Withdrawal of bevacizumab is associated with rebound growth of vestibular schwannomas in neurofibromatosis type 2-related schwannomatosis patients.

Background: Neurofibromatosis type 2 (NF2)-related schwannomatosis is an autosomal dominant tumor-predisposition syndrome characterized by bilateral vestibular schwannomas (VS). In patients with VS associated with NF2, vascular endothelial growth factor A inhibitor, bevacizumab, is a systemic treatment option. The aim of this study is to retrospectively evaluate NF2 patient responses to bevacizumab on VS growth and symptom progression. Methods: This is a retrospective analysis of patients seen at the Mayo Clinic Rochester Multidisciplinary NF2 Clinic. Results: Out of 76 patients with NF2 evaluated between 2020 and 2022, we identified 19 that received treatment with bevacizumab. Thirteen of these patients discontinued bevacizumab after median treatment duration of 12.2 months. The remaining 6 patients are currently receiving bevacizumab treatment for a median duration of 9.4 months as of March, 2023. Fifteen patients had evaluable brain MRI data, which demonstrated partial responses in 5 patients, stable disease in 8, and progression in 2. Within 6 months of bevacizumab discontinuation, 5 patients had rebound growth of their VS greater than 20% from their previous tumor volume, while 3 did not. Three patients with rebound growth went on to have surgery or irradiation for VS management. Conclusions: Our single-institution experience confirms prior studies that bevacizumab can control progression of VS and symptoms associated with VS growth. However, we note that there is the potential for rapid VS growth following bevacizumab discontinuation, for which we propose heightened surveillance imaging and symptom monitoring for at least 6 months upon stopping anti-VEGF therapy.

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

OBJECTIVE: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND METHODS: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. RESULTS: Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). CONCLUSION: Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION: Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS: This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS: For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION: Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.

Impact of preoperative sarcopenia in patients undergoing sacral tumor resection.

INTRODUCTION: Sacral tumor resection is known for a high rate of complications. Sarcopenia has been found to be associated with wound complications; however, there is a paucity of data examining the impact of sarcopenia on the outcome of sacral tumor resection. METHODS: Forty-eight patients (31 primary sarcomas, 17 locally recurrent carcinomas) undergoing sacrectomy were reviewed. Central sarcopenia was assessed by measuring the psoas:lumbar vertebra index (PLVI), with the 50th percentile (0.97) used to determine which patients were high (>0.97) versus low (<0.97). RESULTS: Twenty-four (50%) patients had a high PLVI and 24 (50%) had a low PLVI (sarcopenic). There was no difference (p > 0.05) in the demographics of patients with or without sarcopenia. There was no difference in the incidence of postoperative wound complications (odds ratio [OR] = 1.0, p = 1.0) or deep infection (OR = 0.83, p = 1.0). Sarcopenia was not associated with death due to disease (hazard ratio [HR] = 2.04, p = 0.20) or metastatic disease (HR = 2.47, p = 0.17), but was associated with local recurrence (HR = 6.60, p = 0.01). CONCLUSIONS: Central sarcopenia was not predictive of wound complications or infection following sacral tumor resection. Sarcopenia was, however, an independent risk factor for local tumor recurrence following sacrectomy and should be considered when counseling patients on the outcome of sacrectomy.

AI Integration in the Clinical Workflow.

Machine learning and artificial intelligence (AI) algorithms hold significant promise for addressing important clinical needs when applied to medical imaging; however, integration of algorithms into a radiology department is challenging. Vended algorithms are integrated into the workflow, successfully, but are typically closed systems and unavailable for site researchers to deploy algorithms. Rather than AI researchers creating one-off solutions, a general, multi-purpose integration system is desired. Here, we present a set of use cases and requirements for a system designed to enable rapid deployment of AI algorithms into the radiologist's workflow. The system uses standards-compliant digital imaging and communications in medicine structured reporting (DICOM SR) to present AI measurements, results, and findings to the radiologist in a clinical context and enables acceptance or rejection of results. The system also implements a feedback mechanism for post-processing technologists to correct results as directed by the radiologist. We demonstrate integration of a body composition algorithm and an algorithm for determining total kidney volume for patients with polycystic kidney disease.

