High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon's liver dataset, and the BTCV multi-organ segmentation dataset.

Nickel-Catalyzed 1,2-Arylboration of Unactivated Alkenes to Access Boryl-Functionalized Aliphatic Amines.

We report herein a three-component 1,2-arylboration of alkenyl amines bearing a cleavable picolinamide directing group. With aryl halides as electrophiles and B2Pin2 as nucleophiles, a wide range of alkenes could be converted into valuable boryl-functionalized aliphatic amines. The reaction proceeds with high levels of chemo- and regiocontrol and exhibits high functional group tolerance. In addition, the pinacol boronic ester group could undergo various transformations, indicating that the protocol could potentially provide a platform for versatile regioselective difunctionalization of alkenyl amines.

Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography.

Background: Projection tomography (PT) is a very important and valuable method for fast volumetric imaging with isotropic spatial resolution. Sparse-view or limited-angle reconstruction-based PT can greatly reduce data acquisition time, lower radiation doses, and simplify sample fixation modes. However, few techniques can currently achieve image reconstruction based on few-view projection data, which is especially important for in vivo PT in living organisms. Methods: A 2-stage deep learning network (TSDLN)-based framework was proposed for parallel-beam PT reconstructions using few-view projections. The framework is composed of a reconstruction network (R-net) and a correction network (C-net). The R-net is a generative adversarial network (GAN) used to complete image information with direct back-projection (BP) of a sparse signal, bringing the reconstructed image close to reconstruction results obtained from fully projected data. The C-net is a U-net array that denoises the compensation result to obtain a high-quality reconstructed image. Results: The accuracy and feasibility of the proposed TSDLN-based framework in few-view projection-based reconstruction were first evaluated with simulations, using images from the DeepLesion public dataset. The framework exhibited better reconstruction performance than traditional analytic reconstruction algorithms and iterative algorithms, especially in cases using sparse-view projection images. For example, with as few as two projections, the TSDLN-based framework reconstructed high-quality images very close to the original image, with structural similarities greater than 0.8. By using previously acquired optical PT (OPT) data in the TSDLN-based framework trained on computed tomography (CT) data, we further exemplified the migration capabilities of the TSDLN-based framework. The results showed that when the number of projections was reduced to 5, the contours and distribution information of the samples in question could still be seen in the reconstructed images. Conclusions: The simulations and experimental results showed that the TSDLN-based framework has strong reconstruction abilities using few-view projection images, and has great potential in the application of in vivo PT.

Classification of unlabeled cells using lensless digital holographic images and deep neural networks.

BACKGROUND: Image-based cell analytic methodologies offer a relatively simple and economical way to analyze and understand cell heterogeneities and developments. Owing to developments in high-resolution image sensors and high-performance computation processors, the emerging lensless digital holography technique enables a simple and cost-effective approach to obtain label-free cell images with a large field of view and microscopic spatial resolution. METHODS: The holograms of three types of cells, including MCF-10A, EC-109, and MDA-MB-231 cells, were recorded using a lensless digital holography system composed of a laser diode, a sample stage, an image sensor, and a laptop computer. The amplitude images were reconstructed using the angular spectrum method, and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. Four convolutional neural networks (CNNs) were used to classify the cell types based on the recovered holographic images. RESULTS: Classification of two cell types and three cell types achieved an accuracy of higher than 91% by all the networks used. The ResNet and the DenseNet models had similar classification accuracy of 95% or greater, outperforming the GoogLeNet and the CNN-5 models. CONCLUSIONS: These experiments demonstrated that the CNNs were effective at classifying two or three types of tumor cells. The lensless holography combined with machine learning holds great promise in the application of stainless cell imaging and classification, such as in cancer diagnosis and cancer biology research, where distinguishing normal cells from cancer cells and recognizing different cancer cell types will be greatly beneficial.

Three-dimensional quantitative assessment of myocardial infarction via multimodality fusion imaging: methodology, validation, and preliminary clinical application.

