Luminescence imaging with refinement cycles based on a combination of local illumination

Abstract

A technical solution is presented for imaging an object containing a luminescent substance. A corresponding method (500) is based on a refinement loop. In each iteration of the refinement loop, a different spatial pattern (516-518, 524; 536-540, 546) is determined, a local illumination corresponding to the spatial pattern is applied to the object (520, 526; 542, 548), a component image is acquired in response to the local illumination (522, 528; 544, 550), and the component images are combined (530; 552) into a combined image. A corresponding system (100) is also presented. Furthermore, a computer program (400) and a corresponding computer program product are presented. A diagnostic method, a surgical method, and a therapeutic method based on the same solution are also presented.

Description

Technical Field

[0001] This disclosure relates to imaging applications. More specifically, this disclosure relates to luminescent imaging. Background Technology

[0002] The background of this disclosure is introduced below through a discussion of the techniques associated with that background. However, even if the discussion involves documents, actions, artifacts, etc., it does not imply or imply that the techniques discussed are part of the prior art in the field related to this disclosure or are common general knowledge.

[0003] Imaging typically involves a variety of techniques that allow images of objects (often not directly visible) to be acquired in a substantially non-invasive manner. For example, imaging techniques are often used in devices for medical applications to examine (internal) body parts of a patient or samples thereof for diagnostic, therapeutic, and / or surgical purposes.

[0004] One imaging technique that is increasingly being considered is luminescence imaging, and especially fluorescence imaging. Luminescence imaging is based on the phenomenon of light emission, which consists of (luminescent) substances emitting light when excited by any means other than heating; in particular, fluorescence occurs in certain substances called fluorophores, which emit light when illuminated. More specifically, when a fluorophore absorbs excitation light at a wavelength within its excitation spectrum, it enters an excited (electronic) state. The excited state is unstable, and therefore the fluorophore quickly decays to the ground (electronic) state. As fluorophores decay, they emit (fluorescence) light at a characteristic wavelength (longer than the excitation light wavelength because the energy in the excited state is dissipated as heat). The intensity of the fluorescence depends on the amount of fluorophore being illuminated.

[0005] Fluorescence imaging in medical applications is typically based on the application of fluorescent agents. In particular, fluorescence molecular imaging (FMI) applications are based on the use of targeted fluorescent agents. Each targeted fluorescent agent is applied to a specific molecule that reaches the desired target and is then immobilized thereon (e.g., due to a specific interaction with tumor tissue). The fluorescence emitted by the immobilized (targeted) fluorescent agent allows for the detection of the corresponding target (tumor tissue in the example discussed). Specifically, structured irradiation can be utilized. In this case, the excitation light is spatially modulated in a periodic pattern. Since the tissue acts as a spatial low-pass filter, this allows for the separation of superficial features from deep features.

[0006] Fluorescence imaging can be used for a variety of purposes in medical applications. For example, the representation of (fixed) fluorescent agents in images of body parts allows for the identification of corresponding lesions (which would otherwise be difficult to identify); furthermore, the intensity of the fluorescence emitted by the fluorescent agent allows for the quantification of lesions. This information can be used in diagnostic applications for the detection or monitoring of lesions, in therapeutic applications for delineating lesions to be treated, and in surgical applications for identifying the margins of lesions to be excised.

[0007] Fluorescence imaging is very practical because fluorescent agents are inexpensive, can be mass-produced due to their high stability, are easy to handle and dispose of, and allow for the simultaneous detection of different targets by using corresponding targeted contrast agents that emit fluorescence at different wavelengths based on fluorophores.

[0008] However, the performance of fluorescence imaging is limited by the optical properties of tissues (i.e., scattering and absorption). In particular, visible wavelength excitation light only allows imaging of body parts at very low (penetration) depths (not exceeding 100 μm). Therefore, higher wavelength excitation light is typically used (to reduce absorption by the tissue, which acts as an optical long-pass filter) to increase penetration depth; for example, near-infrared (NIR) wavelengths (i.e., 750–900 nm) excitation light allow imaging of body parts up to 1–2 cm deep.

[0009] However, NIR light increases tissue scattering. This degrades the quality of the acquired images of the body part. In particular, the image becomes relatively diffuse; furthermore, targets at different depths within the body part may become blurred. All of these hinder the detection of desired targets within the body part. For example, in diagnostic applications, this adversely affects the identification and / or quantification of corresponding lesions, potentially leading to misinterpretations (risks of false positives / false negatives and erroneous follow-ups). In therapeutic applications, this adversely affects the depiction of the corresponding lesion to be treated (risks of reduced treatment effectiveness or damage to healthy tissue). In surgical applications, this results in uncertainty in the accurate identification of lesion margins (risks of incomplete lesion resection or excessive removal of healthy tissue). Summary of the Invention

[0010] To provide a basic understanding of this disclosure, a simplified summary of this disclosure is given herein; however, the sole purpose of this summary is to introduce some concepts of this disclosure in a simplified form as a prelude to the more detailed description that follows, and it should not be construed as an indication of its key elements or a description of its scope.

[0011] Generally speaking, this disclosure is based on the idea of ​​applying a refined loop based on local illumination.

[0012] Specifically, one aspect provides a method for imaging an object containing luminescent material. This method is based on a thinning loop. In each iteration of the thinning loop, a different spatial pattern is determined, a local illumination corresponding to the spatial pattern is applied to the object, component images are acquired in response to the local illumination, and the component images are combined into a composite image.

[0013] On the other hand, a computer program for implementing this method is provided.

[0014] On the other hand, it provides corresponding computer program products.

[0015] On the other hand, a system for implementing this method is provided.

[0016] On the other hand, it provides corresponding diagnostic methods.

[0017] On the other hand, it provides corresponding treatment methods.

[0018] On the other hand, corresponding surgical methods were provided.

[0019] More specifically, one or more aspects of this disclosure are set forth in the independent claims, and their advantageous features are set forth in the dependent claims, the wording of which is incorporated herein by reference verbatim (with any advantageous features provided with reference to any particular aspect, adapted to the various other aspects). Attached Figure Description

[0020] The technical solutions, further features, and advantages of this disclosure will be best understood with reference to the following specific embodiments of this disclosure, which are given purely by way of non-limiting indication and will be discussed in conjunction with the appendix. Figure 1 This section is for reading purposes (where, for simplicity, corresponding elements are represented by the same or similar references, and their interpretations are not repeated; the name of each entity is typically used to indicate both its type and its attributes, such as value, content, and representation). Specifically:

[0021] Figure 1 A schematic block diagram of a fluorescence imaging system that can be used to practice the technical solutions according to embodiments of this disclosure is shown.

[0022] Figures 2A-2D An application example of the technical solution according to the embodiments of this disclosure is shown.

[0023] Figures 3A-3E Further application examples of the technical solutions according to embodiments of this disclosure are shown.

[0024] Figure 4 The main software components that can be used to implement the technical solutions according to embodiments of this disclosure are shown.

[0025] Figures 5A-5B An activity diagram is shown describing the activity flow related to the implementation of the technical solutions according to embodiments of the present disclosure, and

[0026] Figure 6 An example of an in vitro application of the technical solution according to an embodiment of this disclosure is shown. Detailed Implementation

[0027] refer to Figure 1A schematic block diagram of a fluorescence imaging system 100 is shown, which can be used to implement the technical solutions according to embodiments of the present disclosure. The fluorescence imaging system 100 is used to examine body parts 105 of a patient 110 (to which a fluorescent agent has been administered), for example, for diagnostic, therapeutic and / or surgical purposes.

[0028] The imaging system 100 has an imaging probe 115 for acquiring images of body parts 105 and a central unit 120 for controlling its operation.

[0029] Starting with imaging probe 115, it includes the following components.

[0030] Light source 125 generates excitation light for illuminating body part 105, the wavelength and energy of which are suitable for exciting fluorophores of a fluorescent agent (e.g., NIR type) previously applied to body part 105 (e.g., intravenous or local injection). Light source 125 may be a laser (producing a narrow beam of collimated coherent light of a specific wavelength) equipped with a filter (selecting the desired wavelength if the laser produces multiple wavelengths, and in any case, cleaning the excitation light to remove unwanted stray wavelengths). In another embodiment, the light source may be based on a solid-state light-emitting diode (LED), or on a combination or stack of multiple LEDs with sufficient spectral characteristics and optical power (also with a cleaning filter). In yet another embodiment, the light source may be based on the blackbody radiation of a filament-based bulb or a gas discharge-based light source (e.g., a halogen lamp or xenon discharge lamp), equipped with sufficient optics to confine to the desired wavelength (e.g., a spectral bandpass filter, a cold mirror, or a combination thereof). Transmission optics 130 transmits the excitation light generated by light source 125. For example, the transmitting optics 130 may include a rigid light guide (e.g., based on lenses and mirrors (guiding the excitation light along a corresponding optical path)) or a flexible light guide (e.g., a single optical fiber, fiber bundle, or liquid light guide). A spatial modulator 135 receives the excitation light via the transmitting optics 130, spatially modulates it (as described in detail below), and then applies the corresponding illumination to the body part 105. In the example discussed, where the excitation light provided by the laser from the light source 125 is a beam illuminating only a single point, for this purpose, the spatial modulator 135 rapidly moves the beam in two dimensions (via a galvanometer mirror system) to scan the entire surface of the body part 105. For example, the spatial modulator 135 may be a digital mirror device or DMD (providing selective reflection controlled by micromechanical structures), a liquid crystal display or LCD (providing electronically controlled selective transmission illumination), a galvanometer mirror (deflecting the illumination beam according to current), and so on. A collecting optics 140 collects the light from the body part 105 (in the incident illumination geometry), including fluorescence emitted by a fluorescein present herein. For example, the collecting optics 140 includes an objective lens (focusing the collected light) and a confocal optics (blocking defocused collected light that is not from the point of illumination of the body part 105). A beam splitter 145 splits the collected light into two channels. For example, the beam splitter 145 is a dichroic mirror (transmitting and reflecting collected light with wavelengths above and below its characteristic wavelength, respectively). In one of the channels of the beam splitter 145 (e.g., the transmission channel), where a portion of the collected light includes the fluorescence wavelength, an emission filter 150 filters the collected light to remove the wavelengths of the excitation light (which may be reflected by the body part 105) and any ambient light (which may be reflected or generated by background / inherent fluorescence), so that only the fluorescence wavelength remains.A fluorescence camera 155 receives (filtered) collected light from an emission filter 150 and produces a corresponding (fluorescence) image representing the fluorescence emitted by the body part 105. For example, the fluorescence camera 155 is based on a CCD sensor. The CCD sensor comprises an array of CCD cells, each accumulating a charge proportional to the fluorescence intensity at a corresponding location on the body part 105 (composed of its basic components). Control circuitry transfers the accumulated charge in each CCD cell to a charge amplifier, which converts it into a corresponding analog voltage. An analog-to-digital converter (ADC) converts each analog voltage into a digital value (which may be preserved as is to maintain proportionality to the amount of fluorophore present at the location, or may be non-linearly compressed (e.g., logarithmically compressed) to uniformly distribute digitization errors). In another channel of a beam splitter 145, where a portion of the collected light excludes the wavelength of the fluorescence (consisting primarily of the reflected light from the body part 105), a camera 160 (also based on a CCD sensor) receives the collected light and generates a corresponding (photographic) image representing the visualized body part 105. Depending on the required wavelength or other design goals, one or more cameras can also be implemented using other sensor technologies, such as enhancement charge-coupled devices (ICCDs), electron multiplier charge-coupled devices (EMCCDs), complementary metal-oxide-semiconductor (CMOS), indium gallium arsenide (InGaAs), etc.