Deep Learning Improves the Temporal Reproducibility of Aortic Measurement.

Imaging-based measurements form the basis of surgical decision making in patients with aortic aneurysm. Unfortunately, manual measurement suffer from suboptimal temporal reproducibility, which can lead to delayed or unnecessary intervention. We tested the hypothesis that deep learning could improve upon the temporal reproducibility of CT angiography-derived thoracic aortic measurements in the setting of imperfect ground-truth training data. To this end, we trained a standard deep learning segmentation model from which measurements of aortic volume and diameter could be extracted. First, three blinded cardiothoracic radiologists visually confirmed non-inferiority of deep learning segmentation maps with respect to manual segmentation on a 50-patient hold-out test cohort, demonstrating a slight preference for the deep learning method (p < 1e-5). Next, reproducibility was assessed by evaluating measured change (coefficient of reproducibility and standard deviation) in volume and diameter values extracted from segmentation maps in patients for whom multiple scans were available and whose aortas had been deemed stable over time by visual assessment (n = 57 patients, 206 scans). Deep learning temporal reproducibility was superior for measures of both volume (p < 0.008) and diameter (p < 1e-5) and reproducibility metrics compared favorably with previously reported values of manual inter-rater variability. Our work motivates future efforts to apply deep learning to aortic evaluation.

Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network.

Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133µm to 35µm, which is smaller than the ultrasound wavelength (73µm). The proposed method was further tested in anin vivohuman liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170µm (FWHM). Adjacent microvessels with a distance of 670µm, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.

Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase.

PURPOSE: To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance. METHODS: In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis. RESULTS: Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [- 2.74 cc (min - 92.96 cc, max 87.47 cc) versus - 23.57 cc (min - 77.32, max 30.19)]. CONCLUSION: Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.

MRI and tractography techniques to localize the ventral intermediate nucleus and dentatorubrothalamic tract for deep brain stimulation and MR-guided focused ultrasound: a narrative review and update.

The thalamic ventral intermediate nucleus (VIM) can be targeted for treatment of tremor by several procedures, including deep brain stimulation (DBS) and, more recently, MR-guided focused ultrasound (MRgFUS). To date, such targeting has relied predominantly on coordinate-based or atlas-based techniques rather than directly targeting the VIM based on imaging features. While general regional differences of features within the thalamus and some related white matter tracts can be distinguished with conventional imaging techniques, internal nuclei such as the VIM are not discretely visualized. Advanced imaging methods such as quantitative susceptibility mapping (QSM) and fast gray matter acquisition T1 inversion recovery (FGATIR) MRI and high-field MRI pulse sequences that improve the ability to image the VIM region are emerging but have not yet been shown to have reliability and accuracy to serve as the primary method of VIM targeting. Currently, the most promising imaging approach to directly identify the VIM region for clinical purposes is MR diffusion tractography.In this review and update, the capabilities and limitations of conventional and emerging advanced methods for evaluation of internal thalamic anatomy are briefly reviewed. The basic principles of tractography most relevant to VIM targeting are provided for familiarization. Next, the key literature to date addressing applications of DTI and tractography for DBS and MRgFUS is summarized, emphasizing use of direct targeting. This literature includes 1-tract (dentatorubrothalamic tract [DRT]), 2-tract (pyramidal and somatosensory), and 3-tract (DRT, pyramidal, and somatosensory) approaches to VIM region localization through tractography.The authors introduce a 3-tract technique used at their institution, illustrating the oblique curved course of the DRT within the inferior thalamus as well as the orientation and relationship of the white matter tracts in the axial plane. The utility of this 3-tract tractography approach to facilitate VIM localization is illustrated with case examples of variable VIM location, targeting superior to the anterior commissure-posterior commissure plane, and treatment in the setting of pathologic derangement of thalamic anatomy. Finally, concepts demonstrated with these case examples and from the prior literature are synthesized to highlight several potential advantages of tractography for VIM region targeting.

Advanced MRI techniques for transcranial high intensity focused ultrasound targeting.