BACKGROUND: The precise assessment of myocardial infarction (MI) is crucial both for therapeutic interventions in old MI and the development of new and effective techniques to repair injured myocardium. A novel method was developed to assess left ventricular (LV) quantitatively infarction through three-dimensional (3D) multimodality fusion based on computed tomography angiography (CTA) and technetium-99m methoxyisobutylisonitrile (99mTc-MIBI) single-photon emission computed tomography (SPECT) images. This study sought to develop a 3D quantitative method for MI for pre-clinical study and clinical application. METHODS: Three months after the MI models were established in 20 minipigs, CTA and SPECT images were acquired separately, which were then aligned automatically with the constraints of the shape and the whole heart and LV myocardium position. Infarct ratios were quantified based on the 3D fusion images. The quantitative assessment was then experimentally validated via an ex vivo histology analysis using triphenyl-tetrazolium-chloride staining and subsequently applied to post-MI patients (n=8). RESULTS: The location of an infarct identified by the SPECT was consistent with that identified by an ex vivo heart in a 3D space. Infarct size determined by CTA-SPECT was correlated with infarct size assessed by triphenyl-tetrazolium-chloride pathology {27.6% [interquartile range (IQR) 17.1-34.7%] vs. 24.1% (IQR 14.7-32.5%), r2=0.99, P<0.01}. In clinical cases, the CTA-SPECT 3D fusion quantitative results were significantly correlated with the quantitative perfusion SPECT results (r=0.976, P<0.01). CONCLUSIONS: The proposed 3D fusion quantitative assessment method provides reliable and intuitive evaluations of infarction. This novel quantification technique enables whole heart quantification for the pre-operation evaluation and post-diagnosis management of old MI patients. It could also be applied to the design of 3D-printed cardiac patches.

The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging.

BACKGROUND: Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region. RESULTS: The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The "soft boundary" defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm. CONCLUSION: The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

Kinetic modeling and analysis of dynamic bioluminescence imaging of substrates administered by intraperitoneal injection.

BACKGROUND: Bioluminescence imaging (BLI) has been found to have diverse applications in the life sciences and medical research due to its ease of use and high sensitivity. From kinetics analysis, dynamic imaging studies have significant advantages for diagnosis when compared to traditional static imaging studies. This work focuses on modeling and quantitatively analyzing the dynamic data produced from the intraperitoneal (IP) injection of D-luciferin in longitudinal BLI, aiming to provide a powerful tool for monitoring the growth of tumors. METHODS: We constructed a three-compartment pharmacokinetic (PK) model and employed the standard Michaelis-Menten (M-M) kinetics to investigate the dynamic BLI data produced from the IP injection of D-luciferin. The 3 compartments were the plasma compartment, the non-specific compartment, and the specific compartment. The validity of this PK model was tested by the dynamic BLI data of MKN28M-luc xenograft mice, along with the published longitudinal dynamic BLI data of B16F10-luc xenograft mice. RESULTS: The R-squares between the simulated lines and the measurement were 1 and 0.99, respectively, for the mice data and the published data. In addition, the 2 kinetic macroparameters obtained reflected the rate of tumor growth in vivo. In particular, the values of macroparameters A showed a significant dependence on tumor surface area. CONCLUSIONS: The proposed PK model may be an effective tool for use in drug development programs and for monitoring the response of tumors to treatment.

A novel natural product, britanin, inhibits tumor growth of pancreatic cancer by suppressing nuclear factor-κB activation.

Pancreatic cancer has a high mortality rate and poor prognosis. The development of novel medicines for pancreatic cancer therapy is urgently need. Britanin is a bioactive sesquiterpene lactone, that exhibits excellent anti-inflammatory and antioxidant effects. However, the potential anti-tumour activity of britanin is also considerable. Hence, in this study, the in vitro and in vivo anti-pancreatic cancer effects of britanin were investigated. Several pancreatic cancer cell lines were applied to evaluate inhibition of proliferation, migration and NF-κB pathway in vitro. Then in vivo toxicity of britanin was evaluated in BALB/c mice. The in vivo inhibitory effects of britanin were investigated by bioluminescence imaging, traditional methods and histological analysis in a pancreatic cancer xenograft mouse model. The results showed that britanin exhibited effective anti-tumour actions both in vitro and in vivo. The IC50 values in PANC-1, BxPC-3 and MIA CaPa-2 cell lines were 1.348, 3.367 and 3.104 µmol/L, respectively, and cell proliferation and migration were significantly inhibited by britanin treatment. Western blotting demonstrated that NF-κB family proteins, such as P50, P65, and P-P65 were affected by britanin treatment. It is worth noting that the P-P65 protein, which regulates the expression of multiple factors downstream, was significantly decreased in britanin treated group. In vivo experiments verified that britanin could suppress the tumour progression in a pancreatic cancer xenograft mouse model, while the compound did not exhibit intolerable toxicity. In conclusion, britanin has remarkable potential treatment effects against pancreatic cancer, and it could be developed as a new agent for pancreatic cancer chemotherapy.

