Quantitative Statistical Methods for Image Quality Assessment

Quantitative measures of image quality and reliability are critical for both qualitative interpretation and quantitative analysis of medical images. While, in theory, it is possible to analyze reconstructed images by means of Monte Carlo simulations using a large number of noise realizations, the associated computational burden makes this approach impractical. Additionally, this approach is less meaningful in clinical scenarios, where multiple noise realizations are generally unavailable. The practical alternative is to compute closed-form analytical expressions for image quality measures. The objective of this paper is to review statistical analysis techniques that enable us to compute two key metrics: resolution (determined from the local impulse response) and covariance. The underlying methods include fixed-point approaches, which compute these metrics at a fixed point (the unique and stable solution) independent of the iterative algorithm employed, and iteration-based approaches, which yield results that are dependent on the algorithm, initialization, and number of iterations. We also explore extensions of some of these methods to a range of special contexts, including dynamic and motion-compensated image reconstruction. While most of the discussed techniques were developed for emission tomography, the general methods are extensible to other imaging modalities as well. In addition to enabling image characterization, these analysis techniques allow us to control and enhance imaging system performance. We review practical applications where performance improvement is achieved by applying these ideas to the contexts of both hardware (optimizing scanner design) and image reconstruction (designing regularization functions that produce uniform resolution or maximize task-specific figures of merit)

Postreconstruction filtering of 3D PET images by using weighted higher-order singular value decomposition

Additional file 1. Original 3D PET images data used in this work to generate the results

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Novel PET Systems and Image Reconstruction with Actively Controlled Geometry

Positron Emission Tomography (PET) provides in vivo measurement of imaging ligands that are labeled with positron emitting radionuclide. Since its invention, most PET scanners have been designed to have a group of gamma ray detectors arranged in a ring geometry, accommodating the whole patient body. Virtual Pinhole PET incorporates higher resolution detectors being placed close to the Region-of-Interest (ROI) within the imaging Field-of-View (FOV) of the whole-body scanner, providing better image resolution and contrast recover. To further adapt this technology to a wider range of diseases, we proposed a second generation of virtual pinhole PET using actively controlled high resolution detectors integrated on a robotic arm. When the whole system is integrated to a commercial PET scanner, we achieved positioning repeatability within 0.5 mm. Monte Carlo simulation shows that by focusing the high-resolution detectors to a specific organ of interest, we can achieve better resolution, sensitivity and contrast recovery. In another direction, we proposed a portable, versatile and low cost PET imaging system for Point-of-Care (POC) applications. It consists of one or more movable detectors in coincidence with a detector array behind a patient. The movable detectors make it possible for the operator to control the scanning trajectory freely to achieve optimal coverage and sensitivity for patient specific imaging tasks. Since this system does not require a conventional full ring geometry, it can be built portable and low cost for bed-side or intraoperative use. We developed a proof-of-principle prototype that consists of a compact high resolution silicon photomultiplier detector mounted on a hand-held probe and a half ring of conventional detectors. The probe is attached to a MicroScribe device, which tracks the location and orientation of the probe as it moves. We also performed Monte Carlo simulations for two POC PET geometries with Time-of-Flight (TOF) capability. To support the development of such PET systems with unconventional geometries, a fully 3D image reconstruction framework has been developed for PET systems with arbitrary geometry. For POC PET and the second generation robotic Virtual Pinhole PET, new challenges emerge and our targeted applications require more efficiently image reconstruction that provides imaging results in near real time. Inspired by the previous work, we developed a list mode GPU-based image reconstruction framework with the capability to model dynamically changing geometry. Ordered-Subset MAP-EM algorithm is implemented on multi-GPU platform to achieve fast reconstruction in the order of seconds per iteration, under practical data rate. We tested this using both experimental and simulation data, for whole body PET scanner and unconventional PET scanners. Future application of adaptive imaging requires near real time performance for large statistics, which requires additional acceleration of this framework

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion

Positron Emission Tomography: Current Challenges and Opportunities for Technological Advances in Clinical and Preclinical Imaging Systems

Positron emission tomography (PET) imaging is based on detecting two time-coincident high-energy photons from the emission of a positronemitting radioisotope. The physics of the emission, and the detection of the coincident photons, give PET imaging unique capabilities for both very high sensitivity and accurate estimation of the in vivo concentration of the radiotracer. PET imaging has been widely adopted as an important clinical modality for oncological, cardiovascular, and neurological applications. PET imaging has also become an important tool in preclinical studies, particularly for investigating murine models of disease and other small-animal models. However, there are several challenges to using PET imaging systems. These include the fundamental trade-offs between resolution and noise, the quantitative accuracy of the measurements, and integration with X-ray computed tomography and magnetic resonance imaging. In this article, we review how researchers and industry are addressing these challenges.This work was supported in part by National Institutes of Health grants R01-CA042593, U01-CA148131, R01CA160253, R01CA169072, and R01CA164371; by Human Frontier Science Program grant RGP0004/2013; and by the Innovative Medicines Initiative under grant agreement 115337, which comprises financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013