Magnetic resonance guided high intensity focused ultrasound is a novel, non-invasive, image-guided procedure that is able to ablate intracranial tissue with submillimetre precision. It is currently FDA approved for essential tremor and tremor dominant Parkinson's disease. The aim of this update is to review the limitations of current landmark-based targeting techniques of the ventral intermediate nucleus and demonstrate the role of emerging imaging techniques that are relevant for both magnetic resonance guided high intensity focused ultrasound and deep brain stimulation. A significant limitation of standard MRI sequences is that the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei cannot be clearly identified. This paper provides original, annotated images demarcating the ventral intermediate nucleus, dentatorubrothalamic tract, and other deep brain nuclei on advanced MRI sequences such as fast grey matter acquisition T1 inversion recovery, quantitative susceptibility mapping, susceptibility weighted imaging, and diffusion tensor imaging tractography. Additionally, the paper reviews clinical efficacy of targeting with these novel MRI techniques when compared to current established landmark-based targeting techniques. The paper has widespread applicability to both deep brain stimulation and magnetic resonance guided high intensity focused ultrasound.

Current concepts of cross-sectional and functional anatomy of the cerebellum: a pictorial review and atlas.

Recognition of key concepts of structural and functional anatomy of the cerebellum can facilitate image interpretation and clinical correlation. Recently, the human brain mapping literature has increased our understanding of cerebellar anatomy, function, connectivity with the cerebrum, and significance of lesions involving specific areas.Both the common names and numerically based Schmahmann classifications of cerebellar lobules are illustrated. Anatomic patterns, or signs, of key fissures and white matter branching are introduced to facilitate easy recognition of the major anatomic features. Color-coded overlays of cross-sectional imaging are provided for reference of more complex detail. Examples of exquisite detail of structural and functional cerebellar anatomy at 7 T MRI are also depicted.The functions of the cerebellum are manifold with the majority of areas involved with non-motor association function. Key concepts of lesion-symptom mapping which correlates lesion location to clinical manifestation are introduced, emphasizing that lesions in most areas of the cerebellum are associated with predominantly non-motor deficits. Clinical correlation is reinforced with examples of intrinsic pathologic derangement of cerebellar anatomy and altered functional connectivity due to pathology of the cerebral hemisphere. The purpose of this pictorial review is to illustrate basic concepts of these topics in a cross-sectional imaging-based format that can be easily understood and applied by radiologists.

Remote ischemic preconditioning for elective endovascular intracranial aneurysm repair: a feasibility study.

OBJECTIVES: Remote ischemic preconditioning has been proposed as a possible potential treatment for ischemic stroke. However, neuroprotective benefits of the pre-procedural administration of remote ischemic preconditioning have not been investigated in patients undergoing an elective endovascular intracranial aneurysm repair procedure. This study investigated the safety and feasibility of remote ischemic preconditioning in patients with an unruptured intracranial aneurysm who undergo elective endovascular treatment. METHODS: In this single-center prospective study, patients with an unruptured intracranial aneurysm undergoing elective endovascular treatment with flow diverters or coiling were recruited. Patients received three intermittent cycles of 5 minutes arm ischemia followed by reperfusion using manual blood cuff inflation/deflation less than 5 hours prior to endovascular treatment. Patients were monitored and followed up for remote ischemic preconditioning-related adverse events and ischemic brain lesions by diffusion -weighted magnetic resonance imaging within 48 hours following endovascular treatment. RESULTS: A total of seven patients aged 60 ± 5 years with an unruptured intracranial aneurysm successfully completed a total of 21 sessions of remote ischemic preconditioning and the required procedures. Except for two patients who developed skin petechiae over their arms, no other serious procedure-related adverse events were observed as a result of the remote ischemic preconditioning procedure. On follow-up diffusion -weighted magnetic resonance imaging, a total of 19 ischemic brain lesions with a median (interquartile range) volume of 245 (61-466) mm3 were found in four out of seven patients. CONCLUSIONS: The application of remote ischemic preconditioning prior to endovascular intracranial aneurysm repair was well tolerated, safe and clinically feasible. Larger sham-controlled clinical trials are required to determine the safety and efficacy of this therapeutic strategy in mitigating ischemic damage following endovascular treatment of intracranial aneurysms.

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation.

