FDG-PET reproducibility in tumor-bearing mice: comparing a traditional SUV approach with a tumor-to-brain tissue ratio approach

Current Applications of PET/MR: Part I: Technical Basics and Preclinical/Clinical Applications

Positron emission tomography/magnetic resonance (PET/MR) imaging has gone through major hardware improvements in recent years, making it a reliable state-of-the-art hybrid modality in clinical practice. At the same time, image reconstruction, attenuation correction, and motion correction algorithms have significantly evolved to provide high-quality images. Part I of the current review discusses technical basics, pre-clinical applications, and clinical applications of PET/MR in radiation oncology and head and neck imaging. PET/MR offers a broad range of advantages in preclinical and clinical imaging. In the preclinic, small and large animal-dedicated devices were developed, making PET/MR capable of delivering new insight into animal models in diseases and facilitating the development of methods that inform clinical PET/MR. Regarding PET/MR's clinical applications in radiation medicine, PET and MR already play crucial roles in the radiotherapy process. Their combination is particularly significant as it can provide molecular and morphological characteristics that are not achievable with other modalities. In addition, the integration of PET/MR information for therapy planning with linear accelerators is expected to provide potentially unique biomarkers for treatment guidance. Furthermore, in clinical applications in the head and neck region, it has been shown that PET/MR can be an accurate modality in head and neck malignancies for staging and resectability assessment. Also, it can play a crucial role in diagnosing residual or recurrent diseases, reliably distinguishing from oedema and fibrosis. PET/MR can furthermore help with tumour characterization and patient prognostication. Lastly, in head and neck carcinoma of unknown origin, PET/MR, with its diagnostic potential, may obviate multiple imaging sessions in the near future.

Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis

Purpose: Pre-operative assessment of thoracic lymphonodal (LN) involvement in patients with lung cancer (LC) is crucial when choosing the treatment modality. Visual assessment of F-18-FDG-PET/CT (PET/CT) is well established, however, there is still a need for prospective quantitative data to differentiate benign from malignant lesions which would simplify staging and guide the further implementation of computer-aided diagnosis (CAD). Methods: In this prospective study, 37 patients with confirmed lung cancer (m/f = 24/13; age: 70 [52–83] years) were analyzed. All patients underwent PET/CT and quantitative data (standardized uptake values) were obtained. Histological results were available for 101 thoracic lymph nodes. Quantitative data were matched to determine cut-off values for delineation between benign vs. malignant lymph nodes. Furthermore, a scoring system derived from these cut-off values was established. Statistical analyses were performed through ROC analysis. Results: Quantitative analysis revealed the optimal cut-off values (p < 0.01) for the differentiation between benign and malignant thoracic lymph nodes in patients suffering from lung cancer. The respective areas under the curve (AUC) ranged from 0.86 to 0.94. The highest AUC for a ratio of lymph node to healthy lung tissue was 0.94. The resulting accuracy ranged from 78.2% to 89.1%. A dedicated scoring system led to an AUC of 0.93 with a negative predictive value of 95.4%. Conclusion: Quantitative analysis of F-18-FDG-PET/CT data provides reliable results for delineation between benign and malignant thoracic lymph nodes. Thus, quantitative parameters can improve diagnostic accuracy and reliability and can also facilitate the handling of the steadily increasing number of clinical examinations.

Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization

Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMR_glc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization.

Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized.

To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses.

Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals.

Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.

A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans

PURPOSE

The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans.

METHODS

Three different three-dimensional (3D) convolutional neural network architectures (U-Net, V-Net, and modified U-Net) were implemented and compared regarding their performance in 3D cerebellum segmentation in FDG PET scans. For network training and testing, 134 PET scans with corresponding manual volumetric segmentations were utilized. For segmentation performance assessment, a fivefold cross-validation was used, and the Dice coefficient as well as signed and unsigned distance errors were calculated. In addition, standardized uptake value (SUV) uptake measurement performance was assessed by means of a statistical comparison to an independent reference standard. Furthermore, a comparison to a previously reported active-shape-model-based approach was performed.

RESULTS

Out of the three convolutional neural networks investigated, the modified U-Net showed significantly better segmentation performance. It achieved a Dice coefficient of 0.911 ± 0.026, a signed distance error of 0.220 ± 0.103 mm, and an unsigned distance error of 1.048 ± 0.340 mm. When compared to the independent reference standard, SUV uptake measurements produced with the modified U-Net showed no significant error in slope and intercept. The estimated reduction in total SUV measurement error was 95.1%.

