Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177 Lu-DOTATATE therapy

BACKGROUND

Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship.

PURPOSE

We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data.

METHODS

Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects.

RESULTS

We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case.

CONCLUSIONS

The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues.

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study

BACKGROUND

Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context.

METHODS

In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics.

FINDINGS

Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features.

INTERPRETATION

This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer.

FUNDING

National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Institutional Strategies to Maintain and Grow Imaging Research During the COVID-19 Pandemic

Understanding imaging research experiences, challenges, and strategies for academic radiology departments during and after COVID-19 is critical to prepare for future disruptive events. We summarize key insights and programmatic initiatives at major academic hospitals across the world, based on literature review and meetings of the Radiological Society of North America Vice Chairs of Research (RSNA VCR) group. Through expert discussion and case studies, we provide suggested guidelines to maintain and grow radiology research in the postpandemic era.

AAPM Report 373: The content, structure, and value of the Professional Doctorate in Medical Physics (DMP)

The Professional Doctorate in Medical Physics (DMP) was originally conceived as a solution to the shortage of medical physics residency training positions. While this shortage has now been largely satisfied through conventional residency training positions, the DMP has expanded to multiple institutions and grown into an educational pathway that provides specialized clinical training and extends well beyond the creation of additional training spots. As such, it is important to reevaluate the purpose and the value of the DMP. Additionally, it is important to outline the defining characteristics of the DMP to assure that all existing and future programs provide this anticipated value. Since the formation and subsequent accreditation of the first DMP program in 2009–2010, four additional programs have been created and accredited. However, no guidelines have yet been recommended by the American Association of Physicists in Medicine. CAMPEP accreditation of these programs has thus far been based only on the respective graduate and residency program standards. This allows the development and operation of DMP programs which contain only the requisite Master of Science (MS) coursework and a 2‐year clinical training program. Since the MS plus 2‐year residency pathway already exists, this form of DMP does not provide added value, and one may question why this existing pathway should be considered a doctorate. Not only do we, as a profession, need to outline the defining characteristics of the DMP, we need to carefully evaluate the potential advantages and disadvantages of this pathway within our education and training infrastructure. The aims of this report from the Working Group on the Professional Doctorate Degree for Medical Physicists (WGPDMP) are to (1) describe the current state of the DMP within the profession, (2) make recommendations on the structure and content of the DMP for existing and new DMP programs, and (3) evaluate the value of the DMP to the profession of medical physics.

Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia

Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL) is characterized morphologically by numerous small lymphocytes and pale nodules composed of prolymphocytes and paraimmunoblasts known as proliferation centers (PCs). Patients with CLL can undergo transformation to a more aggressive lymphoma, most often diffuse large B-cell lymphoma (DLBCL), known as Richter transformation (RT). An accelerated phase of CLL (aCLL) also may be observed which correlates with subsequent transformation to DLBCL, and may represent an early stage of transformation. Distinguishing PCs in CLL from aCLL or RT can be diagnostically challenging, particularly in small needle biopsy specimens. Available guidelines pertaining to distinguishing CLL from its' progressive forms are limited, subject to the morphologist's experience and are often not completely helpful in the assessment of scant biopsy specimens. To objectively assess the extent of PCs in aCLL and RT, and enhance diagnostic accuracy, we sought to design an artificial intelligence (AI)-based tool to identify and delineate PCs based on feature analysis of the combined individual nuclear size and intensity, designated here as the heat value. Using the mean heat value from the generated heat value image of all cases, we were able to reliably separate CLL, aCLL and RT with sensitive diagnostic predictive values.

One-Pot, One-Step Synthesis of Drug-Loaded Magnetic Multimicelle Aggregates

Lipid-based formulations provide a nanotechnology platform that is widely used in a variety of biomedical applications because it has several advantageous properties including biocompatibility, reduced toxicity, relative ease of surface modifications, and the possibility for efficient loading of drugs, biologics, and nanoparticles. A combination of lipid-based formulations with magnetic nanoparticles such as iron oxide was shown to be highly advantageous in a growing number of applications including magnet-mediated drug delivery and image-guided therapy. Currently, lipid-based formulations are prepared by multistep protocols. Simplification of the current multistep procedures can lead to a number of important technological advantages including significantly decreased processing time, higher reaction yield, better product reproducibility, and improved quality. Here, we introduce a one-pot, single-step synthesis of drug-loaded magnetic multimicelle aggregates (MaMAs), which is based on controlled flow infusion of an iron oxide nanoparticle/lipid mixture into an aqueous drug solution under ultrasonication. Furthermore, we prepared molecular-targeted MaMAs by directional antibody conjugation through an Fc moiety using Cu-free click chemistry. Fluorescence imaging and quantification confirmed that antibody-conjugated MaMAs showed high cell-specific targeting that was enhanced by magnetic delivery.

Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma

PURPOSE

The aim of this study was to develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients.

PATIENTS AND METHODS

One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib was determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: SUVmax, metabolic tumor volume, and total lesion glycolysis.

RESULTS

The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), metabolic tumor volume (ROC AUC, 0.579; P = 0.412), total lesion glycolysis (ROC AUC, 0.576; P = 0.199), and a composite model built using all 3 (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model's performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomic model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001).

CONCLUSIONS

We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.

A deep-learning-based prediction model for the biodistribution of 90 Y microspheres in liver radioembolization

BACKGROUND

Radioembolization with ⁹⁰ Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics.

PURPOSE

The purpose of the present study was to employ deep learning (DL) algorithms to differentiate patterns of pretreatment distribution of ^99m Tc-macroaggregated albumin on SPECT/CT and post-treatment distribution of ⁹⁰ Y microspheres on PET/CT and to accurately predict how the ⁹⁰ Y-microspheres will be distributed in the liver tissue by radioembolization therapy.

METHODS

Data for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization with ⁹⁰ Y microspheres were used for the DL training. We developed a 3D voxel-based variation of the Pix2Pix model, which is a special type of conditional GANs designed to perform image-to-image translation. SPECT and CT scans along with the clinical target volume for each patient were used as inputs, as were their corresponding post-treatment PET scans. The real and predicted absorbed PET doses for the tumor and the whole liver area were compared. Our model was evaluated using the leave-one-out method, and the dose calculations were measured using a tissue-specific dose voxel kernel.

RESULTS

The comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy.

CONCLUSIONS

The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted ⁹⁰ Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning.

Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans

GOAL

To evaluate the ability of radiomic feature extraction and a machine learning algorithm to differentiate between benign and malignant indeterminate adrenal lesions on contrast-enhanced computed tomography (CT) studies.

BACKGROUND

Adrenal "incidentalomas" are adrenal lesions that are accidentally discovered during workup not related to the adrenal glands; they have an incidence as high as 5%. Small adrenal incidentalomas (< 4 cm) with high attenuation values on pre-contrast CT(> 10 HU) need further evaluation to calculate the absolute percentage of washout (APW). If the APW is < 60%, these lesions are considered non-adenomas and commonly classified as indeterminate adrenal lesions. Further workup for indeterminate lesions includes more complicated and expensive radiological studies or invasive procedures like biopsy or surgical resection.