PURPOSE: Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. METHODS: CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. RESULTS: The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10-16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. CONCLUSION: Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.

A quantitative symmetry-based analysis of hyperacute ischemic stroke lesions in noncontrast computed tomography.

PURPOSE: Early identification of ischemic stroke plays a significant role in treatment and potential recovery of damaged brain tissue. In noncontrast CT (ncCT), the differences between ischemic changes and healthy tissue are usually very subtle during the hyperacute phase (< 8 h from the stroke onset). Therefore, visual comparison of both hemispheres is an important step in clinical assessment. A quantitative symmetry-based analysis of texture features of ischemic lesions in noncontrast CT images may provide an important information for differentiation of ischemic and healthy brain tissue in this phase. METHODS: One hundred thirty-nine (139) ncCT scans of hyperacute ischemic stroke with follow-up magnetic resonance diffusion-weighted (MR-DW) images were collected. The regions of stroke were identified in the MR-DW images, which were spatially aligned to corresponding ncCT images. A state-of-the-art symmetric diffeomorphic image registration was utilized for the alignment of CT and MR-DW, for identification of individual brain hemispheres, and for localization of the region representing healthy tissue contralateral to the stroke cores. Texture analysis included extraction and classification of co-occurrence and run-length texture-based image features in the regions of ischemic stroke and their contralateral regions. RESULTS: The classification schemes achieved area under the receiver operating characteristic [Az] ≈ 0.82 for the whole dataset. There was no statistically significant difference in the performance of classifiers for the data sets with time between 2 and 8 hours from symptom onset. The performance of the classifiers did not depend on the size of the stroke regions. CONCLUSIONS: The results provide a set of optimal texture features which are suitable for distinguishing between hyperacute ischemic lesions and their corresponding contralateral brain tissue in noncontrast CT. This work is an initial step toward development of an automated decision support system for detection of hyperacute ischemic stroke lesions on noncontrast CT of the brain.

Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience.

The National Alliance for Medical Image Computing (NA-MIC) was launched in 2004 with the goal of investigating and developing an open source software infrastructure for the extraction of information and knowledge from medical images using computational methods. Several leading research and engineering groups participated in this effort that was funded by the US National Institutes of Health through a variety of infrastructure grants. This effort transformed 3D Slicer from an internal, Boston-based, academic research software application into a professionally maintained, robust, open source platform with an international leadership and developer and user communities. Critical improvements to the widely used underlying open source libraries and tools-VTK, ITK, CMake, CDash, DCMTK-were an additional consequence of this effort. This project has contributed to close to a thousand peer-reviewed publications and a growing portfolio of US and international funded efforts expanding the use of these tools in new medical computing applications every year. In this editorial, we discuss what we believe are gaps in the way medical image computing is pursued today; how a well-executed research platform can enable discovery, innovation and reproducible science ("Open Science"); and how our quest to build such a software platform has evolved into a productive and rewarding social engineering exercise in building an open-access community with a shared vision.

MIRMAID: A Content Management System for Medical Image Analysis Research.

Today, a typical clinical study can involve thousands of participants, with imaging data acquired over several time points across multiple institutions. The additional associated information (metadata) accompanying these data can cause data management to be a study-hindering bottleneck. Consistent data management is crucial for large-scale modern clinical imaging research studies. If the study is to be used for regulatory submissions, such systems must be able to meet regulatory compliance requirements for systems that manage clinical image trials, including protecting patient privacy. Our aim was to develop a system to address these needs by leveraging the capabilities of an open-source content management system (CMS) that has a highly configurable workflow; has a single interface that can store, manage, and retrieve imaging-based studies; and can handle the requirement for data auditing and project management. We developed a Web-accessible CMS for medical images called Medical Imaging Research Management and Associated Information Database (MIRMAID). From its inception, MIRMAID was developed to be highly flexible and to meet the needs of diverse studies. It fulfills the need for a complete system for medical imaging research management.

Observer Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction.