[0031] The process moves to a central unit 120, which comprises several units interconnected via a bus structure 165. Specifically, one or more microprocessors (μPs) 170 provide processing and orchestration functions for the central unit 120. Non-volatile memory (ROM) 175 stores the basic code for the boot program of the central unit 120, and volatile memory (RAM) 180 is used as working memory by the microprocessors 170. The central unit 120 is provided with a large-capacity memory 185 (e.g., a solid-state drive (SSD)) for storing programs and data. In addition, the central unit 120 includes multiple controllers 190 for peripheral devices or input / output (I / O) units; specifically, the controllers 190 control the light source 125, spatial modulator 135, fluorescence camera 155, and camera 160 of the imaging probe 115; furthermore, the controllers 190 control other peripheral devices, collectively indicated by reference numeral 195, such as a monitor for displaying images of body parts 105, a keyboard for inputting commands, a trackball for moving a pointer on the monitor, a drive for reading / writing removable storage units (e.g., optical discs such as DVDs), and a network interface card (NIC) for connecting to a communication network (e.g., a LAN).

[0032] Now for reference Figures 2A-2D This illustrates an application example of the technical solution according to embodiments of the present disclosure.

[0033] from Figure 2A Beginning, initial irradiation 205 is applied to a body part, which includes a target area containing a fluorescent agent. Initial irradiation 205 is based on an initial spatial pattern that provides full illumination (white) of the body part so that excitation light reaches all its locations. In response to initial irradiation 205, an initial (fluorescent) image 210 is acquired. Initial image 210 includes a detection portion 215 in which fluorescence is detected. Due to scattering by tissue in the body part, initial image 210 is generally diffuse, and detection portion 215 is larger than the target portion 220 that actually represents the target area (drawn with a dashed line to indicate that it is indistinguishable in initial image 210).

[0034] Move to Figure 2B In the technical solution according to the embodiments of this disclosure, a refinement loop is performed to reduce diffusion. The refinement loop processes the combined (fluorescence) image, which is initialized as a starting image. Specifically, a primary local irradiation 225a is applied to the body part. The primary local irradiation 225a corresponds to a primary spatial pattern that provides full illumination (white) of the body part region corresponding to the detection portion of the previous (version) combined image and non-irradiation (black) of the body part region corresponding to the remaining non-detection portion of the previous combined image, so that the excitation light reaches and does not reach the corresponding positions of the body part, respectively. In response to the primary local irradiation 225a, a primary component (fluorescence) image 230a is acquired. The primary component image 230a includes a detection portion 235a in which fluorescence is detected. The lack of irradiation of the tissue surrounding the target region results in less diffusion in the primary component image 230a, and the detection portion 235a is smaller than the detection portion in the previous combined image (but still larger than the target portion 220). In addition, a secondary local irradiation 225b is applied to the body part. Secondary local illumination 225b corresponds to a secondary spatial pattern, which is a negative of the primary spatial pattern of primary local illumination 225a, providing unilluminated (black) and illuminated (white) body part regions corresponding to the detected and undetected portions of the previously combined image, respectively. A secondary component (fluorescence) image 230b is acquired in response to secondary local illumination 225b. Because the target region is not illuminated, no fluorescence is detected in the entire secondary component image 225b. The (primary / secondary) component images 230a and 230b are combined into a (new version) combined image 240ab by summing the (primary / secondary) component images for each location of the body part. As a result, the combined image 240ab has a detection portion 245ab (where fluorescence is detected) that is the same as (larger than) the target portion 220 of the detection portion 235a.

[0035] Go to Figure 2CThe same operation is repeated in each iteration of the refinement cycle. Specifically, a primary local irradiation 225c with a primary spatial pattern defined according to the previously combined image is applied to the body part. In response to the primary local irradiation 225c, a primary component (fluorescence) image 230c is acquired, which includes a detection portion 235c in which fluorescence is detected. Because less and less tissue around the target region is irradiated in each iteration of the refinement cycle, at some point, the detection portion 235c becomes substantially the same as the target portion. Furthermore, a secondary local (irradiation) 225d is applied to the body part, where the secondary spatial pattern is the negative of the primary spatial pattern of the primary local irradiation 225c. Similarly, in response to the secondary local irradiation 225d, a secondary component (fluorescence) image 230d in which no fluorescence is detected is acquired. The (primary / secondary) component images 230c and 230d are combined into a (new version) combined image 240cd (by summing them for each location of the body part). As a result, the combined image 235cd has a detection portion 245cd that is the same as (equal to) the target portion 235c.

[0036] Then the detected portion of the component image remains the same as the target portion. In fact, as... Figure 2D As shown, the primary spatial pattern of the primary local illumination 225e can even provide a primary component (fluorescence) image 230e that includes a detection portion 235e smaller than the target portion 220. However, in this case, the secondary local illumination 225f will provide a secondary component (fluorescence) image 230f, where the secondary spatial pattern is the negative of the primary spatial pattern of the primary local illumination 225e, and the secondary component (fluorescence) image 230f includes the detection portion 235f for the remaining portion of the target portion 220. Therefore, the (new version) combined image 240ed obtained by summing the (primary / secondary) component images 230e and 230f for each location of the body part will have a detection portion 245ef (still equal to the target portion 220) given by the union of the detection portions 235e and 235f.

[0037] Now for reference Figures 3A-3E This illustrates further examples of the application of the technical solutions according to embodiments of the present disclosure.

[0038] from Figure 3AInitial irradiation 305 is applied to the body part; as described above, initial irradiation 305 is based on an initial spatial pattern that provides full irradiation (white) of the body part. In this case, the body part includes two target regions containing a fluorescent agent; specifically, a superficial target region is disposed on the surface of the body part (e.g., skin in diagnostic / therapeutic applications or exposed tissue in surgical applications), and a deep target region is disposed below the surface of the body part. An initial (fluorescent) image 310 is acquired in response to initial irradiation 305. The initial image 310 includes two detection portions 315S and 315D in which fluorescence is detected; detection portion 315S represents the superficial target region, and detection portion 315D represents the deep target region (their diffusion is disregarded for simplicity).

[0039] Move to Figure 3BIn the technical solution according to embodiments of this disclosure, a similar refinement loop is performed to distinguish shallow / deep target regions at different depths within the body part (as described above, the combined image is processed and initialized as a starting image). Specifically, a main local illumination 325a is applied to the body part. The main local illumination 325a corresponds to a main spatial pattern providing general illumination (whitest) for two regions of the body part corresponding to the detection portions of the previously combined image and general non-illumination (darkest) for regions of the body part corresponding to the remaining non-detection portions of the previously combined image, so that excitation light reaches the corresponding locations of the body part at high and low spatial frequencies, respectively. In response to the main local illumination 325a, a principal component (fluorescence) image 330a is acquired. The principal component image 330a includes two detection portions 335Sa and 335Da in which fluorescence is detected, corresponding to the shallow target region and the deep target region, respectively. The tissue acts as a spatial low-pass filter, with filtering efficiency increasing with thickness. Therefore, the high spatial frequency of illumination in the shallow target region makes the detection portion 335Sa essentially unaffected (white); conversely, the high spatial frequency of illumination in the deep target region makes the detection portion 335Da significantly darker (light gray) in its central portion, but even more significantly darker (dark gray) in its lower scattering edge portion. Furthermore, secondary local illumination 325b is applied to the body part. Secondary local illumination 325b corresponds to a secondary spatial pattern, which is a negative of the primary spatial pattern of the primary local illumination 325a, providing generally non-illuminated (blackest) and generally illuminated (whitest) regions of the body part corresponding to the detection and non-detection portions of the previously combined image, respectively. A secondary component (fluorescence) image 330b is acquired in response to secondary local illumination 325b. The secondary component image 330b includes two detection portions 335Sb and 335Db in which fluorescence is detected, corresponding to the shallow target region and the deep target region, respectively. Due to the low spatial frequency of illumination in the (shallow / deep) target regions, both detection portions 335Sb and 335Db are very dark (dark gray). By subtracting them for each location of the body part, the principal component image 330a and the secondary component image 330b are combined into a (new version) composite image 340ab. As a result, the composite image 340ab has two detection portions 345Sab and 345Dab (in which fluorescence is detected). The detection portion 345Sab, corresponding to the superficial target region, remains negligibly dark (whitest). Conversely, the detection portion 345Dab, corresponding to the deep target region, is significantly darker (dark gray) in the central portion of the detection portion 335Dab and disappears (black) at the edges of the detection portion 335Dab. Consequently, the detection portion 345Dab becomes smaller.