METHODS

We searched our institutional database for indeterminate adrenal lesions with the following characteristics: < 4 cm, pre-attenuation value > 10 HU, and APW < 60%. Exclusion criteria included pheochromocytoma and no histopathological examination. CT images were converted to Nifti format, and adrenal tumors were segmented using Amira software. Radiomic features from the adrenal mask were extracted using PyRadiomics software after removing redundant features (highly pairwise correlated features and low-variance features) using recursive feature extraction to select the final discriminative set of features. Lastly, the final features were used to build a binary classification model using a random forest algorithm, which was validated and tested using leave-one-out cross-validation, confusion matrix, and receiver operating characteristic curve.

RESULTS

We found 40 indeterminate adrenal lesions (21 benign and 19 malignant). Feature extraction resulted in 3947 features, which reduced down to 62 features after removing redundancies. Recursive feature elimination resulted in the following top 4 discriminative features: gray-level size zone matrix-derived size zone non-uniformity from pre-contrast and delayed phases, gray-level dependency matrix-derived large dependence high gray-level emphasis from venous-phase, and gray-level co-occurrence matrix-derived cluster shade from delayed-phase. A binary classification model with leave-one-out cross-validation showed AUC = 0.85, sensitivity = 84.2%, and specificity = 71.4%.

CONCLUSION

Machine learning and radiomic features extraction can differentiate between benign and malignant indeterminate adrenal tumors and can be used to direct further workup with high sensitivity and specificity.

Findings of the AAPM Ad Hoc committee on magnetic resonance imaging in radiation therapy: Unmet needs, opportunities, and recommendations

The past decade has seen the increasing integration of magnetic resonance (MR) imaging into radiation therapy (RT). This growth can be contributed to multiple factors, including hardware and software advances that have allowed the acquisition of high-resolution volumetric data of RT patients in their treatment position (also known as MR simulation) and the development of methods to image and quantify tissue function and response to therapy. More recently, the advent of MR-guided radiation therapy (MRgRT) - achieved through the integration of MR imaging systems and linear accelerators - has further accelerated this trend. As MR imaging in RT techniques and technologies, such as MRgRT, gain regulatory approval worldwide, these systems will begin to propagate beyond tertiary care academic medical centers and into more community-based health systems and hospitals, creating new opportunities to provide advanced treatment options to a broader patient population. Accompanying these opportunities are unique challenges related to their adaptation, adoption, and use including modification of hardware and software to meet the unique and distinct demands of MR imaging in RT, the need for standardization of imaging techniques and protocols, education of the broader RT community (particularly in regards to MR safety) as well as the need to continue and support research, and development in this space. In response to this, an ad hoc committee of the American Association of Physicists in Medicine (AAPM) was formed to identify the unmet needs, roadblocks, and opportunities within this space. The purpose of this document is to report on the major findings and recommendations identified. Importantly, the provided recommendations represent the consensus opinions of the committee's membership, which were submitted in the committee's report to the AAPM Board of Directors. In addition, AAPM ad hoc committee reports differ from AAPM task group reports in that ad hoc committee reports are neither reviewed nor ultimately approved by the committee's parent groups, including at the council and executive committee level. Thus, the recommendations given in this summary should not be construed as being endorsed by or official recommendations from the AAPM.

Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels

Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with ⁹⁰Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Materials and Methods: Two tissue-specific DVKs for ⁹⁰Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations. A method was developed to produce a 3D dose map by convolving the calculated DVKs with the activity biodistribution derived from clinical single-photon emission computed tomography (SPECT) or positron emission tomography (PET) images. Image registration for the SPECT or PET images with the corresponding computed tomography scans was performed before dosimetry calculation. The authors first compared the DVK convolution dosimetry with a direct full MC simulation on an XCAT anthropomorphic phantom. They then tested it in 25 individual clinical cases of patients who underwent ⁹⁰Y therapy. All MC simulations were carried out using the GATE MC toolkit. Results: Comparison of the measured absorbed dose using tissue-specific DVKs and direct MC simulation on 25 patients revealed a mean difference of 1.07% ± 1.43% for the liver and 1.03% ± 1.21% for the tumor tissue, respectively. The largest difference between DVK convolution and full MC dosimetry was observed for the lung tissue (10.16% ± 1.20%). The DVK statistical uncertainty was <0.75% for both media. Conclusions: This semiautomatic algorithm is capable of performing rapid, accurate, and efficient 3D dosimetry. The proposed method considers tissue and activity heterogeneity using tissue-specific DVKs. Furthermore, this method provides results in <1 min, making it suitable for everyday clinical practice.

Estimating Local Cellular Density in Glioma Using MR Imaging Data

BACKGROUND AND PURPOSE

Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which cellular density can be quantitatively estimated from imaging is unknown. The purpose of this study was to discover the best MR imaging and processing techniques to make quantitative and spatially specific estimates of cellular density.

MATERIALS AND METHODS

We collected stereotactic biopsies in a prospective imaging clinical trial targeting untreated patients with gliomas at our institution undergoing their first resection. The data included preoperative MR imaging with conventional anatomic, diffusion, perfusion, and permeability sequences and quantitative histopathology on biopsy samples. We then used multiple machine learning methodologies to estimate cellular density using local intensity information from the MR images and quantitative cellular density measurements at the biopsy coordinates as the criterion standard.

RESULTS

The random forest methodology estimated cellular density with R 2 = 0.59 between predicted and observed values using 4 input imaging sequences chosen from our full set of imaging data (T2, fractional anisotropy, CBF, and area under the curve from permeability imaging). Limiting input to conventional MR images (T1 pre- and postcontrast, T2, and FLAIR) yielded slightly degraded performance (R2 = 0.52). Outputs were also reported as graphic maps.

CONCLUSIONS

Cellular density can be estimated with moderate-to-strong correlations using MR imaging inputs. The random forest machine learning model provided the best estimates. These spatially specific estimates of cellular density will likely be useful in guiding both diagnosis and treatment.

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive technique among radiologists. Although many studies have been published validating the SWE technique either in a clinical setting, or by applying machine learning on SWE elastograms, minimal work has been done on comparing the performance of popular pre-trained deep learning networks on CLD assessment. Currently available literature reports suggest technical advancements on specific deep learning structures, with specific inputs and usually on a limited CLD fibrosis stage class group, with limited comparison on competitive deep learning schemes fed with different input types. The aim of the present study is to compare some popular deep learning pre-trained networks using temporally stable and full elastograms, with or without augmentation as well as propose suitable deep learning schemes for CLD diagnosis and progress assessment. 200 liver biopsy validated patients with CLD, underwent US SWE examination. Four images from the same liver area were saved to extract elastograms and processed to exclude areas that were temporally unstable. Then, full and temporally stable masked elastograms for each patient were separately fed into GoogLeNet, AlexNet, VGG16, ResNet50 and DenseNet201 with and without augmentation. The networks were tested for differentiation of CLD stages in seven classification schemes over 30 repetitions using liver biopsy as the reference. All networks achieved maximum mean accuracies ranging from 87.2%-97.4% and area under the receiver operating characteristic curves (AUCs) ranging from 0.979-0.990 while the radiologists had AUCs ranging from 0.800-0.870. ResNet50 and DenseNet201 had better average performance than the other networks. The use of the temporal stability mask led to improved performance on about 50% of inputs and network combinations while augmentation led to lower performance for all networks. These findings can provide potential networks with higher accuracy and better setting in the CLD diagnosis and progress assessment. A larger data set would help identify the best network and settings for CLD assessment in clinical practice.

Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization

Background: Hepatocellular carcinoma (HCC) is the most common liver malignancy and the leading cause of death in patients with cirrhosis. Various treatments for HCC are available, including transarterial chemoembolization (TACE), which is the commonest intervention performed in HCC. Radiologic tumor response following TACE is an important prognostic factor for patients with HCC. We hypothesized that, for large HCC tumors, assessment of treatment response made with automated volumetric response evaluation criteria in solid tumors (RECIST) might correlate with the assessment made with the more time- and labor-intensive unidimensional modified RECIST (mRECIST) and manual volumetric RECIST (M-vRECIST) criteria. Accordingly, we undertook this retrospective study to compare automated volumetric RECIST (A-vRECIST) with M-vRECIST and mRESIST for the assessment of large HCC tumors' responses to TACE. Methods:We selected 42 pairs of contrast-enhanced computed tomography (CT) images of large HCCs. Images were taken before and after TACE, and in each of the images, the HCC was segmented using both a manual contouring tool and a convolutional neural network. Three experienced radiologists assessed tumor response to TACE using mRECIST criteria. The intra-class correlation coefficient was used to assess inter-reader reliability in the mRECIST measurements, while the Pearson correlation coefficient was used to assess correlation between the volumetric and mRECIST measurements. Results:Volumetric tumor assessment using automated and manual segmentation tools showed good correlation with mRECIST measurements. For A-vRECIST and M-vRECIST, respectively, r = 0.597 vs. 0.622 in the baseline studies; 0.648 vs. 0.748 in the follow-up studies; and 0.774 vs. 0.766 in the response assessment (P < 0.001 for all). The A-vRECIST evaluation showed high correlation with the M-vRECIST evaluation (r = 0.967, 0.937, and 0.826 in baseline studies, follow-up studies, and response assessment, respectively, P < 0.001 for all). Conclusion:Volumetric RECIST measurements are likely to provide an early marker for TACE monitoring, and automated measurements made with a convolutional neural network may be good substitutes for manual volumetric measurements.

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging

BACKGROUND

Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard.

METHODS

MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain.

RESULTS

Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only.

CONCLUSION

Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.

Imaging-Based Algorithm for the Local Grading of Glioma

BACKGROUND AND PURPOSE

Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models.

MATERIALS AND METHODS

Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic biopsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen κ, precision, and recall.

RESULTS

Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy sites. The random forest method was the best algorithm tested. Tumor grade was predicted at 96% accuracy (κ = 0.93) using 4 inputs (T2, ADC, CBV, and transfer constant from dynamic contrast-enhanced imaging). By means of the conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.79) and 43% of high-grade samples were misclassified as lower-grade disease.

CONCLUSIONS

We found that local pathologic grade can be predicted with a high accuracy using clinical imaging data. Advanced imaging data improved this accuracy, adding value to conventional imaging. Confirmatory imaging trials are justified.

Translating preclinical MRI methods to clinical oncology

The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.

A compressed sensing approach to immobilized nanoparticle localization for superparamagnetic relaxometry

Superparamagnetic relaxometry (SPMR) exploits the unique magnetic properties of targeted superparamagnetic iron oxide nanoparticles (SPIOs) to detect small numbers of cancer cells. Reconstruction of the spatial distribution of cancer-bound nanoparticles requires solving an ill-posed inverse problem. The current method, multiple source analysis (MSA), uses a least-squares fit to determine the strength and location of a pre-determined number of magnetic dipoles. In this proof-of-concept study, we propose the application of a sparsity averaged reweighting algorithm (SARA) for volumetric reconstruction of immobilized nanoparticle distributions. We first calibrate the parameters that define the location of the sensors in the forward model of measurement physics. Using this optimized model, we evaluated the performance of the algorithms on various configurations of single and multiple point-source phantoms. We investigated the effect of the data fidelity parameter, voxel size, and iterative reweighting on the reconstruction produced by SARA. We found that the calibrated physics model can predict the detected field values within 5% of the measured data. When only a single source was present, both algorithms were able to detect as little as 0.5 µg of immobilized particles. However, when two sources were measured simultaneously, MSA failed to detect sources containing as much as 10 µg of particles, while SARA detected all of the sources containing at least 5 µg of particles. We show that a suitable data fidelity parameter can be selected objectively, and the total magnitude and location of a point source reconstructed by SARA is not sensitive to voxel size. Detection and localization of multiple small clusters of nanoparticles is a crucial step in SPMR-based diagnostic applications. Our algorithm overcomes the need to know the number of dipoles before reconstruction and improves the sensitivity of the reconstruction when multiple sources are present.

Hepatocellular carcinoma response to transcatheter arterial chemoembolisation using automatically generated pre-therapeutic tumour volumes by a random forest-based segmentation protocol

AIM

To demonstrate the feasibility of correlating pre-therapeutic volumes and residual liver volume (RLV) with clinical outcomes: time to progression (TTP) and overall survival (OS) in hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolisation (TACE).

MATERIALS AND METHODS

TTP was calculated from a database of 105 patients, receiving first-line treatment with TACE. TTP cut-off for stratifying patients into responders and non-responders was 28 weeks. Pre-treatment tumour and liver volumes were correlated with the TTP and OS following treatment. Univariate cox-regression model was used to assess whether these volumes could predict TTP and/or OS. Kaplan-Meier analysis with log-rank test was used to compare the TTP between high and low volume groups for viable, necrotic, and total tumour. Kaplan-Meier analysis was performed comparing the OS of 10 patients with the longest TTP (mean=122 weeks) in the responder group and 10 patients with the shortest TTP (mean=7 weeks) in the non-responder group.

RESULTS

HCC in high tumour volume groups had a shorter TTP than lesions in low tumour volume groups (p=0.05, p=0.04, p=0.02, for enhancing, non-enhancing, total tumour groups, respectively). A negative (correlation coefficient [CC] 0.3) linear correlation between TTP and tumour volumes, and a positive linear correlation between TTP and residual liver volumes were also demonstrated (CC 0.3). Patients with the longest TTP had a higher OS than with the shortest TTP (p=0.03).

CONCLUSION

This demonstrates the feasibility of predicting treatment response of HCC to TACE using volumetric measurements of pre-treatment lesion and the feasibility of correlating RLV with TACE outcome data in HCC patients.

A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization

PURPOSE

Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE.