Removal of random-valued impulse noise from Cerenkov luminescence images.

Cerenkov luminescence imaging(CLI) is an emerging molecular imaging technology able to optically visualize radioactive decay signals from medical isotopes and has found wide application in tumor diagnose, cancer therapy, drug development, intraoperative guidance, and so on. When Cerenkov luminescence data are collected, the high-energy particles from the radioactive nucleus will be detected by the sensitive CCD camera and lead to impulse noise. To suppress the impulse noise and improve the contrast of the useful signal to the background, the detection-based fuzzy switching median filtering framework is proposed in this paper. Several experiments were conducted respectively to investigate the statistical feature of the noise and to evaluate the performance of the proposed noise removal framework. The results show that the signal-to-noise ratio is improved after noise elimination. The proposed filtering framework outperforms the classical median filter in terms of root mean squared error and the structural similarity index. It also preserves the maximum value and the mean value in the regions of interest better than the median filter does. In addition, compared with the FLICMCDD algorithm, the proposed method works much faster while getting similar results. Graphical abstract.

A photo-triggered conjugation approach for attaching RGD ligands to biodegradable mesoporous silica nanoparticles for the tumor fluorescent imaging.

Fluorescent probes conjugated to peptide or antibody directing groups, which exhibit high signal to background ratios, have been widely used to image tumors and monitor their growth. A photo-triggered cycloaddition reaction between the arginine-glycine-aspartic acid -N-ɛ-acryllysine (RGD-Acrk) peptides and the tetrazole compounds bound to the surface of biodegradable mesoporous silica nanoparticles (bMSN) has been used to construct a fluorescent nanoprobe (bMSN@T2-RGD-Acrk), which showed fluorescent emission at 550 nm and could selectively image the 4T1 cells and breast cancer. This means that the bMSN@T2-RGD-Acrk nanoprobe made by photo-triggered conjugation approach is a promising fluorescent imaging agent for visualizing tumors. Thus, the photo-triggered one-spot reaction can give a stable crosslinker in a biocompatible manner for bioconjugation with nanoparticles and produce a fluorescent group that is suitable for imaging in vivo.

Intravenous Administration-Oriented Pharmacokinetic Model for Dynamic Bioluminescence Imaging.

OBJECTIVE: In vivo bioluminescence imaging (BLI) is a promising tool for monitoring the growth and metastasis of tumors. However, quantitative BLI research based on intravenous (IV) injection is limited, which greatly restricts its further application. To address this problem, we designed a pharmacokinetic (PK) model which is suitable for applying on IV administration of small amounts of D-Luciferin. METHODS: After three weeks of postimplantation, mkn28-luc xenografted mice were subjected to 40-min dynamic BLI immediately following D-Luciferin intravenous injection on days 1, 3, 5, 7, and 9. Furthermore, the PK model was applied on dynamic BLI data to obtain the sum of kinetic rate constants (SKRC). RESULTS: Results showed that the SKRC values decreased rapidly with the growth of the tumor. There was a statistical difference between the SKRC values measured at different time points, while the time point of luminous intensity peak was unaffected by the growth of the tumor. CONCLUSION: In short, our results imply that dynamic BLI combined with our PK model can predict tumor growth earlier and with higher sensitivity compared to the conventional method, which is crucial for improving drug evaluation efficacy. In addition, the dynamic BLI may provide a valuable reference for the noninvasive acquiring arterial input function, which may also provide a new application prospect for hybrid PET-optical imaging.

Hypoxia-activated prodrugs and redox-responsive nanocarriers.

Hypoxia is one of the marked features of malignant tumors, which is associated with several adaptation changes in the microenvironment of tumor cells. Therefore, targeting tumor hypoxia is a research hotspot for cancer therapy. In this review, we summarize the developing chemotherapeutic drugs for targeting hypoxia, including quinones, nitroaromatic/nitroimidazole, N-oxides, and transition metal complexes. In addition, redox-responsive bonds, such as nitroimidazole groups, azogroups, and disulfide bonds, are frequently used in drug delivery systems for targeting the redox environment of tumors. Both hypoxia-activated prodrugs and redox-responsive drug delivery nanocarriers have significant effects on targeting tumor hypoxia for cancer therapy. Hypoxia-activated prodrugs are commonly used in clinical trials with favorable prospects, while redox-responsive nanocarriers are currently at the experimental stage.