[0040] Move to Figure 3CThe same operation is repeated in each iteration of the refinement loop. Specifically, a primary local illumination 325c having a primary spatial pattern defined according to the previously combined image is applied to the body part. In response to the primary local illumination 325c, a principal component (fluorescence) image 330c is acquired, including two detection portions 335Sc and 335Dc in which fluorescence is detected. As described above, the detection portion 335Sc corresponding to the shallow target region is essentially unaffected (white). Conversely, because the detection portion is smaller, the inner portion of the deep target region now receives general illumination, while the outer portion of the deep target region receives general non-illumination. Therefore, as described above, the corresponding inner portion of the detection portion 335Dc is significantly darker (light gray) in its central portion, but even more significantly darker (dark gray) in its edge portion; furthermore, the corresponding outer portion of the detection portion 335Dc is very dark (dark gray). In addition, a secondary local illumination 325d is applied to the body part, wherein the secondary spatial pattern is the negative of the primary spatial pattern of the primary local illumination 325c. In response to secondary local irradiation 325d, a secondary component (fluorescence) image 330d is acquired, containing two target regions 335Sd and 335Dd in which fluorescence is detected. As described above, the detection portion 335Sd corresponding to the shallow target region is very dark (dark gray). Conversely, similarly, because the detection portion is smaller, the inner portion of the deep target region now receives generally non-irradiation, while the outer portion of the deep target region receives generally irradiation. Therefore, as described above, the corresponding inner portion of the detection portion 335Dd is very dark (dark gray), while the corresponding outer portion of the detection portion 335Dd is less dark (light gray). By subtracting them for each location of the body part, the principal component image 330c and the secondary component image 330d are combined into a (new version) combined image 340cd. As a result, the combined image 340cd has two detection portions 345Scd and 345Dcd corresponding to the shallow target region and the deep target region, respectively. As described above, the detection portion 345Scd still represents a shallow target area (negligible dimness), and the detection portion 345Dcd (inside the detection portion 335Dc) is significantly dim (dark gray) in its central portion and disappears (black) at its edge portion; furthermore, the detection portion 345Dcd also disappears (black) in the outer portion of the detection portion 335Dc. As a result, the detection portion 345Dcd also becomes smaller.

[0041] Move to Figure 3DAt a certain point, the detection portion of the combined image corresponding to the deep target region disappears. Therefore, in the next iteration of the refinement loop, the primary local illumination 325e is applied to the body part. The primary local illumination 325e corresponds to the primary spatial pattern that provides general illumination (whitest) for a single region of the body part (corresponding to the shallow target region) and general non-illumination (darkest) for the remaining region of the body part, corresponding to the single detection portion of the previously combined image. In response to the primary local illumination 325e, the principal component (fluorescence) image 330e, which includes the two detection portions 335Se and 335De in which the fluorescence is detected, is acquired. As described above, the detection portion 335Se corresponding to the shallow target region is essentially unaffected (white); conversely, the general non-illumination of the deep target region makes the detection portion 335De very dark (dark gray). Furthermore, the secondary local illumination 325f is applied to the body part, where the secondary spatial pattern is the negative of the primary spatial pattern of the primary local illumination 325e. In response to secondary local illumination 325f, a secondary component (fluorescence) image 330f is acquired, containing two detection portions 335Sf and 335Df in which fluorescence is detected. As described above, the detection portion 335Sf corresponding to the superficial target region is very dark (dark gray). Conversely, the general illumination of the deep target region makes the detection portion 335Df less dark (light gray). By subtracting them for each location of the body part, the primary component image 330e and the secondary component 330f are combined into a (new version) combined image 340ef. The component image 340ef still has a single detection portion 345Sef corresponding to the superficial target region (represented by negligible darkness), and the detection portion 335Df corresponds to the deep target region, which has still disappeared. The combined image is then left unchanged, with only the detection portion representing the superficial target region.

[0042] Move to Figure 3E If necessary, the combined image 345Sef (in the final version) can be subtracted from the initial image 310. In the corresponding inverted (fluorescence) image 350 thus obtained, the detection portion 345Sef corresponding to the shallow target region disappears; therefore, the inverted image 350 only has the detection portion 315D representing the deep target region.

[0043] The above-described technical solutions significantly improve imaging quality. Specifically, this reduces the diffusion of the representation of target areas containing fluorescent agents and / or distinguishes the representation of target areas at different depths. All of the above is beneficial for the detection of target areas in body parts. In particular, in diagnostic applications, this improves the identification and / or quantification of corresponding lesions, thus reducing misinterpretations (lower risk of false positives / false negatives and erroneous follow-ups); in therapeutic applications, it improves the depiction of corresponding lesions to be treated (lower risk of diminished treatment effectiveness or damage to healthy tissue); and in surgical applications, it makes the identification of lesion margins more accurate (lower risk of incomplete lesion resection or excessive removal of healthy tissue).

[0044] Now for reference Figure 4 The main software components that can be used to implement the technical solutions according to the embodiments of this disclosure are shown.

[0045] All software components (programs and data) as a whole are indicated by reference numeral 400 in the accompanying drawings. Software component 400, along with other software components not directly related to the technical solutions according to embodiments of this disclosure (such as operating systems, user interface modules, qualitative / quantitative analyzers, etc.) (other software components are omitted for simplicity), are typically stored in mass storage and loaded (at least partially) into the working memory of the imaging system's control unit when the program is run. The program is initially installed into the mass storage, for example, from a removable storage unit or from a network (not shown in the figures). In this respect, each program may be part of a module, segment, or code that includes one or more executable instructions for implementing a specified logical function.

[0046] Specifically, the acquisition subsystem 405 drives the acquisition components (i.e., its emission filter and fluorescence camera) of an imaging probe dedicated to continuously acquiring fluorescence images (defining a start image and primary / secondary component images) during each imaging process of a body part. The acquisition subsystem 405 (in write mode) accesses the fluorescence image stack 410. The fluorescence image stack 410 is organized as a first-in-first-out (FIFO) shift register with two locations (tail and head) for storing the last two fluorescence images. Each fluorescence image is defined by a bitmap comprising a cell matrix (e.g., with 512 rows and 512 columns), each cell storing the value of a pixel (i.e., a basic image element corresponding to the body part location); each pixel value defines the brightness of the pixel as a function of the intensity of fluorescence emitted at that location (e.g., encoded in 16 bits, increasing from 0 (black) to 2 as the fluorescence intensity increases). 16-1 (White)). The acquisition subsystem 405 further (in write mode) accesses the starting image file 415. The starting image file 415 latches the starting image of the imaging process. The combiner 420 combines each pair of (primary / secondary) component images generated by a pair of complementary (primary / secondary) local illuminations into a corresponding combined image. The combiner 420 (in read mode) accesses both the tail and head of the fluorescence image stack 410 and (in write mode) accesses the combined image file 425. The combined image file 425 stores the final combined image. The combined image is defined by a bitmap comprising a cell matrix of the same size as the fluorescence image, each cell storing the pixel value at the corresponding location (derived from the pixel values ​​of its paired component images).

[0047] Segmenter 430 segments each combined image into one or more detection portions representing corresponding detection regions of the body part (where fluorescent agent is detected (high brightness)) and one or more non-detection portions representing corresponding non-detection regions of the body part (where fluorescent agent is not detected (low brightness)). Segmenter 430 accesses the combined image file 425 (in read mode) and accesses the segmentation mask stack 435 (in write mode). The segmentation mask stack 435 is also organized as a FIFO shift register with two positions (tail and head) for storing segmentation masks that define the segmentation of the last two combined images. Each segmentation mask consists of a cell matrix of the same size as the fluorescent image, with each cell storing a segmentation flag (i.e., a binary value) for the corresponding position of the body part; the segmentation flag is set (e.g., at a logic value of 1) when fluorescent agent is detected at that position, and deset (e.g., at a logic value of 0) when no fluorescent agent is detected at that position. Modulator 440 determines each (start / major / minor) spatial pattern that defines the corresponding (start / major / minor) illumination of the body part. Modulator 440 (in read mode) accesses the tail of segmentation mask file 435 (containing the last segmentation mask) and (in write mode) accesses spatial pattern file 445. Spatial pattern file 445 stores spatial pattern masks defining spatial patterns. Spatial pattern masks are formed by a matrix of cells the same size as the fluorescence image, each cell storing an illumination marker for a corresponding location of a body part; the illumination marker is set (e.g., at logic 1) when the location is to be illuminated and de-set (e.g., at logic 0) when the location is not to be illuminated. Illumination driver 450 drives the illumination components (i.e., its light source and spatial modulator) of an imaging probe dedicated to illuminating the body part according to the spatial pattern. For this purpose, illumination driver 450 (in read mode) accesses spatial pattern file 445. Comparator 455 compares each pair of consecutive segmentation masks to determine the variation in the corresponding combined image. Comparator 455 (in read mode) accesses both the tail and head of segmentation mask stack 435. The inverter 460 inverts the distinction between target regions at different depths (from shallow to deep target regions) in the final combined image produced by the thinning cycle. For this purpose, the inverter 460 accesses the starting image file 415 (in read mode) and accesses the combined image file 425 (containing the final combined image) (in read / write mode).