MATERIALS AND METHODS

Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control.

RESULTS

The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome.

CONCLUSION

This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.

Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment

PURPOSE

To automatically detect and isolate areas of low and high stiffness temporal stability in shear wave elastography (SWE) image sequences and define their impact in chronic liver disease (CLD) diagnosis improvement by means of clinical examination study and deep learning algorithm employing convolutional neural networks (CNNs).

MATERIALS AND METHODS

Two hundred SWE image sequences from 88 healthy individuals (F0 fibrosis stage) and 112 CLD patients (46 with mild fibrosis (F1), 16 with significant fibrosis (F2), 22 with severe fibrosis (F3), and 28 with cirrhosis (F4)) were analyzed to detect temporal stiffness stability between frames. An inverse Red, Green, Blue (RGB) colormap-to-stiffness process was performed for each image sequence, followed by a wavelet transform and fuzzy c-means clustering algorithm. This resulted in a binary mask depicting areas of high and low stiffness temporal stability. The mask was then applied to the first image of the SWE sequence, and the derived, masked SWE image was used to estimate its impact in standard clinical examination and CNN classification. Regarding the impact of the masked SWE image in clinical examination, one measurement by two radiologists was performed in each SWE image and two in the corresponding masked image measuring areas with high and low stiffness temporal stability. Then, stiffness stability parameters, interobserver variability evaluation and diagnostic performance by means of ROC analysis were assessed. The masked and unmasked sets of SWE images were fed into a CNN scheme for comparison.

RESULTS

The clinical impact evaluation study showed that the masked SWE images decreased the interobserver variability of the radiologists' measurements in the high stiffness temporal stability areas (interclass correlation coefficient (ICC) = 0.92) compared to the corresponding unmasked ones (ICC = 0.76). In terms of diagnostic accuracy, measurements in the high-stability areas of the masked SWE images (area-under-the-curve (AUC) ranging from 0.800 to 0.851) performed similarly to those in the unmasked SWE images (AUC ranging from 0.805 to 0.893). Regarding the measurements in the low stiffness temporal stability areas of the masked SWE images, results for interobserver variability (ICC = 0.63) and diagnostic accuracy (AUC ranging from 0.622 to 0.791) were poor. Regarding the CNN classification, the masked SWE images showed improved accuracy (ranging from 82.5% to 95.5%) compared to the unmasked ones (ranging from 79.5% to 93.2%) for various CLD stage combinations.

CONCLUSION

Our detection algorithm excludes unreliable areas in SWE images, reduces interobserver variability, and augments CNN's accuracy scores for many combinations of fibrosis stages.

Abstract LB-060: Detection of HER2+ tumor cells using MagSenseTM nanoparticles: safety and sensitivity

MagSenseTM is an in-vivo diagnostic for the detection of primary and metastatic disease. The platform consists of tumor targeting superparamagnetic iron oxide nanoparticle (NPs) and a device capable of distinguishing the magnetic signatures of NPs that are free (e.g. flowing through the blood) from those that reach and bind their intended target (e.g., the cancer cell). Our first intended use of this technology is to test the ability of MagSense NPs labeled with an anti-HER2 antibody to detect HER2+ breast cancer in the sentinel and axillary lymph nodes of patients previously confirmed with HER2+ disease. Our preclinical data on the MagSense™-anti-HER2 platform reveal: 1) specific binding and detection of HER2 positive tumor cells in-vitro (5000 cells); 2) specific detection of HER2 positive tumors in-vivo; 3) binding and amplitude of the magnetic signal is proportional to the level of HER2 expression in-vitro and in-vivo; and 4) the nanoconstruct remains stable in circulation. Based on these supportive preclinical data, the MagSense Anti-HER2 NPs are being produced under cGMP along with an R&D version of the MagSense device, for an early stage research clinical trial.

Objective: To support our clinical efforts, initial assessment of NPs safety were conducted. In-vitro efforts focused on the ability of the PEGylated NPs to induce an inflammatory response, activate complement, cause coagulation and platelet aggregation. In-vivo, the safety of the NPs was confirmed by following the degradation of the NPs over time in NGS mice.

Results: MagSense NPs incubated with human whole blood did not cause a release of the pro-inflammatory cytokines IL-1β, IL-8, TNFα and IFNγ nor induce complement activation as measured by iC3b. In-vivo, systemically administered MagSense NPs eventually cleared by the monocyte phagocytic system as confirmed by detecting magnetic signature of the NPs in the liver and spleen 24 hours post injection. Although the signal from the liver remained constant over the next 48 hours, we observed a gradual reduction of the magnetic signal in the liver/spleen and over the next 8 weeks, 97% of the signal was no longer detectable. The mice exhibited no signs of morbidity and did not lose weight.

Conclusion: These data and observations that all mice survived to study termination, support that the MagSense nanoparticles are safe when systemically administered. Future efforts will focus on further optimization for first in human testing.

Citation Format: Farideh Z. Bischoff, Rebeca Romero Aburto, Erika C. Vreeland, John D. Hazle, Konstantin Sokolov, Kayla E. Minser, Todor Karaulanov, Giulio Paciotti. Detection of HER2+ tumor cells using MagSenseTM nanoparticles: safety and sensitivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-060.

Role of Wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance

Hepatocellular carcinoma (HCC) is one of the most common primary hepatic malignancies and one of the fastest-growing causes of cancer-related mortality in the United States. The molecular basis of HCC carcinogenesis has not been clearly identified. Among the molecular signaling pathways implicated in the pathogenesis of HCC, the Wnt/β-catenin signaling pathway is one of the most frequently activated. A great effort is under way to clearly understand the role of this pathway in the pathogenesis of HCC and its role in the transition from chronic liver diseases, including viral hepatitis, to hepatocellular adenomas (HCAs) and HCCs and its targetability in novel therapies. In this article, we review the role of the β-catenin pathway in hepatocarcinogenesis and progression from chronic inflammation to HCC, the novel potential treatments targeting the pathway and its prognostic role in HCC patients, as well as the imaging features of HCC and their association with aberrant activation of the pathway.

Application value of biplane transrectal ultrasonography plus ultrasonic elastosonography and contrast-enhanced ultrasonography in preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer

PURPOSE

To determine the accuracy of biplane transrectal ultrasonography (TRUS) plus ultrasonic elastosonography (UE) and contrast-enhanced ultrasonography (CEUS) in preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer.

MATERIALS AND METHODS

Fifty-three patients with advanced lower rectal cancer were examined before and after neoadjuvant chemoradiotherapy with use of TRUS plus UE and CEUS and were diagnosed as having T stage disease. We compared ultrasonic T stages before and after neoadjuvant chemoradiotherapy and analyzed any changes. Also, with postoperative pathological stages as the gold standard, we compared ultrasonic and pathological T stages and determined their consistency by the kappa statistic.

