-based therapeutic strategy. Collaborative multiinstitutional prospective studies are warranted to delineate treatment consensus and investigate prognostic factors.

Improved Risk Stratification in Pediatric Septic Shock Using Both Protein and mRNA Biomarkers. PERSEVERE-XP.

RATIONALE: We previously derived and validated the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate baseline mortality risk in children with septic shock. The PERSEVERE biomarkers are serum proteins selected from among the proteins directly related to 80 mortality risk assessment genes. The initial approach to selecting the PERSEVERE biomarkers left 68 genes unconsidered. OBJECTIVES: To determine if the 68 previously unconsidered genes can improve upon the performance of PERSEVERE and to provide biological information regarding the pathophysiology of septic shock. METHODS: We reduced the number of variables by determining the biological linkage of the 68 previously unconsidered genes. The genes identified through variable reduction were combined with the PERSEVERE-based mortality probability to derive a risk stratification model for 28-day mortality using classification and regression tree methodology (n = 307). The derived tree, PERSEVERE-XP, was then tested in a separate cohort (n = 77). MEASUREMENTS AND MAIN RESULTS: Variable reduction revealed a network consisting of 18 mortality risk assessment genes related to tumor protein 53 (TP53). In the derivation cohort, PERSEVERE-XP had an area under the receiver operating characteristic curve (AUC) of 0.90 (95% confidence interval, 0.85-0.95) for differentiating between survivors and nonsurvivors. In the test cohort, the AUC was 0.96 (95% confidence interval, 0.91-1.0). The AUC of PERSEVERE-XP was superior to that of PERSEVERE. CONCLUSIONS: PERSEVERE-XP combines protein and mRNA biomarkers to provide mortality risk stratification with possible clinical utility. PERSEVERE-XP significantly improves on PERSEVERE and suggests a role for TP53-related cellular division, repair, and metabolism in the pathophysiology of septic shock.

Fast automated segmentation of multiple objects via spatially weighted shape learning.

Active shape models (ASMs) have proved successful in automatic segmentation by using shape and appearance priors in a number of areas such as prostate segmentation, where accurate contouring is important in treatment planning for prostate cancer. The ASM approach however, is heavily reliant on a good initialisation for achieving high segmentation quality. This initialisation often requires algorithms with high computational complexity, such as three dimensional (3D) image registration. In this work, we present a fast, self-initialised ASM approach that simultaneously fits multiple objects hierarchically controlled by spatially weighted shape learning. Prominent objects are targeted initially and spatial weights are progressively adjusted so that the next (more difficult, less visible) object is simultaneously initialised using a series of weighted shape models. The scheme was validated and compared to a multi-atlas approach on 3D magnetic resonance (MR) images of 38 cancer patients and had the same (mean, median, inter-rater) Dice's similarity coefficients of (0.79, 0.81, 0.85), while having no registration error and a computational time of 12-15 min, nearly an order of magnitude faster than the multi-atlas approach.

Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences.

PURPOSE: To validate automatic substitute computed tomography CT (sCT) scans generated from standard T2-weighted (T2w) magnetic resonance (MR) pelvic scans for MR-Sim prostate treatment planning. PATIENTS AND METHODS: A Siemens Skyra 3T MR imaging (MRI) scanner with laser bridge, flat couch, and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole-pelvis MRI scan (1.6 mm 3-dimensional isotropic T2w SPACE [Sampling Perfection with Application optimized Contrasts using different flip angle Evolution] sequence) was acquired. Three additional small field of view scans were acquired: T2w, T2*w, and T1w flip angle 80° for gold fiducials. Patients received a routine planning CT scan. Manual contouring of the prostate, rectum, bladder, and bones was performed independently on the CT and MR scans. Three experienced observers contoured each organ on MRI, allowing interobserver quantification. To generate a training database, each patient CT scan was coregistered to their whole-pelvis T2w using symmetric rigid registration and structure-guided deformable registration. A new multi-atlas local weighted voting method was used to generate automatic contours and sCT results. RESULTS: The mean error in Hounsfield units between the sCT and corresponding patient CT (within the body contour) was 0.6 ± 14.7 (mean ± 1 SD), with a mean absolute error of 40.5 ± 8.2 Hounsfield units. Automatic contouring results were very close to the expert interobserver level (Dice similarity coefficient): prostate 0.80 ± 0.08, bladder 0.86 ± 0.12, rectum 0.84 ± 0.06, bones 0.91 ± 0.03, and body 1.00 ± 0.003. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same dose prescription was found to be 0.3% ± 0.8%. The 3-dimensional γ pass rate was 1.00 ± 0.00 (2 mm/2%). CONCLUSIONS: The MR-Sim setup and automatic sCT generation methods using standard MR sequences generates realistic contours and electron densities for prostate cancer radiation therapy dose planning and digitally reconstructed radiograph generation.

Lung cancer imaging.

Lung cancer remains the leading cause of cancer-related deaths in the US. Imaging plays an important role in the diagnosis, staging, and follow-up evaluation of patients with lung cancer. With recent advances in technology, it is important to update and standardize the radiological practices in lung cancer evaluation. In this article, the authors review the main clinical applications of different imaging modalities and the most common radiological presentations of lung cancer.