[0048] Camera driver 465 drives the camera of the imaging probe for continuously acquiring photographic images during imaging. Camera driver 465 (in write mode) accesses photographic image file 470. Photographic image file 470 stores the final photographic image. The photographic image is defined by a bitmap comprising a cell matrix (the size of which may be the same as or different from the size of the fluorescence image), each cell storing a pixel value for a corresponding location of a body part (the same as or different from the fluorescence image); each pixel value defines the brightness of the pixel as a function of the intensity of the (visible) light reflected from the corresponding location (e.g., encoded in 12 bits, represented in grayscale, increasing from 0 (black) to 2 as the intensity of the reflected light increases). 12 -1 (white)). The camera can be equipped with pixel-interleaved spectral filters for different colors (e.g., red, green, and blue for a Bayer mode filter). In this case, the image in subsequent steps is deinterleaved to insert each pixel value (in this tri-color implementation: red, green, and blue). For this tri-color implementation, the photographic image is stored accordingly in tri-color representation.

[0049] Overlay layer 475 continuously generates overlapping images in each imaging process based on the corresponding sets of the final combined image, segmentation mask, and photographic image. Overlay layer 475 (in read mode) accesses the combined image file 425 (containing the final combined image), the tail of the segmentation mask stack 435 (containing the corresponding final segmentation mask), and the photographic image file 470 (containing the final photographic image), and (in write mode) it accesses the overlapping image repository 480. The overlapping image repository 480 stores the sequence of overlapping images generated during the imaging process. Each overlapping image is defined by a bitmap comprising a cell matrix (having the same size as the fluorescence image, the same size as the photographic image, or different sizes from both), each cell storing a pixel value (same or different for the fluorescence image and the photographic image) of a corresponding location of a body part, obtained by overlaying the combined image processed according to the segmentation mask onto the photographic image (described below). Monitor driver 485 drives the monitor of the central unit to continuously display the overlapping images. Monitor driver 485 accesses the overlapping image repository 480 (in read mode).

[0050] The imaging manager 490 manages the imaging process. For this purpose, the imaging manager 490 is integrated with the aforementioned software module.

[0051] Now for reference Figures 5A-5B An activity diagram is shown that describes the activity flow related to the implementation of the technical solutions according to embodiments of the present disclosure.

[0052] In particular, the activity diagram represents an exemplary process that can be used to image body parts via method 500. In this respect, each block may correspond to one or more executable instructions for implementing a specified logical function on a control server.

[0053] Prior to imaging of a body site, a medical professional (e.g., a nurse or doctor) administers a fluorescent agent to the patient (the timing of administration is determined based on the pharmacokinetics of the fluorescent agent). For example, the fluorescent agent is a target-specific fluorescent agent, adapted to attach to a specific (biological) target through a specific interaction with that target. The desired behavior can be achieved by incorporating target-specific ligands into the formulation of the fluorescent agent, for example, based on chemical binding properties and / or physical structures suitable for interaction with different tissues, vascular properties, metabolic properties, etc. For example, in oncology (diagnostic / therapeutic / surgical) applications, the fluorescent agent may be target-specific for tumor tissue. As another example, in any surgical application, the fluorescent agent may be target-specific for vital tissues (e.g., nerves, blood vessels, lymph nodes, or lymphatic vessels) that should be preserved during surgery. The fluorescent agent is administered to the patient intravenously in a given dose (using a syringe); as a result, the contrast agent circulates within the patient's vascular system until it reaches the body site and binds to the desired target (e.g., through interaction with it at the molecular level). Conversely, unbound fluorescent agent is cleared from the blood pool according to its corresponding blood half-life and thus from the body site. The timing of these processes depends on several parameters and can be influenced by the chemical properties of the targeting agent (e.g., in the case of a target-specific fluorescent agent from a humanized antibody, sufficient administration time is indicated as approximately 24–72 hours before the imaging procedure). During the imaging procedure (i.e., diagnostic examination, therapeutic treatment, or surgical intervention), the imaging probe is placed close to the body site. The operator of the imaging system (e.g., a radiologist or surgeon) then inputs a start command into the central unit to begin imaging of the body site.

[0054] In response, the imaging manager initiates the imaging process by advancing from the black starting circle 502 to block 504. Here, the modulator defines a starting spatial pattern for providing initial illumination of the body part. To this end, the modulator generates a new spatial pattern mask by setting illumination flags at all locations. As a result, the starting spatial pattern is configured to provide full illumination to all locations of the body part (i.e., forwarding the illumination provided by the light source). The modulator saves the spatial pattern mask thus obtained to a corresponding file (by replacing its previous version). The illumination driver applies the initial illumination corresponding to the spatial pattern mask extracted from the corresponding file at block 506; in this case, the illumination driver makes the excitation light reach all locations of the body part. The activity stream then branches into two operations performed simultaneously. Specifically, the acquisition subsystem acquires a new fluorescence image defining the starting image at block 508. The fluorescence camera driver adds the starting image thus obtained to the fluorescence image stack (saving it to the tail after moving the contents of the tail to the head, with the contents of the head lost); furthermore, the acquisition subsystem also saves the starting image to a corresponding file (to avoid losing it during the next refinement cycle). Simultaneously, at block 510, the camera driver acquires a new photographic image. The camera driver saves the photographic image to the appropriate file (by replacing its contents). The activity stream connects at block 512, where the combiner initializes the combined image by saving the initial image (extracted from the tail of the fluorescence image stack) to the appropriate file (by replacing its contents).

[0055] Depending on the imaging system's configuration (e.g., indicated in preset parameters, which can be changed along with the start command at runtime), the process now branches at block 514. Specifically, if the imaging system is configured to reduce diffusion in the target region, blocks 516-534 are executed, while if the imaging system is configured to distinguish target regions at different depths, blocks 536-560 are executed. In both cases, the method then merges again at block 562.

[0056] Now consider block 516 (the configuration for reducing diffusion in the target region), entering the corresponding refinement loop. Here, the segmenter retrieves the final combined image from the corresponding file (consisting of the initial image at the beginning). The segmenter then segments the combined image into one or more detected portions and one or more non-detected portions. For example, body part locations are classified by assigning each location to one of two classes: a detection class where fluorescent agents are detected and a non-detected class where fluorescent agents are not detected. Specifically, a location is assigned to either the detection or non-detection class when the pixel value of each location is (possibly strictly) above or below a segmentation threshold, respectively. The segmentation threshold can be calculated to maximize the intra-class variance between the pixel values ​​of locations assigned to the two (detection / non-detection) classes (e.g., by applying Otsu's algorithm). The detected and non-detected portions are then defined by clustering the locations into substantially homogeneous groups of detected and non-detected locations, respectively (e.g., by applying the k-nearest neighbor (kNN) algorithm). The segmenter generates a new segmentation mask by setting or desetting the segmentation flag assigned to each position in the detection or non-detection section. The segmenter then adds the resulting segmentation mask to the corresponding stack (saving the previous segmentation mask to the tail after moving it to the head, with the head contents lost).

[0057] At block 518, the modulator determines the master spatial pattern corresponding to the segmentation mask (extracted from the tail of the corresponding stack). To this end, the modulator generates a new spatial pattern mask by simply copying the segmentation mask, setting or de-setting the illumination flag for each location when the position belongs to either the detection portion or the non-detection portion. As a result, the master spatial pattern is configured to provide full illumination (i.e., forwarding the illumination provided by the light source) to the locations of the body parts corresponding to the detection portions and to provide non-illumination (i.e., blocking the illumination provided by the light source) to the locations of the body parts corresponding to the non-detection portions. The modulator saves the spatial pattern mask thus obtained to a corresponding file (by replacing its contents). The illumination driver applies master local illumination corresponding to the spatial pattern mask (extracted from the corresponding file) at block 520; in this case, the master spatial pattern ensures that the excitation light reaches only the locations of the body parts corresponding to the detection portions. The acquisition subsystem acquires a new fluorescence image at block 522, defining the corresponding principal component image. The acquisition subsystem adds the principal component image thus obtained to the fluorescence image stack (as described above). At block 524, the modulator determines a secondary spatial pattern that defines a secondary local illumination (complementary to the previous primary local illumination). To this end, the modulator generates a new spatial pattern mask by simply inverting the mask stored in the corresponding file (swapping its logic values ​​0 and 1). The modulator saves the spatial pattern mask thus obtained to the corresponding file (as described above). At block 526, the illumination driver applies the secondary local illumination corresponding to the spatial pattern mask (extracted from the corresponding file); in this case, the secondary spatial pattern ensures that the excitation light reaches only the location of the body part corresponding to the non-detected portion. At block 528, the acquisition subsystem acquires a new fluorescence image that defines a corresponding secondary component image. The acquisition subsystem adds the secondary component image thus obtained to the fluorescence image stack (as described above); as a result, the fluorescence image stack will contain a pair of (primary / secondary) component images corresponding to a pair of complementary (primary / secondary) local illuminations applied to the body part.