Intrinsically Zirconium-89-Labeled Manganese Oxide Nanoparticles for In Vivo Dual-Modality Positron Emission Tomography and Magnetic Resonance Imaging.

Manganese-based nanoparticles (NPs) have recently attracted much attention in the field of biomedical imaging due to their impressive enhanced T1 contrast ability. Although the reported manganese-based NPs have exhibited good imaging capabilities as contrast agents, it is still urgent to develop novel multifunctional manganese-based imaging probes for future biomedical imaging, especially PET/MRI probes. Herein, we present chelator-free zirconium-89 (89Zr, t1/2: 78.4 h) labeling of manganese oxide NPs (Mn3O4@PEG) with ∼78% labeling yield and good stability. Serial positron emission tomography (PET) and magnetic resonance imaging (MRI) studies non-invasively assessed the biodistribution patterns of the NPs and the feasibility of in vivo dual-modality imaging and lymph-node mapping. Since Mn3O4 NPs exhibited desirable properties for enhanced T1 imaging and the simplicity of chelator-free radiolabeling, [89Zr]Mn3O4@PEG NPs offer a novel, simple, safe and accurate nanoplatforms for future precise cancer imaging and diagnosis.

Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography.

The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT.

In vivo near infrared fluorescence imaging and dynamic quantification of pancreatic metastatic tumors using folic acid conjugated biodegradable mesoporous silica nanoparticles.

Cancer metastasis is one of the biggest challenges in cancer treatments since it increases the likelihood that a patient will die from the disease. Therefore, the availability of techniques for the early detection and quantification of tumors is very important. We have prepared cyanine 7.5 NHS ester (Cy7.5) and folic acid (FA) conjugated biodegradable mesoporous silica nanoparticles (bMSN@Cy7.5-FA NPs) (~100 nm) for visualizing tumors in vivo. The fluorescence spectra revealed that the emission peak of bMSN@Cy7.5-FA NPs had a red-shift of 1 nm. Confocal immunofluorescent images showed that bMSN@Cy7.5-FA NPs had an excellent targeting ability for visualizing cancer cells. In vivo fluorescence imaging has been conducted using an orthotopic model for pancreatic cancer within 48 h, and the fluorescence intensity reached a maximum at a post injection time-point of 12 h, which demonstrated that the use of bMSN@Cy7.5-FA NPs provides an excellent imaging platform for tumor precision therapy in mice.

In Vivo Dual-Modality Fluorescence and Magnetic Resonance Imaging-Guided Lymph Node Mapping with Good Biocompatibility Manganese Oxide Nanoparticles.

Multifunctional manganese oxide nanoparticles (NPs) with impressive enhanced T1 contrast ability show great promise in biomedical diagnosis. Herein, we developed a dual-modality imaging agent system based on polyethylene glycol (PEG)-coated manganese oxide NPs conjugated with organic dye (Cy7.5), which functions as a fluorescence imaging (FI) agent as well as a magnetic resonance imaging (MRI) imaging agent. The formed Mn3O4@PEG-Cy7.5 NPs with the size of ~10 nm exhibit good colloidal stability in different physiological media. Serial FI and MRI studies that non-invasively assessed the bio-distribution pattern and the feasibility for in vivo dual-modality imaging-guided lymph node mapping have been investigated. In addition, histological and biochemical analyses exhibited low toxicity even at a dose of 20 mg/kg in vivo. Since Mn3O4@PEG-Cy7.5 NPs exhibited desirable properties as imaging agents and good biocompatibility, this work offers a robust, safe, and accurate diagnostic platform based on manganese oxide NPs for tumor metastasis diagnosis.

Radiolabeled, Antibody-Conjugated Manganese Oxide Nanoparticles for Tumor Vasculature Targeted Positron Emission Tomography and Magnetic Resonance Imaging.