PURPOSE: To determine if lower-dose computed tomographic (CT) scans obtained with adaptive image-based noise reduction (adaptive nonlocal means [ANLM]) or iterative reconstruction (sinogram-affirmed iterative reconstruction [SAFIRE]) result in reduced observer performance in the detection of malignant hepatic nodules and masses compared with routine-dose scans obtained with filtered back projection (FBP). MATERIALS AND METHODS: This study was approved by the institutional review board and was compliant with HIPAA. Informed consent was obtained from patients for the retrospective use of medical records for research purposes. CT projection data from 33 abdominal and 27 liver or pancreas CT examinations were collected (median volume CT dose index, 13.8 and 24.0 mGy, respectively). Hepatic malignancy was defined by progression or regression or with histopathologic findings. Lower-dose data were created by using a validated noise insertion method (10.4 mGy for abdominal CT and 14.6 mGy for liver or pancreas CT) and images reconstructed with FBP, ANLM, and SAFIRE. Four readers evaluated routine-dose FBP images and all lower-dose images, circumscribing liver lesions and selecting diagnosis. The jackknife free-response receiver operating characteristic figure of merit (FOM) was calculated on a per-malignant nodule or per-mass basis. Noninferiority was defined by the lower limit of the 95% confidence interval (CI) of the difference between lower-dose and routine-dose FOMs being less than -0.10. RESULTS: Twenty-nine patients had 62 malignant hepatic nodules and masses. Estimated FOM differences between lower-dose FBP and lower-dose ANLM versus routine-dose FBP were noninferior (difference: -0.041 [95% CI: -0.090, 0.009] and -0.003 [95% CI: -0.052, 0.047], respectively). In patients with dedicated liver scans, lower-dose ANLM images were noninferior (difference: +0.015 [95% CI: -0.077, 0.106]), whereas lower-dose FBP images were not (difference -0.049 [95% CI: -0.140, 0.043]). In 37 patients with SAFIRE reconstructions, the three lower-dose alternatives were found to be noninferior to the routine-dose FBP. CONCLUSION: At moderate levels of dose reduction, lower-dose FBP images without ANLM or SAFIRE were noninferior to routine-dose images for abdominal CT but not for liver or pancreas CT.

Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT.

Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since image noise cannot be fully characterized on the sole basis of the noise level at computed tomography (CT). Noise spatial correlation (or noise texture) is closely related to the detection and characterization of low-contrast objects and may be quantified by analyzing the noise power spectrum. High-contrast spatial resolution can be measured using the modulation transfer function and section sensitivity profile and is generally unaffected by noise reduction. Detectability of low-contrast lesions can be evaluated subjectively at varying dose levels using phantoms containing low-contrast objects. Clinical applications with inherent high-contrast abnormalities (eg, CT for renal calculi, CT enterography) permit larger dose reductions with denoising techniques. In low-contrast tasks such as detection of metastases in solid organs, dose reduction is substantially more limited by loss of lesion conspicuity due to loss of low-contrast spatial resolution and coarsening of noise texture. Existing noise reduction strategies for dose reduction have a substantial impact on lowering the radiation dose at CT. To preserve the diagnostic benefit of CT examination, thoughtful utilization of these strategies must be based on the inherent lesion-to-background contrast and the anatomy of interest. The authors provide an overview of existing noise reduction strategies for low-dose abdominopelvic CT, including analytic reconstruction, image and projection space denoising, and iterative reconstruction; review qualitative and quantitative tools for evaluating these strategies; and discuss the strengths and limitations of individual noise reduction methods.

Adaptive nonlocal means filtering based on local noise level for CT denoising.

PURPOSE: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. METHODS: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. RESULTS: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. CONCLUSIONS: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

DEWEY: the DICOM-enabled workflow engine system.

Workflow is a widely used term to describe the sequence of steps to accomplish a task. The use of workflow technology in medicine and medical imaging in particular is limited. In this article, we describe the application of a workflow engine to improve workflow in a radiology department. We implemented a DICOM-enabled workflow engine system in our department. We designed it in a way to allow for scalability, reliability, and flexibility. We implemented several workflows, including one that replaced an existing manual workflow and measured the number of examinations prepared in time without and with the workflow system. The system significantly increased the number of examinations prepared in time for clinical review compared to human effort. It also met the design goals defined at its outset. Workflow engines appear to have value as ways to efficiently assure that complex workflows are completed in a timely fashion.