[0058] At block 530, the combiner extracts the pair of component images from the head and tail of the corresponding stack. The combiner generates a new combined image by summing the component images pixel-by-pixel (for each location, the pixel value of the combined image is set to the pixel value of the primary component image extracted from the head plus the pixel value of the secondary component image extracted from the tail). The combiner saves the resulting combined image to the corresponding file (as described above). Starting from the second iteration of the refinement loop, at block 532, the comparator extracts the last two segmentation masks from the head and tail of the corresponding stack, which define the segmentation of the last two combined images. The comparator calculates a similarity index that measures the similarity between the segmentation masks (and consequently, between the corresponding combined images); for example, the similarity index is calculated using Sorenson dice. The coefficient constraint is twice the number of locations with the same segmentation marker in the segmentation mask divided by the total number of locations (ranging from 0 to 1 in ascending order of similarity). The comparator verifies the exit condition of the thinning loop, constrained by the similarity index, at block 534. Specifically, if the similarity index (potentially strictly) is below the similarity threshold (e.g., 0.8–0.9), meaning the combined image is still significantly thinning, the thinning loop is iterated by returning to block 516 (always true on the first iteration of the thinning loop). Conversely, if it means the combined image has reached substantially stable thinning, the thinning loop exits by proceeding down to block 566.

[0059] Now consider block 536 (the configuration used to distinguish target regions at different depths), entering the corresponding refinement loop. As described above, the segmenter retrieves the final combined image (composed of the initial image at the beginning) from the corresponding file, segments it (dividing it into its detection and non-detection parts), generates a corresponding new segmentation mask, and adds it to the corresponding stack. Here, the modulator determines the master spatial pattern corresponding to the segmentation mask (extracted from the tail of the corresponding stack). To do this, the modulator initializes the master spatial pattern as described above at block 538, generating a new spatial pattern mask by simply copying the segmentation mask. At block 540, the modulator jitters the master spatial pattern mask (e.g., by applying the Floyd-Steinberg algorithm) to add pseudo-random illumination noise to the master spatial pattern. Therefore, the main spatial pattern is configured to provide general illumination of the body parts corresponding to the detection portions and general non-illumination of the body parts corresponding to the non-detection portions; the general illumination distributes the illumination from the light source at the respective positions of the body parts corresponding to the detection portions with a high spatial frequency (e.g., 70-90%), and distributes the illumination from the light source at the respective positions of the body parts corresponding to the non-detection portions with a low spatial frequency strictly below the high spatial frequency (e.g., equal to 0.1-0.3 of it, e.g., 10-30%). The modulator saves the spatial pattern mask thus obtained to a corresponding file (as described above). The illumination driver applies main local illumination corresponding to the spatial pattern mask (extracted from the corresponding file) at block 542; in this case, the spatial pattern causes the excitation light to primarily reach the positions of the body parts corresponding to the detection portions, and rarely reach the positions of the body parts corresponding to the non-detection portions. The acquisition subsystem acquires a new fluorescence image at block 544, which defines the corresponding principal component image. The acquisition subsystem adds the principal component image thus obtained to the fluorescence image stack (as described above). At block 546, the modulator determines a secondary spatial pattern that defines a secondary local illumination (complementary to the previous primary local illumination). To this end, as described above, the modulator generates a new spatial pattern mask by simply inverting a spatial pattern mask stored in a corresponding file (swapping its logic values ​​0 and 1). The modulator saves the spatial pattern mask thus obtained to the corresponding file (as described above). At block 548, the illumination driver applies a secondary local illumination corresponding to this spatial pattern mask (extracted from the corresponding file); in this case, the secondary spatial pattern ensures that the excitation light rarely reaches the position corresponding to the detected portion of the body part and mainly reaches the position corresponding to the undetected portion of the body part. At block 550, the acquisition subsystem acquires a new fluorescence image that defines a corresponding secondary component image. The acquisition subsystem adds the secondary component image thus obtained to the fluorescence image stack (as described above) such that it contains a pair of (primary / secondary) component images corresponding to a pair of complementary (primary / secondary) local illuminations applied to the body part as described above.

[0060] The combiner extracts the pair of component images from the head and tail of the corresponding stack at block 552. The combiner generates a new combined image by subtracting the component images pixel-by-pixel; more specifically, for each location, the pixel value of the combined image is set by subtracting the pixel value of the secondary component image extracted from the tail from the pixel value of the primary component image extracted from the head. The combiner saves the combined image thus obtained to the corresponding file (as described above). Starting from the second iteration of the thinning loop, the comparator extracts the last two segmentation masks (which define the segmentation of the last two combined images) from the head and tail of the corresponding stack at block 546 and calculates their similarity index as described above. The comparator verifies the same exit condition of the thinning loop (defined by the similarity index) at block 548. In particular, if the similarity indicator (possibly strictly) is below the similarity threshold, the thinning loop is iterated by returning to block 536 (always true during the first iteration of the thinning loop). Conversely, the thinning loop exits by going down to block 558.

[0061] Here, the activity flow branches according to the desired differentiation type of the target region at different depths. Specifically, if the imaging system is configured to differentiate deep target regions (relative to shallow target regions), the inverter extracts the starting image from the corresponding file at block 560 (acquired at the start of the thinning loop) and extracts the final combined image (representing only shallow target regions) from the tail of the corresponding stack. The inverter generates the inverted (version) combined image by subtracting the final (version) combined image pixel by pixel from the starting image; more specifically, for each position, the pixel value of the inverted combined image is set to the pixel value of the starting image minus the pixel value of the final combined image. The combiner saves the thus obtained (inverted) combined image to the corresponding file (as described above). The process then proceeds down to block 562; if the imaging system is configured to differentiate shallow target regions (relative to deep target regions), it can also proceed directly from block 558 to the same point, thus keeping the combined image unchanged. In both cases (not shown in the figure), the representation of the (selected) shallow or deep target regions selected for differentiation can also be improved here by applying the diffusion reduction techniques described above. Specifically, for each selected target region, a sub-image is extracted from the corresponding portion of the combined image (retrieved from the corresponding file), for example, by setting it to a minimum rectangle containing a representation of the selected target region. The same operation described above is then repeated for this sub-image to recover a representation of the selected target region with reduced diffusion. The extracted sub-image portion of the combined image is then replaced with the obtained result in the corresponding file.

[0062] Referring now to block 562, the overlay layer retrieves the (final) combined image from the corresponding file and its segmentation mask from the tail of the corresponding stack. The overlay layer colors the combined image by converting the pixel values ​​at each location where the corresponding segmentation flag in the segmentation mask is set (i.e., where a fluorescent agent has been detected) to a discrete level, and then associating this with a representation of the corresponding color (e.g., via an access index to a color palette); the higher the discrete level, the brighter the color. At this stage, any (preferably monotonic) transfer function can also be applied to adjust the opacity. The overlay layer further retrieves the (final) photographic image from the corresponding file in block 564. The overlay layer generates the overlay image by overlaying the (colored) combined image onto the photographic image. For this purpose, if necessary, the combined image and the photographic image are scaled to a common size and / or transposed to a common image coordinate system such that their corresponding pixel values ​​originate from the same location of the body part. An affine transformation is an advantageous implementation of this transformation, the parameters of which can be calculated in the image calibration step prior to the standard operation of the imaging system. For each location, when the corresponding segmentation flag in the segmentation mask is set (fluorescein detected), the pixel value of the overlay image is set to the (color-coded) pixel value of the combined image; conversely, in the case of no fluorescent agent detected, the pixel value of the overlay image is set to the pixel value of the photographic image. The overlay layer adds the overlay image thus obtained to the corresponding repository (at the end of the corresponding sequence). In block 566, the monitor driver continuously extracts the overlay images from the corresponding repository and displays them on the monitor (essentially in real time, except for a small delay caused by the thinning loop). In this way, the overlay image shows the presence of fluorescent agents (quantified according to the corresponding color), contextualized in the anatomical representation of the body part. For example, this information can be used for diagnostic applications to identify and / or quantify lesions (e.g., to find new lesions or monitor known lesions), for therapeutic applications to delineate lesions to be treated (e.g., by applying radiation), or for surgical applications to identify the edges of lesions to be excised (to guide surgery).

[0063] A test is performed at block 568, where the imaging manager verifies whether the imaging process has been completed. If not, the activity flow returns to block 504 to repeat the same operation. Conversely, once the operator inputs the end command for the imaging process into the central unit, the activity flow moves down to the concentric white / black stop circle 578.

[0064] Now for reference Figure 6 This illustrates an example of the in vitro application of a technical solution according to an embodiment of the present disclosure.

[0065] An optical model 605 has been created to simulate the optical properties (i.e., scattering and absorption) of biological tissue (e.g., human breast tissue). The optical model 605 is based on a liquid contained in a box with transparent walls. The optical model 605 allows for the introduction of (transparent) bottles containing a fluorescent agent. Specifically, shallow bottles 610s have been introduced into the shallow portion of the optical model 605, and deep bottles 610d have been introduced into the depth of the optical model 605, respectively near and far from the front surface 615f of the optical model 605. The optical model 605 is then imaged in different ways (via the aforementioned incident illumination geometry).

[0066] Specifically, the optical model 605 has been imaged by simply applying full illumination. The resulting fluorescence image 620 shows two (light)detection portions 625s and 625d, representing the shallow bottle 610s and the deep bottle 610d, respectively. However, the detection portions 625s and 625d are relatively dispersed; furthermore, their different depths from the front surface 615f cannot be distinguished.

[0067] As shown above, the optical model 605 has been imaged to distinguish target regions at different depths, particularly shallow target regions relative to deep target regions. The fluorescence image 630 thus obtained only shows the (light) detection portion 635s representing the shallow bottle 610s (without showing the deep bottle 610d).

[0068] The optical model 605 has been imaged as shown above to reduce diffusion in the target area. The resulting fluorescence image 640 shows two (light) detection portions 645s and 645d, representing the shallow bottle 610s and the deep bottle 610d, respectively. Detection portion 645s diffuses much less (while detection portion 645d is almost indistinguishable).