RESULTS

For patients with rectal cancer, ultrasonic T stages were lower after neoadjuvant chemoradiotherapy than before, with a statistically significant difference (P < 0.05). The posttreatment downstaging rate was 39.6% (21/53). A total of 84.9% received correct staging with use of biplane TRUS plus UE and CEUS in the evaluation of preoperative T staging after neoadjuvant chemoradiotherapy for rectal cancer, which was highly consistent with that of pathological staging (κ = 0.768, P < 0.05). Its sensitivities were 80.0%, 50.0%, 75.0%, 96.3%, and 100% in the diagnoses of stages T0 to T4 rectal cancers, respectively; the specificities were 95.4%, 97.9%, 95.1%, 88.5%, and 100% at stages T0 to T4, respectively.

CONCLUSION

Biplane TRUS plus UE and CEUS can be used to accurately perform preoperative T staging in rectal cancer after neoadjuvant chemoradiotherapy; in addition, this procedure well reflects changes in depth of rectal cancer invasion into the intestinal wall before and after neoadjuvant chemoradiotherapy. It is of great value in clinically evaluating the efficacy of neoadjuvant chemoradiotherapy, in selecting therapeutic regimens, and in avoiding overtreatment.

IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies

Task-evoked and resting-state (rs) functional magnetic resonance imaging (fMRI) techniques have been applied to the clinical management of neurological diseases, exemplified by presurgical localization of eloquent cortex, to assist neurosurgeons in maximizing resection while preserving brain functions. In addition, recent studies have recommended incorporating cerebrovascular reactivity (CVR) imaging into clinical fMRI to evaluate the risk of lesion-induced neurovascular uncoupling (NVU). Although each of these imaging techniques possesses its own advantage for presurgical mapping, a specialized clinical software that integrates the three complementary techniques and promptly outputs the analyzed results to radiology and surgical navigation systems in a clinical format is still lacking. We developed the Integrated fMRI for Clinical Research (IClinfMRI) software to facilitate these needs. Beyond the independent processing of task-fMRI, rs-fMRI, and CVR mapping, IClinfMRI encompasses three unique functions: (1) supporting the interactive rs-fMRI mapping while visualizing task-fMRI results (or results from published meta-analysis) as a guidance map, (2) indicating/visualizing the NVU potential on analyzed fMRI maps, and (3) exporting these advanced mapping results in a Digital Imaging and Communications in Medicine (DICOM) format that are ready to export to a picture archiving and communication system (PACS) and a surgical navigation system. In summary, IClinfMRI has the merits of efficiently translating and integrating state-of-the-art imaging techniques for presurgical functional mapping and clinical fMRI studies.

Seeing SPIOs Directly In Vivo with Magnetic Particle Imaging

Magnetic particle imaging (MPI) is a new molecular imaging technique that directly images superparamagnetic tracers with high image contrast and sensitivity approaching nuclear medicine techniques-but without ionizing radiation. Since its inception, the MPI research field has quickly progressed in imaging theory, hardware, tracer design, and biomedical applications. Here, we describe the history and field of MPI, outline pressing challenges to MPI technology and clinical translation, highlight unique applications in MPI, and describe the role of the WMIS MPI Interest Group in collaboratively advancing MPI as a molecular imaging technique. We invite interested investigators to join the MPI Interest Group and contribute new insights and innovations to the MPI field.

Accuracy of Presurgical Functional MR Imaging for Language Mapping of Brain Tumors: A Systematic Review and Meta-Analysis

Purpose To compare functional magnetic resonance (MR) imaging for language mapping (hereafter, language functional MR imaging) with direct cortical stimulation (DCS) in patients with brain tumors and to assess factors associated with its accuracy. Materials and Methods PubMed/MEDLINE and related databases were searched for research articles published between January 2000 and September 2016. Findings were pooled by using bivariate random-effects and hierarchic summary receiver operating characteristic curve models. Meta-regression and subgroup analyses were performed to evaluate whether publication year, functional MR imaging paradigm, magnetic field strength, statistical threshold, and analysis software affected classification accuracy. Results Ten articles with a total of 214 patients were included in the analysis. On a per-patient basis, the pooled sensitivity and specificity of functional MR imaging was 44% (95% confidence interval [CI]: 14%, 78%) and 80% (95% CI: 54%, 93%), respectively. On a per-tag basis (ie, each DCS stimulation site or "tag" was considered a separate data point across all patients), the pooled sensitivity and specificity were 67% (95% CI: 51%, 80%) and 55% (95% CI: 25%, 82%), respectively. The per-tag analysis showed significantly higher sensitivity for studies with shorter functional MR imaging session times (P = .03) and relaxed statistical threshold (P = .05). Significantly higher specificity was found when expressive language task (P = .02), longer functional MR imaging session times (P < .01), visual presentation of stimuli (P = .04), and stringent statistical threshold (P = .01) were used. Conclusion Results of this study showed moderate accuracy of language functional MR imaging when compared with intraoperative DCS, and the included studies displayed significant methodologic heterogeneity. ^© RSNA, 2017 Online supplemental material is available for this article.

Gaussian process classification of superparamagnetic relaxometry data: Phantom study

MOTIVATION

Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image.

OBJECTIVE

Our goal is to examine the use of data-driven machine learning technique to detect a weak signal induced by a small cluster of SPIONs (surrogate tumor) in presence of background signal and measurement uncertainty. We aim to investigate the performance of both data-driven and image reconstruction models to characterize situations that one can replace the computationally-challenging reconstruction technique by the data-driven model.

METHODS

We utilize Gaussian process (GP) classification model and a physics-based image reconstruction method, tailored to SPMR datasets that are obtained from (i) in silico simulations designed based on mouse cancer models and (ii) phantom experiments using MagSense system (Imagion Biosystems, Inc.). We investigate the performance of the GP classifier against the reconstruction technique, for different levels of measurement noise, different scenarios of SPIONs distribution, and different concentrations of SPIONs at the surrogate tumor.

RESULTS

In our in silico source detection analysis, we were able to achieve high sensitivity results using GP model that outperformed the image reconstruction model for various choices of SPIONs concentration at the surrogate tumor and measurement noise levels. Moreover, in our phantom studies we were able to detect the surrogate tumor phantoms with 5% and 7.3% of the total used SPIONs, surrounded by 9 low-concentration phantoms with accuracies of 87.5% and 96.4%, respectively.

CONCLUSIONS

The GP framework provides acceptable classification accuracies when dealing with in silico and phantom SPMR datasets and can outperform an image reconstruction method for binary classification of SPMR data.

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination.