CONCLUSIONS

The presented work demonstrates the potential of deep convolutional neural networks in automated SUV measurement of reference regions. While it focuses on the cerebellum, utilized methods can be generalized to other reference regions like the liver or aortic arch. Future work will focus on combining lesion and reference region analysis into one approach.

PET/MRI: a frontier in era of complementary hybrid imaging

With primitive approaches, the diagnosis and therapy were operated at the cellular, molecular, or even at the genetic level. As the diagnostic techniques are more concentrated towards molecular level, multi modal imaging becomes specifically essential. Multi-modal imaging has extensive applications in clinical as well as in pre-clinical studies. Positron Emission Tomography (PET) has flourished in the field of nuclear medicine, which has motivated it to fuse with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for PET/CT and PET/MRI respectively. However, the challenges in PET/CT are due to the inability of simultaneous acquisition and reduced soft tissue contrast, which has led to the development of PET/MRI. Also, MRI offers the better soft tissue contrast over CT. Hence, fusion of PET and MRI results in combining structural information with functional image from PET. Yet, it has many technical challenges due to the interference between the modalities. Also, it must be resolved with various approaches for addressing the shortcomings of each system and improvise on the image quantification system. This review elaborates on the various challenges in the present PET/MRI system and the future directions of the hybrid modality. Also, the different data acquisition and analysis techniques of PET/MRI system are discussed with enhanced details on the software tools.

The potential of hyperpolarized 13C magnetic resonance spectroscopy to monitor the effect of combretastatin based vascular disrupting agents

BACKGROUND

Targeting tumor vasculature with vascular disrupting agents (VDAs) results in substantial cell death that precede tumor shrinkage. Here, we investigate the potential of hyperpolarized magnetic resonance spectroscopy (HPMRS) to monitor early metabolic changes associated with VDA treatment.

METHODS

Mice bearing C3H mammary carcinomas were treated with the VDAs combretastatin-A4-phosphate (CA4P) or the analog OXi4503, and HPMRS was performed following [1-¹³C]pyruvate administration. Similarly, treated mice were positron emission tomography (PET) scanned following administration of the glucose analog FDG. Finally, metabolic imaging parameters were compared to tumor regrowth delay and measures of vascular damage, derived from dynamic contrast-agent enhanced magnetic resonance imaging (DCE-MRI) and histology.

RESULTS

VDA-treatment impaired tumor perfusion (histology and DCE-MRI), reduced FDG uptake, increased necrosis, and slowed tumor growth. HPMRS, revealed that the [1-¹³C]pyruvate-to-[1-¹³C]lactate conversion remained unaltered, whereas [1-¹³C]lactate-to-[¹³C]bicarbonate (originating from respiratory CO₂) ratios increased significantly following treatment.

CONCLUSIONS

DCE-MRI and FDG-PET revealed loss of vessel functionality, impaired glucose delivery and reduced metabolic activity prior to cell death. [1-¹³C]lactate-to-[¹³C]bicarbonate ratios increased significantly during treatment, indicating a decline in respiratory activity driven by the onset of hypoxia. HPMRS is promising for early detection of metabolic stress inflicted by VDAs, which cannot easily be inferred based on blood flow measurements.

Results from 11C-metformin-PET scans, tissue analysis and cellular drug-sensitivity assays questions the view that biguanides affects tumor respiration directly

The anti-diabetic biguanide drugs metformin (METF) and phenformin (PHEN) may have anti-cancer effects. Biguanides suppress plasma growth factors, but nonetheless, the view that these mitochondrial inhibitors accumulate in tumor tissue to an extent that leads to severe energetic stress or alleviation of hypoxia-induced radioresistance is gaining ground. Our cell studies confirm that biguanides inhibits cell proliferation by targeting respiration, but only at highly suprapharmacological concentrations due to low drug retention. Biodistribution/PET studies of ¹¹C-labeled metformin (¹¹C-METF) revealed that plasma bioavailability remained well below concentrations with metabolic/anti-proliferative in vitro effects, following a high oral dose. Intraperitoneal administration resulted in higher drug concentrations, which affected metabolism in normal organs with high METF uptake (e.g., kidneys), but tumor drug retention peaked at low levels comparable to plasma levels and hypoxia was unaffected. Prolonged intraperitoneal treatment reduced tumor growth in two tumor models, however, the response did not reflect in vitro drug sensitivity, and tumor metabolism and hypoxia was unaffected. Our results do not support that direct inhibition of tumor cell respiration is responsible for reduced tumor growth, but future studies using ¹¹C-METF-PET are warranted, preferably in neoplasia’s originating from tissue with high drug transport capacity, to investigate the controversial idea of direct targeting.