[0069] Revise

[0070] Naturally, those skilled in the art can apply numerous logical and / or physical modifications and alterations to this disclosure to meet local and specific needs. More specifically, while this disclosure has been described with a degree of particularity with reference to one or more embodiments thereof, it should be understood that various omissions, substitutions, and variations in form and detail, as well as in other embodiments, are possible. In particular, different embodiments of this disclosure may even be practiced without the specific details (e.g., numerical values) set forth in the foregoing description to provide a more thorough understanding thereof; conversely, well-known features may have been omitted or simplified to avoid obscuring the description with unnecessary detail. Furthermore, it is explicitly intended that, as a matter of general design choice, specific elements and / or method steps described in connection with any embodiment of this disclosure may be incorporated into any other embodiment. Furthermore, items and different embodiments, examples, or alternatives presented in the same group should not be construed as being factually equivalent to each other (rather, they are independent and autonomous entities). In any case, each numerical value should be modified according to applicable tolerances; in particular, terms such as “substantially,” “nearly,” “about,” etc., should be understood as “within 10%.” Furthermore, each range of numerical values ​​should be intended to explicitly specify any possible numbers along a continuum (including its endpoints) within that range. Ordinal numbers or other qualifiers are used only as markers to distinguish elements with the same name, but do not in themselves imply any priority, precedence, or order. Terms including, comprising, having, containing, relating, etc., should be intended to have an open, non-exhaustive meaning (i.e., not limited to the listed items), terms based on, depending on, according to, varying accordingly, etc., should be a non-exclusive relationship (i.e., potentially involving more variables), terms one / a should mean one or more objects (unless otherwise explicitly stated), and terms imply (or any means plus a functional formula) should be any structure adjusted or configured to perform the relevant function.

[0071] For example, one embodiment provides a method for imaging an object. However, the object can be of any type (e.g., body parts, fingerprints, mechanical components, etc.) and can be imaged for any purpose (e.g., in medical analysis, forensic applications, defect / crack inspection, etc.).

[0072] In one embodiment, the object comprises a luminescent material. However, the luminescent material can be of any non-inherent / inherent type (e.g., any luminescent agent, any naturally luminescent component, based on any luminescent phenomenon, such as fluorescence, phosphorescence, chemiluminescence, bioluminescence, induced Raman radiation, etc.), and it can be provided in any manner (e.g., applied in any manner and at any time before and / or during imaging, generated in any manner, such as for fluorescent pigments deposited in the retinal pigment epithelium in cases of age-related macular degeneration, etc.).

[0073] In one embodiment, the method is implemented under the control of a computing device. However, the computing device can be of any type (see below).

[0074] In one embodiment, the method includes initializing a composite image (by a computing device) based on an initial imaging of the object or default values. However, the composite image can be initialized in any way (e.g., based on any initial imaging of the object, such as its fluorescence image, its photographic image, etc., based on any default values, such as all white, a selected area of ​​interest being white and the rest being black, etc.).

[0075] In one embodiment, the composite image includes multiple image values ​​representing the emitted light from a corresponding location on the object by the luminescent material. However, the composite image can have any size and shape (from the entire matrix to one or more portions thereof), and it can include any type of image values ​​(e.g., pixel values, voxel values, or groups thereof representing a corresponding location on the object in any grayscale or color).

[0076] In one embodiment, the method includes (by a computing device) repeating a refinement loop until an exit condition for the refinement loop is met. However, the refinement loop may be repeated until any exit condition (e.g., depending on the result of each iteration, a predefined maximum number of iterations, etc.) is met.

[0077] In one embodiment, the refinement loop includes segmenting the composite image (by a computing device) into multiple parts representing corresponding regions of an object based on image values ​​of the composite image according to segmentation criteria. However, the composite image can be segmented into any number of parts in any manner (e.g., by applying a linear / quadratic classifier, support vector machine, kernel estimator, decision tree, neural network, etc.) (two or more for each of two or more categories, such as detected / non-detected parts, high-detected / low-detected / non-detected parts, etc.).

[0078] In one embodiment, the refinement loop includes (by a computing device) determining multiple spatial patterns. However, the spatial patterns can be any number and any type (e.g., combined to provide full illumination of an object, such as a pair of complementary spatial patterns, or more generally different from each other to elicit a particular spatial pattern), and they can be determined in any way (e.g., by calculating one of a pair of complementary spatial patterns and then inverting it to obtain the other, by directly calculating all spatial patterns, etc.).

[0079] In one embodiment, multiple spatial patterns are determined based on the segmentation of the combined image. However, spatial patterns can be determined in any way based on the segmentation (e.g., providing full illumination, no illumination, general illumination, general no illumination, etc. in any combination).

[0080] In one embodiment, the refinement cycle includes (via an illumination device) sequentially applying multiple local illuminations corresponding to a spatial pattern to the object. However, each local illumination can be applied in any manner (e.g., in any scan mode, in full mode, etc.) by any illumination device (see below).

[0081] In one embodiment, the refinement loop includes continuously acquiring corresponding component images (via an acquisition device) in response to local illumination. However, each component image can be acquired in any manner (e.g., in a single channel, in multiple channels, using any filtering, etc.) by any acquisition device (see below).

[0082] In one embodiment, each component image includes a corresponding image value representing the object's location. However, each combined image can have any size and shape, and it can include any type of image values ​​(whether the same or different relative to the combined image).

[0083] In one embodiment, the refinement loop includes (by a computing device) combining the component images into a new version of the combined image. However, the component images can be combined in any way (e.g., as is or compressed / expanded, by accumulating or subtracting the corresponding image values, or by considering only image values ​​at the same or adjacent locations, etc.).

[0084] In one embodiment, the method includes outputting an imaging result based on the final version of the combined image at the exit of the refinement loop (by an output device). However, the imaging result can be of any type (e.g., an output image representing an object, a parametric image representing the distribution of parameter values ​​throughout the object, aggregated values ​​of attributes representing the region of interest of the object, etc.), and it can be based on the final version of the combined image in any way (e.g., as is or in any other way, such as color encoding, etc.); furthermore, the imaging result can be provided by any output device (see below).

[0085] Further embodiments provide additional advantageous features, however these features may be omitted entirely in the basic implementation.

[0086] In particular, in one embodiment, the luminescent material is a fluorescent material. However, the fluorescent material can be any non-inherent / inherent type (e.g., used for imaging any pathological tissue, any healthy tissue, such as imaging gene-encoded fluorophore expression (e.g., fluorescent protein) as green fluorescent protein (GFP) in preclinical applications, etc.).

[0087] In one embodiment, the method includes applying initial illumination to the object (via an illumination device). However, the initial illumination can be any other type (e.g., total illumination, partial illumination, structural illumination, etc.).

[0088] In one embodiment, the method includes acquiring a starting image (by an acquisition device) in response to initial illumination. However, the starting image can be acquired in any manner (whether the acquisition image is the same as or different from the component images).

[0089] In one embodiment, the starting image includes corresponding image values ​​representing the emitted light from the object's location by the luminescent material. However, the starting image can have any size and shape, and it can include any type of image values ​​(whether the same or different from the combined image).

[0090] In one embodiment, the method includes initializing the combined image based on a starting image (by a computing device). However, the combined image can be initialized based on the starting image in any way (e.g., as is or after any processing, etc.).

[0091] In one embodiment, the method includes applying initial illumination to the object (via an illumination device) to provide full illumination of it. However, full illumination can be of any type (e.g., with any intensity, scan mode / full mode, etc.).

[0092] In one embodiment, the method includes (by a computing device) segmenting a combined image into at least one detection portion representing a detection region (where a luminescent agent is detected) and at least one non-detection portion representing a non-detection region (where no luminescent agent is detected). However, the detection / non-detection portions can be any number (same or different), and they can be determined in any way (e.g., by any segmentation algorithm, such as based on dynamically determined or predefined thresholds, using any clustering algorithm, such as centroid- or density-based models, histograms, edge detection, region growing, or model-based algorithms, etc.).

[0093] In one embodiment, the method includes (by a computing device) determining a master spatial pattern for providing full illumination of the at least one detection region and non-illumination of the at least one non-detection region. However, the full illumination and non-illumination of the master spatial pattern can be of any type (e.g., uniform to provide a sharp transition, reduced towards the boundaries of the detection and non-detection regions respectively to provide a slow gradient transition between them, etc.).

[0094] In one embodiment, the method includes (by a computing device) determining a secondary spatial pattern for providing non-illumination of the at least one detection region and full illumination of the at least one non-detection region. However, the full illumination and non-illumination of the secondary spatial pattern can be of any type (same or different relative to the primary spatial pattern).

[0095] In one embodiment, the method includes combining component images (by a computing device) into a combined image by accumulating corresponding image values ​​of the component images to each image value of the combined image. However, the image values ​​can be accumulated in any way (e.g., based on their sum, product, integral, with or without time-dependent smoothing weights, etc.).

[0096] In one embodiment, the method includes combining component images (by a computing device) into a combined image by setting each image value of the combined image according to the sum of corresponding image values ​​of the component images. However, the image values ​​can be summed in any way (e.g., as is, weighted, etc.).

[0097] In one embodiment, the method includes (by a computing device) determining a master spatial pattern for providing general illumination distributed across the at least one detection region. However, the master spatial pattern can provide any general illumination of the detection regions (e.g., by alternating illumination and non-illumination, by varying illumination intensity, by random distribution, by uniform distribution, by illumination decreasing towards the boundary to provide a slow gradient transition, etc.), and any type of illumination of the non-detection regions (e.g., general non-illumination, non-illumination, etc.).

[0098] In one embodiment, the method includes (by a computing device) determining a secondary spatial pattern for providing general illumination distributed across the at least one non-detection region. However, the secondary spatial pattern can provide any general illumination of the non-detection regions (which may be the same as or different from the general illumination of the detection regions in the primary spatial pattern), as well as any type of illumination of the detection regions (e.g., general non-illumination, non-illumination, etc.).