A methodology for thermal dose model parameter development using perioperative MRI

Post-treatment imaging is the principal method for evaluating thermal lesions following image-guided thermal ablation procedures. While real-time temperature feedback using magnetic resonance temperature imaging (MRTI) is a complementary tool that can be used to optimise lesion size throughout the procedure, a thermal dose model is needed to convert temperature-time histories to estimates of thermal damage. However, existing models rely on empirical parameters derived from laboratory experiments that are not direct indicators of post-treatment radiologic appearance. In this work, we investigate a technique that uses perioperative MR data to find novel thermal dose model parameters that are tailored to the appearance of the thermal lesion on post-treatment contrast-enhanced imaging. Perioperative MR data were analysed for five patients receiving magnetic resonance-guided laser-induced thermal therapy (MRgLITT) for brain metastases. The characteristic enhancing ring was manually segmented on post-treatment T1-weighted imaging and registered into the MRTI geometry. Post-treatment appearance was modelled using a coupled Arrhenius-logistic model and non-linear optimisation techniques were used to find the maximum-likelihood kinetic parameters and dose thresholds that characterise the inner and outer boundary of the enhancing ring. The parameter values and thresholds were consistent with previous investigations, while the average difference between the predicted and segmented boundaries was on the order of one pixel (1 mm). The areas predicted using the optimised model parameters were also within 1 mm of those predicted by clinically utilised dose models. This technique makes clinically acquired data available for investigating new thermal dose model parameters driven by clinically relevant endpoints.

Abstract 564: Binary classification of superparamagnetic relaxometry data for cancer screening

Introduction: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use as a second-line screening modality to improve early cancer detection. During SPMR scanning, targeted superparamagnetic iron oxide nanoparticles (SPIONs) specifically bind to cancer cells and their spatial distribution can be characterized by measurement of the magnetic field relaxation following a brief excitation pulse. Highly sensitive superconducting quantum interference devices (SQUIDs) detect relaxation of clusters of SPIONs bound to small tumors. Challenges inherent to the SPMR technology include measurement noise, as well as the competing influence of SPION uptake by healthy organs (namely the liver), which also contributes to the overall SPMR signal. Hence, manual and stand-alone classification of the SPMR data into positive (i.e., the subject has cancer) or negative (i.e., the subject does not have cancer) screen results can be erroneous.

Methods: We employed a data-driven approach based on Gaussian process (GP) formulation tailored to SPMR datasets to systematically quantify the probability of cancer. In silico, we simulated the SPION uptake process and generated SPMR signals that closely resembled experimental data collected in mouse models of cancer. We investigated the classification accuracy for different amounts of SPION accumulation within the tumor, as well as different levels of measurement noise (coefficient of variation (CV)). In a phantom study, a mouse liver was simulated by clustering together nine cotton swabs containing a total of 150 μg of immobilized SPIONs, while a mouse tumor was simulated by a single cotton swab containing either 9.4 μg or 14.4 μg of immobilized SPIONs. An additional nine cotton swabs containing 32.3 μg of immobilized SPIONs (&lt;5 μg per phantom) were evenly distributed within the scan plane to represent background SPIONs not bound to the tumor or liver. For each of the tumor phantoms, 18 datasets were collected using a magnetic relaxometry device (Senior Scientific LLC) by moving the phantom to 18 different locations. Moreover, 10 datasets were collected without using the tumor phantom to represent the expected signal from healthy mice. In each iteration, the background SPION phantoms were randomly relocated within the scan plane.

Results: Our in silico analysis for tumor accumulations of 3% and 5% of the injected SPION dose achieved 87% and 97% classification accuracies, respectively, when CV=0 and 75% and 93% when CV=0.015. Similarly, in our phantom study, classification accuracies of 87.5% and 96.4%, respectively, were reported for the 9.4 μg and 14.4 μg tumor phantoms.

Conclusion: Using a data-driven GP model, tumor-status classification accuracies of up to 96.4% were achieved in SPMR phantom datasets. In the future, we plan to evaluate the accuracy of our classifier in preclinical settings using animal datasets.

Citation Format: Javad Sovizi, Sara L. Thrower, David Fuentes, Wolfgang Stefan, John D. Hazle, Kelsey Mathieu. Binary classification of superparamagnetic relaxometry data for cancer screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 564. doi:10.1158/1538-7445.AM2017-564

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI

PURPOSE

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm.

METHODS

71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis.

RESULTS

The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%.

CONCLUSIONS

Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures.

Abstract 887: Magnetic relaxometry detection of stealth, antibody-targeted micellar iron oxide nanoparticles in-vivo

Magnetic relaxometry (MRX) has the potential to provide unprecedented sensitivity in early detection of cancer by sensing changes in magnetic relaxation of iron oxide (Fe3O4) nanoparticles targeted to cancer biomarkers and is expected to exceed the detection limits of established clinical modalities. MRX uses superconducting quantum interference device (SQUID) sensors to measure Neél relaxation of bound particles. Our strategy was to develop molecular specific Fe3O4 nanoparticles using amphiphilic functionalized phospholipids that allow for clinical translation of the MRX technology. To accomplish this, we used automated, controlled rate, direct infusion of an organic phase mixture of phospholipids and nanoparticles into water to produce monodisperse micellar nanoparticles with a mean diameter of 75±12 nm and surface charge of -10mV. The particles were determined to be stable in various biological media, including human plasma, for more than 24 hours with no detectable formation of a protein corona. Furthermore, in-vivo studies in healthy mice showed blood circulation times of more than 2 hours, as well as minimal MRX signals during this time. Additionally, we developed maleimide conjugation chemistry for epidermal growth factor receptor (EGFR) antibody attachment to micellar nanoparticles. We have achieved molecular specific labeling of cancer cells over-expressing EGFR. In the future, we will evaluate the MRX signal impact from injecting EGFR-conjugated nanoparticles into tumor-bearing mice.

Citation Format: Rebeca Romero Aburto, Konstantin Sokolov, Adam M. Kulp, Erika C. Vreeland, Zhen Lu, Robert C. Bast, John D. Hazle, Kelsey B. Mathieu. Magnetic relaxometry detection of stealth, antibody-targeted micellar iron oxide nanoparticles in-vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 887. doi:10.1158/1538-7445.AM2017-887

Abstract 888: Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry

Superparamagnetic relaxometry (SPMR) is an emerging technology that uses the unique magnetic properties of superparamagnetic iron oxide nanoparticles (SPIONs) to detect cancer cells. In order to estimate tumor locations from raw MRX data, we developed an L1 reconstruction algorithm under the assumption that early stage disease is sparsely distributed throughout the anatomy. The approach was previously validated in phantom datasets of known signal locations. Advantages of our method are that the solver does not require the user to input prior information regarding the expected number of tumors or their approximate locations. Additionally, the solver reconstructs a volumetric distribution of detected sources within the field of view. To validate the algorithm for use in preclinical settings, SPMR was performed on SKOV3 ovarian tumor bearing mice (n = 3) with the MRX device over time following an intratumoral injection of anti-Her2 antibody-conjugated 25nm SPIONs (Senior Scientific LLC). The SPMR data was reconstructed with our sparse solver and was found to be highly correlated (r = 0.9978) with the results generated by the commercial software that accompanies the MRX instrument (MSA). Additionally, segmentation of the reconstruction revealed a strong signal (2.0·106 pJ/T) in the area of the tumor and almost no signal in areas outside of the tumor (0.077 pJ/T) at four hours after injection. This result was consistent with our prior observations which have revealed that a large fraction of intratumorally-injected nanoparticles remain localized within the tumor for several hours after injection. Furthermore, these results were consistent with SPMR data collected by measuring tissue samples excised 24 hours after injection, of which the tumor had the highest signal. Thus, our sparse reconstruction algorithm was able to return the expected results without prior information regarding the location of nanoparticles. Future work will focus on quantifying the uncertainty in our reconstruction method, as well as characterizing its stability with increasingly complex nanoparticle distributions and detectability limits. In conclusion, this work represents an important advancement of the SPMR technology by allowing for volumetric reconstructions of bound nanoparticles from in vivo data.