Manganese oxide nanoparticles (Mn3O4 NPs) have attracted a great deal of attention in the field of biomedical imaging because of their ability to create an enhanced imaging signal in MRI as novel potent T1 contrast agents. In this study, we present tumor vasculature-targeted imaging in mice using Mn3O4 NPs through conjugation to the anti-CD105 antibody TRC105 and radionuclide copper-64 (64Cu, t1/2: 12.7 h). The Mn3O4 conjugated NPs, 64Cu-NOTA-Mn3O4@PEG-TRC105, exhibited sufficient stability in vitro and in vivo. Serial positron emission tomography (PET) and magnetic resonance imaging (MRI) studies evaluated the pharmacokinetics and demonstrated targeting of 64Cu-NOTA-Mn3O4@PEG-TRC105 to 4T1 murine breast tumors in vivo, compared to 64Cu-NOTA-Mn3O4@PEG. The specificity of 64Cu-NOTA-Mn3O4@PEG-TRC105 for the vascular marker CD105 was confirmed through in vivo, in vitro, and ex vivo experiments. Since Mn3O4 conjugated NPs exhibited desirable properties for T1 enhanced imaging and low toxicity, the tumor-specific Mn3O4 conjugated NPs reported in this study may serve as promising multifunctional nanoplatforms for precise cancer imaging and diagnosis.

Silica Cross-Linked Micellar Core--Shell Nanoparticles Encapsulating IR-780 with Strong Bright and Good Biocompatibility for Optical Imaging In Vivo.

IR-780 iodide, a near-infrared (NIR) fluorescent heptamethine dye, has attracted wide attention in the biomedical community. However, hydrophobicity and toxicity severely limit its further biomedical application. Although many successful efforts have been made to increase its solubility and biocompatibility, a strong fluorescent signal and longer retention time are in high demand in biomedical application in vivo as well as basic science research. In this work, we report the development of novel silica cross-linked micellar core­shell nanoparticles encapsulating IR-780 (NIR-Silica NPs) and their utility for biomedical imaging in vivo. Compared to free IR-780, the solubility of NIR-Silica NPs was not only greatly increased, but there was also a dramatic 5­7-fold enhancement of fluorescence intensity. More importantly, the exceptionally small size (25 nm) and colloidal stability of the NPs are also sufficient to realize the enhanced permeability and retention effect. In vitro cell viability assays further indicated that the NIR-Silica NPs are safer even at the highest concentration tested of 1.0 mg/mL. Finally, sentinel lymph node mapping and long-term tumor imaging in vivo demonstrated that the NIR-Silica NPs have a long circulation time and higher signal-to-noise ratio at a very low dye concentration and short exposure time (0.2 s). Therefore, the NIR-Silica NPs may be a promising fluorescence imaging agent for clinical application.

Removing Noises Induced by Gamma Radiation in Cerenkov Luminescence Imaging Using a Temporal Median Filter.

Cerenkov luminescence imaging (CLI) can provide information of medical radionuclides used in nuclear imaging based on Cerenkov radiation, which makes it possible for optical means to image clinical radionuclide labeled probes. However, the exceptionally weak Cerenkov luminescence (CL) from Cerenkov radiation is susceptible to lots of impulse noises introduced by high energy gamma rays generating from the decays of radionuclides. In this work, a temporal median filter is proposed to remove this kind of impulse noises. Unlike traditional CLI collecting a single CL image with long exposure time and smoothing it using median filter, the proposed method captures a temporal sequence of CL images with shorter exposure time and employs a temporal median filter to smooth a temporal sequence of pixels. Results of in vivo experiments demonstrated that the proposed temporal median method can effectively remove random pulse noises induced by gamma radiation and achieve a robust CLI image.

Investigation of injection dose and camera integration time on quantifying pharmacokinetics of a Cy5.5-GX1 probe with dynamic fluorescence imaging in vivo.

The aim of this article is to investigate the influence of a tracer injection dose (ID) and camera integration time (IT) on quantifying pharmacokinetics of Cy5.5-GX1 in gastric cancer BGC-823 cell xenografted mice. Based on three factors, including whether or not to inject free GX1, the ID of Cy5.5-GX1, and the camera IT, 32 mice were randomly divided into eight groups and received 60-min dynamic fluorescence imaging. Gurfinkel exponential model (GEXPM) and Lammertsma simplified reference tissue model (SRTM) combined with a singular value decomposition analysis were used to quantitatively analyze the acquired dynamic fluorescent images. The binding potential (Bp) and the sum of the pharmacokinetic rate constants (SKRC) of Cy5.5-GX1 were determined by the SRTM and EXPM, respectively. In the tumor region, the SKRC value exhibited an obvious trend with change in the tracer ID, but the Bp value was not sensitive to it. Both the Bp and SKRC values were independent of the camera IT. In addition, the ratio of the tumor-to-muscle region was correlated with the camera IT but was independent of the tracer ID. Dynamic fluorescence imaging in conjunction with a kinetic analysis may provide more quantitative information than static fluorescence imaging, especially for a priori information on the optimal ID of targeted probes for individual therapy.