[0099] In one embodiment, the method includes (by a computing device) determining a master spatial pattern for providing a general non-illuminated distribution over the at least one non-detection region. However, the master spatial pattern can provide any general non-illuminated distribution over the non-detection region (e.g., by alternating illumination and non-illumination, by varying illumination intensity, having a random distribution, having a uniform distribution, having illumination increasing towards the boundary to provide a slow gradient transition, etc.), and any type of illumination over the detection region (e.g., general illumination, full illumination, etc.).

[0100] In one embodiment, the method includes (by a computing device) determining a secondary spatial pattern for providing general non-illumination distributed across the at least one detection region. However, the secondary spatial pattern can provide any general non-illumination of the detection region (which may be the same as or different from the general non-illumination of the non-detection region in the primary spatial pattern), and any type of illumination of the non-detection region (e.g., general illumination, full illumination, etc.).

[0101] In one embodiment, universal illumination provides a randomly distributed illumination, while universal non-illumination provides a randomly distributed non-illumination. However, the random distributions can be of any type, the same or different (e.g., true random, pseudo random, etc.), and they can be obtained in any way (e.g., by adding illumination noise to full illumination and non-illumination respectively, by generating them directly, etc.).

[0102] In one embodiment, general illumination provides illumination with a high spatial frequency, while general non-illumination provides illumination with a low spatial frequency lower than the high spatial frequency. However, the high and low spatial frequencies can have any values ​​(relative or absolute).

[0103] In one embodiment, the method includes (by a computing device) initializing at least one of a spatial pattern for providing full illumination for each region requiring general illumination and a spatial pattern for providing non-illumination for each region requiring general non-illumination. However, the initialization may be applied in any manner (same as or different from the above) to either the primary spatial pattern or the secondary spatial pattern (when the other spatial pattern is obtained by inversion) or to both the primary and secondary spatial patterns (when each of them is directly computed).

[0104] In one embodiment, the method includes (by a computing device) adding pseudo-random illumination noise to each initialized spatial pattern. However, the pseudo-random illumination noise can be added in any way (e.g., using any dithering algorithm, such as patterned or ordered types, or more generally using any pseudo-random generator, such as a non-uniform or mid-square type pseudo-random generator, etc.).

[0105] In one embodiment, the method includes combining component images (by a computing device) into a composite image by subtracting corresponding image values ​​of component images generated from a primary spatial pattern and corresponding image values ​​of component images generated from a secondary spatial pattern to obtain each image value of the composite image. However, the image values ​​can be subtracted in any way (e.g., based on their difference, ratio, derivative, with or without time-dependent smoothing weights, etc.).

[0106] In one embodiment, the method includes combining component images (by a computing device) into a combined image by setting each image value of the combined image based on the difference between corresponding image values ​​of the component images generated by the primary spatial pattern and corresponding image values ​​of the component images generated by the secondary spatial pattern. However, image values ​​can be subtracted in any way (e.g., as is, weighted, etc.).

[0107] In one embodiment, the method includes (by a computing device) inverting the final version of the combined image by setting each image value of the final version of the combined image according to the difference between the corresponding image values ​​of the starting image and the corresponding image values ​​of the final version of the combined image. However, the image values ​​can be subtracted in any way (e.g., as is, weighted, etc.); in any case, this operation can be performed to replace the final version of the combined image, create an additional version of the combined image, or the operation can be omitted entirely.

[0108] In one embodiment, the spatial pattern combination for each iteration of the refinement loop is used to provide full illumination of the object. However, the spatial patterns can be combined in any way to provide full illumination (e.g., corresponding image values ​​at each location provide full or no illumination, local illumination is summed to full illumination, etc.).

[0109] In one embodiment, the method includes (by a computing device) verifying an exit condition based on the combined image. However, the exit condition can be verified based on the combined image in any way (e.g., based on similarity, rate of change, etc. between successive versions).

[0110] In one embodiment, the method includes verifying an exit condition (by a computing device) based on the similarity of the current version of the combined image produced by the current iteration of the refinement loop (different from the first iteration of the refinement loop) with respect to at least one previous version of the combined image produced by previous iterations of the refinement loop. However, the similarity can be limited in any way (e.g., based on Sorenson dice). Jaccard, Bray–Curti, Czekanowski, Steinhaus, Pielou, Hellinger indices, etc., can be used in any way to verify exit conditions (e.g., once the similarity between two final versions of the combined image reaches any threshold, when this is true for two or more consecutive iterations of the refinement loop, etc.).

[0111] In one embodiment, the object is a body part. However, a body part can be of any type (e.g., an organ (e.g., liver, prostate, or heart), region, tissue, etc.) and in any state (e.g., in a living body, in a dead body, extracted from a body, such as a biopsy sample, etc.).

[0112] In one embodiment, the body part is the patient. However, the patient can be of any type (e.g., human, animal, etc.).

[0113] In one embodiment, the luminescent agent is pre-administered to the body site prior to performing the method. However, the luminescent agent can be of any type (e.g., any targeted luminescent agent, such as those based on specific or non-specific interactions, any non-targeted luminescent agent, etc.), and it can have been pre-administered in any manner (e.g., using a syringe, infusion pump, etc.) at any time (e.g., in advance, just before the method is performed, during which it is performed continuously, etc.). In any case, this is a data processing method that can be implemented independently of any interaction with the patient; furthermore, the luminescent agent can also be administered to the patient in a non-invasive manner (e.g., orally for gastrointestinal imaging, via a nebulizer into the airway, or applied as a topical spray during surgery) or in any situation without any substantial physical intervention requiring specialized medical expertise or posing any health risk to the patient (e.g., intramuscular injection). While this method can benefit the physician's task, it only provides intermediate results that can assist him / her, and strictly speaking, medical activities are always performed by the physician himself / herself.

[0114] Another embodiment provides another method for imaging an object. As described above, in one embodiment, the object contains a luminescent material; in another embodiment, the method is implemented under the control of a computing device; and in yet another embodiment, the method includes (by the computing device) repeating a refinement loop until an exit condition for the refinement loop is met.

[0115] In one embodiment, the refinement cycle includes (by a computing device) determining a plurality of spatial patterns, wherein the spatial patterns are predefined at the first iteration of the refinement cycle and further defined by applying preset modifications to their previous versions in each subsequent iteration of the refinement cycle. However, the spatial patterns can be any number and can be determined in any manner. For example, one can begin with two complementary spatial patterns providing uniform illumination with either a high spatial frequency (for reducing diffusion) or a low spatial frequency (for distinguishing different depths); for example, in the next iteration of the refinement cycle, the spatial patterns can be changed by decreasing the high spatial frequency or by increasing the low spatial frequency (e.g., by a fixed percentage), by moving them (e.g., by a fixed offset), etc.

[0116] As described above, in one embodiment, the refinement loop includes (by an illumination device) continuously applying multiple local illuminations corresponding to a spatial pattern to an object; in one embodiment, the refinement loop includes continuously acquiring corresponding component images in response to the local illuminations (by an acquisition device); in one embodiment, each component image includes multiple image values ​​representing light emitted by a luminescent substance from a corresponding location on the object; in one embodiment, the refinement loop includes (by a computing device) combining the component images into a new version of a combined image; in one embodiment, the combined image includes corresponding image values ​​representing light emitted by a luminescent substance from a location on the object; and in one embodiment, the method includes outputting an imaging result based on the final version of the combined image (by an output device) at the refinement loop exit.

[0117] The same considerations noted above also apply to this embodiment (e.g., additional features of the combined image of the final version of the inverted reference, the exit condition of the refined loop, the application to body parts, etc.).

[0118] Generally, similar considerations apply if the same technical solution is achieved by using equivalent methods (by using similar steps with the same function in more steps or parts of it, removing some unnecessary steps, or adding more optional steps); in addition, these steps can be performed in different orders, simultaneously or in an alternating manner (at least partially).

[0119] One embodiment provides a computer program configured to cause the computing device to perform the methods described above when executed on a computing device. Another embodiment provides a computer program product comprising a computer-readable storage medium implementing the computer program, which is loaded into the working memory of the computing device to configure the computing device to perform the same methods. However, the computer program can be implemented as a standalone module, as a plug-in to a pre-existing software program (e.g., an image manager), or even directly within the latter. Similar considerations apply in any case if the structure of the computer program differs, or if additional modules or functionalities are provided; similarly, the storage structure can be of other types, or can be replaced by an equivalent entity (not necessarily composed of a physical storage medium). The computer program can take any form suitable for use by any computing device (see below) to configure the computing device to perform desired operations; in particular, the computer program can be in the form of external or resident software, firmware, or microcode (in object code or source code—e.g., to be compiled or interpreted). Furthermore, the computer program can be provided on any computer-readable storage medium. A storage medium is any tangible medium (unlike a transient signal itself) that can retain and store instructions used by the computing device. For example, the storage medium can be electrical, magnetic, optical, electromagnetic, infrared, or semiconductor; examples of such storage media are fixed disks (which may be pre-loaded with programs), removable disks, storage keys (e.g., USB), etc. Computer programs can be downloaded from the storage medium or via a network (e.g., the Internet, WAN, and / or LAN, including transmission cables, fiber optics, wireless connections, network devices); one or more network adapters in the computing device receive the computer program from the network and forward it to one or more storage devices of the computing device for storage. In any case, the technical solutions according to embodiments of this disclosure enable implementation, either by hardware structures (e.g., by electronic circuits integrated in one or more semiconductor material chips) or by a combination of appropriately programmed or otherwise configured software and hardware.