Citation Format: Sara L. Thrower, Kelsey Mathieu, Wolfgang Stefan, R. Romero Aburto, Zhen Lu, Robert C. Bast, Javad Sovizi, David Fuentes, John D. Hazle. Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 888. doi:10.1158/1538-7445.AM2017-888

Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images

PURPOSE

Neurosurgical laser ablation is experiencing a renaissance. Computational tools for ablation planning aim to further improve the intervention. Here, global optimisation and inverse problems are demonstrated to train a model that predicts maximum laser ablation extent.

METHODS

A closed-form steady state model is trained on and then subsequently compared to N = 20 retrospective clinical MR thermometry datasets. Dice similarity coefficient (DSC) is calculated to provide a measure of region overlap between the 57 °C isotherms of the thermometry data and the model-predicted ablation regions; 57 °C is a tissue death surrogate at thermal steady state. A global optimisation scheme samples the dominant model parameter sensitivities, blood perfusion (ω) and optical parameter (μ_eff) values, throughout a parameter space totalling 11 440 value-pairs. This represents a lookup table of μ_eff-ω pairs with the corresponding DSC value for each patient dataset. The μ_eff-ω pair with the maximum DSC calibrates the model parameters, maximising predictive value for each patient. Finally, leave-one-out cross-validation with global optimisation information trains the model on the entire clinical dataset, and compares against the model naïvely using literature values for ω and μ_eff.

RESULTS

When using naïve literature values, the model's mean DSC is 0.67 whereas the calibrated model produces 0.82 during cross-validation, an improvement of 0.15 in overlap with the patient data. The 95% confidence interval of the mean difference is 0.083-0.23 (p < 0.001).

CONCLUSIONS

During cross-validation, the calibrated model is superior to the naïve model as measured by DSC, with +22% mean prediction accuracy. Calibration empowers a relatively simple model to become more predictive.

Referenceless magnetic resonance temperature imaging using Gaussian process modeling

PURPOSE

During magnetic resonance (MR)-guided thermal therapies, water proton resonance frequency shift (PRFS)-based MR temperature imaging can quantitatively monitor tissue temperature changes. It is widely known that the PRFS technique is easily perturbed by tissue motion, tissue susceptibility changes, magnetic field drift, and modality-dependent applicator-induced artifacts. Here, a referenceless Gaussian process modeling (GPM)-based estimation of the PRFS is investigated as a methodology to mitigate unwanted background field changes. The GPM offers a complementary trade-off between data fitting and smoothing and allows prior information to be used. The end result being the GPM provides a full probabilistic prediction and an estimate of the uncertainty.

METHODS

GPM was employed to estimate the covariance between the spatial position and MR phase measurements. The mean and variance provided by the statistical model extrapolated background phase values from nonheated neighboring voxels used to train the model. MR phase predictions in the heating ROI are computed using the spatial coordinates as the test input. The method is demonstrated in ex vivo rabbit liver tissue during focused ultrasound heating with manually introduced perturbations (n = 6) and in vivo during laser-induced interstitial thermal therapy to treat the human brain (n = 1) and liver (n = 1).

RESULTS

Temperature maps estimated using the GPM referenceless method demonstrated a RMS error of <0.8°C with artifact-induced reference-based MR thermometry during ex vivo heating using focused ultrasound. Nonheated surrounding areas were <0.5°C from the artifact-free MR measurements. The GPM referenceless MR temperature values and thermally damaged regions were within the 95% confidence interval during in vivo laser ablations.

CONCLUSIONS

A new approach to estimation for referenceless PRFS temperature imaging is introduced that allows for an accurate probabilistic extrapolation of the background phase. The technique demonstrated reliable temperature estimates in the presence of the background phase changes and was demonstrated useful in the in vivo brain and liver ablation scenarios presented.

Performance Assessment for Brain MR Imaging Registration Methods

BACKGROUND AND PURPOSE

Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison methodology for these products.

MATERIALS AND METHODS

Twenty patients were imaged on clinical 3T scanners by using 4 sequences: T2-weighted, FLAIR, susceptibility-weighted angiography, and T1 postcontrast. Fiducial landmark sites (n = 1175) were specified throughout these image volumes to define identical anatomic locations across sequences. Multiple registration algorithms were applied by using the T2 sequence as a fixed reference. Euclidean error was calculated before and after each registration and compared with a criterion standard landmark registration. The Euclidean effectiveness ratio is the fraction of Euclidean error remaining after registration, and the statistical effectiveness ratio is similar, but accounts for dispersion and noise.

RESULTS

Before registration, error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 2.07 ± 0.55 mm, 2.63 ± 0.62 mm, and 3.65 ± 2.00 mm, respectively. Postregistration, the best error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 1.55 ± 0.46 mm, 1.34 ± 0.23 mm, and 1.06 ± 0.16 mm, with Euclidean effectiveness ratio values of 0.493, 0.181, and 0.096 and statistical effectiveness ratio values of 0.573, 0.352, and 0.929 for rigid mutual information, affine mutual information, and a commercial GE registration, respectively.

CONCLUSIONS

We demonstrate a method for comparing the performance of registration algorithms and suggest the Euclidean error, Euclidean effectiveness ratio, and statistical effectiveness ratio as performance metrics for clinical registration algorithms. These figures of merit allow registration algorithms to be rationally compared.

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys

PURPOSE

Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition.

METHODS

A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers.

RESULTS

The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm.

CONCLUSION

Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.

Quantitative CT imaging features for hepatocellular carcinoma (HCC) with b-catenin (CTNNB1) gene mutation

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Background: To determine whether CT imaging features can provide quantitative biomarkers to differentiate HCC with pathologic B-catenin gene mutation and those without mutation. Methods: Quantitative imaging features were extracted from a database of manually labeled liver with enhancing and non-enhancing tumor tissue,which were established using multiphasic CT images from 17 patients. CT studies were done before each patient underwent surgical removal of the HCC, which were subjected to pathologic analysis to evaluate B-catenin mutation.The mean period between the CT studies and the pathologic analyses was 18 days. According to the pathology results, the patients were divided into two groups: HCC with CTNNB1 mutation and HCC without. Image feature extraction included image gradients, co-occurrence matrix, and pixel neighborhood statistics of the first, second, and third moments. Pairwise analyses of the imaging features were performed on the mutated and non-mutated HCC images and the background liver tissue of both groups. Independent samples t-test and Mann Whitney U test were performed to quantitatively compare between the means of the imaging features extracted from the tumor tissues of both groups and those extracted from the background liver tissue of both groups. Results: Imaging feature analysis of the pairwise difference between the mutated and non-mutated HCC scans for multiple pixel-neighborhood image features are statistically significant.The top stratifying image features include the skewness (p = 0.02), energy (p = .03), and entropy (p = .03) during the venous and arterial phase. Conclusions: This preliminary study demonstrates the feasibility of quantitative imaging feature extraction from CE-CT imaging to differentiate between HCC with proven B-catenin gene mutation and those without mutation. Non-invasive methods of identifying HCC with B-catenin mutations may be clinically beneficial since B-catenin is an important potential target in novel cancer therapies, and identifying B-catenin mutations may also help provide information regarding prognosis.Verifying the quantitative features in larger patient populations is needed to confirm the results of this study.

Accelerated magnetic resonance thermometry in the presence of uncertainties

A model-based information theoretic approach is presented to perform the task of magnetic resonance (MR) thermal image reconstruction from a limited number of observed samples on k-space. The key idea of the proposed approach is to optimally detect samples of k-space that are information-rich with respect to a model of the thermal data acquisition. These highly informative k-space samples can then be used to refine the mathematical model and efficiently reconstruct the image. The information theoretic reconstruction was demonstrated retrospectively in data acquired during MR-guided laser induced thermal therapy (MRgLITT) procedures. The approach demonstrates that locations with high-information content with respect to a model-based reconstruction of MR thermometry may be quantitatively identified. These information-rich k-space locations are demonstrated to be useful as a guide for k-space undersampling techniques. The effect of interactively increasing the predicted number of data points used in the subsampled model-based reconstruction was quantified using the L2-norm of the distance between the subsampled and fully sampled reconstruction. Performance of the proposed approach was also compared with uniform rectilinear subsampling and variable-density Poisson disk subsampling techniques. The proposed subsampling scheme resulted in accurate reconstructions using a small fraction of k-space points, suggesting that the reconstruction technique may be useful in improving the efficiency of thermometry data temporal resolution.

Correction of ischaemic brain acidosis with SQ29,548/1-benzylimidazole

Thromboxane A2 (TXA2) is a proaggregatory vasoconstrictor that is synthesized and released during reperfusion of ischaemic brain. We administered a TXA2 receptor antagonist, SQ29,548, and a thromboxane A synthase inhibitor, 1-benzylimidazole (1-BI), to rats subjected to 30 min of reversible forebrain ischaemia. Cerebral thromboxane B2 (TXB2), the stable metabolite of TXA2, measured after 60 min of reperfusion was 0.37 +/- 0.08 ng/mg brain protein in animals treated with SQ29,548/1-BI compared with 1.20 +/- 0.16 in ischaemic controls (p < 0.05). Cerebral pH determined by 31P magnetic resonance spectroscopy was higher in treated animals, 7.06 +/- 0.04, than in ischaemic controls, 6.5 +/- 0.01, after 20 min of reperfusion (p < or = 0.01). The significant elevation of cerebral pH in treated animals persisted at 30 (7.17 +/- 0.05 vs. 6.5 +/- 0.01; p < or = 0.01), 35 (7.17 +/- 0.05 vs. 6.44 +/- 0.04; p < or = 0.01), and 40 min of reperfusion (7.06 +/- 0.06 vs. 6.37 +/- 0.01; p < or = 0.05). We conclude that SQ29,548/1-BI reduces thromboxane levels and promotes resolution of tissue acidosis in ischaemic brain. The combination of a TXA2 receptor antagonist with a thromboxane A synthase inhibitor deserves further study as a potential treatment for acute cerebral infarction.

Technical Note: A Monte Carlo study of magnetic-field-induced radiation dose effects in mice

PURPOSE

Magnetic fields are known to alter radiation dose deposition. Before patients receive treatment using an MRI-linear accelerator (MRI-Linac), preclinical studies are needed to understand the biological consequences of magnetic-field-induced dose effects. In the present study, the authors sought to identify a beam energy and magnetic field strength combination suitable for preclinical murine experiments.

METHODS

Magnetic field dose effects were simulated in a mouse lung phantom using various beam energies (225 kVp, 350 kVp, 662 keV [Cs-137], 2 MV, and 1.25 MeV [Co-60]) and magnetic field strengths (0.75, 1.5, and 3 T). The resulting dose distributions were compared with those in a simulated human lung phantom irradiated with a 6 or 8 MV beam and orthogonal 1.5 T magnetic field.

RESULTS

In the human lung phantom, the authors observed a dose increase of 45% and 54% at the soft-tissue-to-lung interface and a dose decrease of 41% and 48% at the lung-to-soft-tissue interface for the 6 and 8 MV beams, respectively. In the mouse simulations, the magnetic fields had no measurable effect on the 225 or 350 kVp dose distribution. The dose increases with the Cs-137 beam for the 0.75, 1.5, and 3 T magnetic fields were 9%, 29%, and 42%, respectively. The dose decreases were 9%, 21%, and 37%. For the 2 MV beam, the dose increases were 16%, 33%, and 31% and the dose decreases were 9%, 19%, and 30%. For the Co-60 beam, the dose increases were 19%, 54%, and 44%, and the dose decreases were 19%, 42%, and 40%.

CONCLUSIONS

The magnetic field dose effects in the mouse phantom using a Cs-137, 3 T combination or a Co-60, 1.5 or 3 T combination most closely resemble those in simulated human treatments with a 6 MV, 1.5 T MRI-Linac. The effects with a Co-60, 1.5 T combination most closely resemble those in simulated human treatments with an 8 MV, 1.5 T MRI-Linac.

A flexible fast spin echo triple-echo Dixon technique

PURPOSE

To develop a flexible fast spin echo (FSE) triple-echo Dixon (FTED) technique.

METHODS

An FSE pulse sequence was modified by replacing each readout gradient with three fast-switching bipolar readout gradients with minimal interecho dead time. The corresponding three echoes were used to generate three raw images with relative phase shifts of -θ, 0, and θ between water and fat signals. A region growing-based two-point Dixon phase correction algorithm was used to joint process two separate pairs of the three raw images, yielding a final set of water-only and fat-only images. The flexible FTED technique was implemented on 1.5T and 3.0T scanners and evaluated in five subjects for fat-suppressed T2-weighted imaging and in one subject for post-contrast fat-suppressed T1-weighted imaging.

RESULTS

The flexible FTED technique achieved a high data acquisition efficiency, comparable to that of FSE, and was flexible in scan protocols. The joint two-point Dixon phase correction algorithm helped to ensure consistency in the processing of the two separate pairs of raw images. Reliable and uniform separation of water and fat was achieved in all of the test cases.

CONCLUSION

The flexible FTED technique incorporates the benefits of both FSE and Dixon imaging and provided more flexibility than the original FTED in applications such as fat-suppressed T2-weighted and T1-weighted imaging. Magn Reson Med 77:1049-1057, 2017. © 2016 International Society for Magnetic Resonance in Medicine.