[0120] One embodiment provides a system including means configured to perform the steps of the method described above. Another embodiment provides a system including circuitry (i.e., any hardware appropriately configured by software) for performing each step of the same method. However, the system can be of any type (e.g., an imaging system, microscope, flexible fiber-based endoscope (fiber endoscope), rigid fiber endoscope, flexible endoscope characterized by distal image digitization (video endoscope), rigid laparoscope based on rod lenses, etc.), which may include any computing device (e.g., any integrated central unit, any standalone computer, etc.), any illumination device (e.g., laser-based, LED, UV lamp / halogen lamp / xenon lamp, etc.), any acquisition device (e.g., based on any number and type of lenses, waveguides, mirrors, CCD, ICCD, EMCCD, CMOS, InGaAs, or PMT sensors, etc.), and any output device (e.g., monitor, printer, network connection, head-mounted video projection device, etc.).

[0121] Generally, similar considerations apply if the system has a different structure or contains equivalent components, or if it has other operational characteristics. In any case, each component can be separated into more elements, or two or more components can be combined together to form a single element; furthermore, each component can be replicated to support the parallel execution of corresponding operations. Moreover, unless otherwise stated, any interaction between different components generally does not need to be sequential, and it can be direct or indirect through one or more intermediaries.

[0122] One embodiment provides a diagnostic method comprising the following steps: Imaging a patient's body part according to the method described above to output corresponding imaging results; Assessing the health status of the body part based on the imaging results. However, the proposed method can be found in any type of diagnostic application in the broadest sense of the term (e.g., in vivo / in vitro, aimed at discovering new lesions, monitoring known lesions, evaluating resected tissue for clinical decision-making, etc.) and can be used to analyze any type of body part of any patient (see above).

[0123] One embodiment provides a treatment method comprising the following steps: Imaging a patient's body part according to the method described above to output a corresponding imaging result; and treating the body part based on the imaging result. However, the proposed method can be found in any type of treatment method in the broadest sense of the term (e.g., aimed at curing a pathological condition, preventing its development, preventing the occurrence of a pathological condition, or simply improving patient comfort) and can be used on any type of body part of any patient (see above).

[0124] One embodiment provides a surgical method comprising the following steps: Imaging a patient's body part according to the method described above to output a corresponding imaging result; and manipulating the body part according to the imaging result. However, the proposed method can be applied to any type of surgical procedure (e.g., for therapeutic, preventative, cosmetic, etc.) in the broadest sense of the term, and can be used on any type of body part of any patient (see above).

[0125] In one embodiment, the diagnostic, therapeutic, and / or surgical method includes administering a luminescent agent to the patient. However, the luminescent agent may be administered in any manner (see above), or this step may be omitted entirely (if the luminescent agent is inherent).

Claims

1. A method (500) for imaging an object containing a luminescent material, wherein the method (500) comprises: The computing device (120) initializes (504-512) a combined image based on the initial imaging of the object or default values. This combined image includes multiple image values ​​representing the corresponding locations of the object. The refinement loop is repeated by the computing device (120), and the refinement loop includes: The computing device (120) segments the combined image into multiple parts representing corresponding regions of the object based on the image values ​​of the combined image according to the segmentation criteria. The computing device (120) determines multiple spatial patterns (518, 524; 538-540, 546) based on the segmentation of the combined image. Multiple local illuminations (520, 526; 542, 548) corresponding to a spatial pattern are continuously applied to the object by the illumination device (125-135). The acquiring device (140-155) continuously acquires corresponding component images (522, 528; 544, 550) in response to local illumination. Each component image includes a corresponding image value representing the light emitted by the luminescent material from the object's location, and The computing device (120) combines the component images (530, 552) into a new version of the combined image. Until the exit condition of the refinement loop is met at (532-534; 554-556), and The output device (195) outputs (562-566) the imaging results based on the combined image of the last version at the exit of the refinement cycle.

2. The method (500) according to claim 1, wherein the luminescent material is a fluorescent material.

3. The method (500) according to claim 1 or 2, wherein the method (500) comprises: Irradiation is initiated by applying (504-506) to the target using an irradiation device (125-135). The acquiring device (140-155) acquires (508) a starting image in response to initial illumination. The starting image includes corresponding image values ​​representing the light emitted by the luminescent material from the position of the object, and The computing device (120) initializes (512) the combined image based on the initial image.

4. The method (500) according to claim 3, wherein the method (500) comprises: The object is initially irradiated by applying (504-506) irradiation with irradiation equipment (125-135) to provide it with full irradiation.

5. The method (500) according to claim 3, wherein the method (500) comprises: The combined image is segmented (516) by the computing device (120); 536) is at least one detection portion representing a detection region in which a luminescent agent is detected, and at least one non-detection portion representing a non-detection region in which a luminescent agent is not detected.

6. The method (500) according to claim 5, wherein the method (500) comprises: The computing device (120) determines (518) a main spatial pattern for providing full illumination of the at least one detection area and non-illumination of the at least one non-detection area, and A secondary spatial pattern is determined by the computing device (120) (524) for providing non-irradiation of the at least one detection area and full irradiation of the at least one non-detection area.

7. The method (500) according to claim 6, wherein the method (500) comprises: The computing device (120) combines (530) the component images into a combined image by accumulating the corresponding image values ​​of the component images to each image value of the combined image.

8. The method (500) according to claim 7, wherein the method (500) comprises: The computing device (120) combines (530) the component images into a combined image by setting each image value of the combined image according to the sum of the corresponding image values ​​of the component images.

9. The method (500) according to claim 5, wherein the method (500) comprises: The computing device (120) determines (538-540) a main spatial pattern for providing general illumination distributed in the at least one detection area, and The computing device (120) determines (546) a secondary spatial pattern for providing general illumination distributed in the at least one non-detection area.

10. The method (500) according to claim 9, wherein the method (500) comprises: The computing device (120) determines (538-540) a general non-illuminated main spatial pattern distributed in the at least one non-detection area, and The computing device (120) determines (546) a general non-irradiated secondary spatial pattern distributed in the at least one detection area.

11. The method (500) of claim 10, wherein the general illumination provides illumination randomly distributed at a high spatial frequency, and the general non-illumination provides illumination randomly distributed at a low spatial frequency lower than the high spatial frequency.

12. The method (500) according to claim 11, wherein the method (500) comprises: The computing device (120) initializes (538) at least one of the following: providing full illumination to each region in the area to be universally illuminated and providing a non-illuminated spatial pattern to each region in the area to be universally non-illuminated; and The computing device (120) adds pseudo-random illumination noise (540) to each initialized spatial pattern.

13. The method (500) according to claim 9, wherein the method (500) comprises: The computing device (120) combines (552) the component images into a combined image by taking the difference between the corresponding image values ​​of the component images generated by the main spatial pattern and the corresponding image values ​​of the component images generated by the secondary spatial pattern as each image value of the combined image.

14. The method (500) according to claim 13, wherein the method (500) comprises: The computing device (120) combines (552) the component images into a combined image by setting each image value of the combined image according to the difference between the corresponding image value of the component image generated by the primary spatial pattern and the corresponding image value of the component image generated by the secondary spatial pattern.

15. The method (500) according to claim 9, wherein the method (500) comprises: The computing device (120) reverses (558-560) the final version of the combined image by setting each image value of the final version of the combined image according to the difference between the corresponding image value of the starting image and the corresponding image value of the final version of the combined image.

16. The method (500) of claim 1 or 2, wherein the spatial pattern combination of each iteration of the refinement cycle is used to provide full illumination of the object.

17. The method (500) according to claim 1 or 2, wherein the method (500) comprises: The computing device (120) verifies the exit conditions (532-534, 554-556) based on the combined image.

18. The method (500) according to claim 17, wherein the method (500) comprises: The exit condition (532-534, 554-556) is verified by the computing device (120) based on the similarity of the combined image of the current version generated by the current iteration of the refinement cycle, which is different from the first iteration of the refinement cycle, to at least one previous version of the combined image generated by the previous iteration of the refinement cycle.

19. The method (500) according to claim 1 or 2, wherein, The object in question is a part of the patient's body.

20. The method (500) of claim 19, wherein the luminescent agent has been pre-applied to the body part prior to performing the method.

21. A method for imaging an object containing a luminescent material, wherein the method comprises: The refinement loop is repeated by the computing device, and the refinement loop includes: Multiple spatial patterns are determined by a computing device. These spatial patterns are predefined in the first iteration of a refinement cycle and further refined in each subsequent iteration of the refinement cycle by applying preset modifications to their previous versions. The irradiation device continuously applies multiple local irradiations corresponding to the spatial pattern to the object. The acquiring device continuously acquires corresponding component images in response to local illumination. Each component image includes multiple image values ​​representing the light emitted by the luminescent material from the corresponding position of the object, and The computing device combines the component images into a new version of the combined image, which includes corresponding image values ​​representing the light emitted from the object's location by the luminescent material. Until the exit condition of the refinement loop is met, and The output device outputs the imaging result based on the combined image of the last version at the exit of the refinement cycle.

22. A computer program (400) configured to cause the computing device (120) to perform the method (500) according to any one of claims 1 to 21 when the computer program (400) is executed on the computing device (120).

23. A computer program product comprising a computer-readable storage medium implementing a computer program, the computer program being loaded into the working memory of a computing device to configure the computing device to perform the method according to any one of claims 1 to 21.

24. A system (120) for imaging an object containing a luminescent material, comprising means (400) configured to perform the steps of the method (500) according to any one of claims 1 to 21.

25. A system for imaging an object containing a luminescent material, comprising circuitry configured to perform each step of the method according to any one of claims 1 to 21.

26. A diagnostic method, comprising: The method according to any one of claims 1 to 21 images a patient's body parts to output imaging results, and Assess the health status of body parts based on imaging results.

Images