Multimodality Imaging in Prostate Cancer

ABSTRACT Prostate cancer is the most common cancer in men in Finland. Its aggressiveness varies widely, from indolent to fatal disease. Accurate characterization of prostate cancer is extremely essential to prevent overtreatment while sustaining good survivorship and high quality of life. This is feasible using novel technology in imaging and automatic tools in treatment planning. In the first part of this thesis work, the aim was to evaluate anti-1-amino-3-18Ffluorocyclobutane-1-carboxylic acid (18F-FACBC) PET/CT, PET/MRI, and multiparametric MRI (mpMRI) in detection of primary prostate cancer. The uptake of 18F-FACBC was significantly stronger in tumors with higher Gleason score and it may therefore assist in targeted biopsies when combined with MRI. 18F-FACBC PET/MRI outperformed PET/CT but did not demonstrate higher diagnostic performance than mpMRI performed separately. Furthermore, PET/MRI and mpMRI failed to detect pelvic lymph node metastasis measuring less than 8mm. 18F-FACBC PET/MRI is promising in characterization of primary prostate cancer, especially if ablative treatments are planned. It is not likely to replace mpMRI in clinical practice. The second study assessed multimodality imaging in detecting bone metastasis in high-risk prostate cancer and breast cancer patients. All patients underwent 99mTc-HDP bone scintigraphy (BS), 99mTc-HDP SPECT, 99mTc-HDP SPECT/CT, 18F-NaF PET/CT, and whole body (wb) MRI+DWI. 99mTc-HDP SPECT/CT, 18F-NaF PET/CT, and wbMRI+DWI had superior sensitivity compared to conventional nuclear imaging. In particular non-BS techniques showed less equivocal findings. wbMRI+DWI was as accurate as 18F-NaF PET/CT for detecting bone metastasis and may be considered a potential “single-step” imaging modality for detection of bone metastasis in high-risk patients with prostate and breast cancer. The purpose of the third study was to evaluate and validate the performance of a fully automated segmentation tool (AST) in MRI-based radiotherapy planning of prostate cancer. It showed high agreement for delineating prostate, bladder, and rectum, compared to manual contouring, and suggested adoption of AST in clinical practice. Finally, the fourth study investigated the long-term toxicity after biologically guided radiotherapy in men with localized prostate cancer. Carbon-11 acetate (11C-ACE) PET-CT was used to guide dose escalation into metabolically active intraprostatic lesions. 11C-ACE PET-guided radiotherapy was feasible and well tolerated. Although erectile dysfunction was relatively common, severe gastro-intestinal symptoms were very rare, and no grade 3 genitourinary symptoms were present at five years after radiotherapy. The findings of this thesis have potential to improve diagnostic imaging and radiotherapy planning in primary and metastatic prostate cancer. Eventually, they are likely to improve patients’ quality of life and survival. KEYWORDS: prostate cancer, magnetic resonance imaging, positron emission tomography, radiotherapy planning, toxicity, bone metastasisTIIVISTELMÄ Eturauhassyöpä on miesten yleisin syöpä Suomessa. Sen taudinkuva vaihtelee laajasti rauhallisesta aggressiiviseen ja tappavaan. On oleellista, että taudin luonne arvioidaan tarkasti, jotta vältytään sen liialliselta hoidolta, tinkimättä erinomaisista hoitotuloksista selviytymisessä ja elämän laadussa. Uudet kuvantamisteknologiat ja automaattityökalut mahdollistavat tämän. Tämän väitöskirjan ensimmäisessä osatyössä oli tavoitteena arvioida anti-1-amino-3-18Ffluorosyklobutaani-1-karboksyylihappo (18F-FACBC) PET-tietokonetomografiaa (TT), PET-magneettiresonanssikuvantamista (MRI) ja multiparametrista MRI-kuvantamista (mpMRI) eturauhassyövän diagnoosivaiheessa. 18F-FACBC-kertymät olivat tilastollisesti merkitsevästi voimakkaampia korkean Gleason-luokituksen kasvaimissa, joten yhdistettyä PET-MRI-kuvantamista voidaan käyttää hyväksi esimerkiksi kohdennetussa koepalojen otossa. 18F-FACBC PET-MRI oli parempi kuin PET-TT ja samanveroinen kuin mpMRI eturauhassyövän diagnostiikassa. PET-MRI ja mpMRI eivät havainneet alle 8 mm:n läpimittaisia imusolmukemetastaaseja. 18F-FACBC PET-MRI on lupaava kuvantamismuoto eturauhassyövän diagnostiikassa, erityisesti kajoavia hoitoja suunniteltaessa, mutta ei korvanne mpMRI:a kliinisessä käytössä. Toinen osatyö käsitteli luustoetäpesäkkeiden toteamista eri kuvantamismenetelmillä korkean uusiutumisriskin eturauhas- ja rintasyöpäpotilailla. Kaikille potilaille tehtiin 99mTc-HDP luustokarttakuvaus, 99mTc-HDP SPECT, 99mTc-HDP SPECT-TT, 18F-NaF PET-TT ja koko kehon MRI diffuusiopainotettuna (wbMRI+DWI). 99mTc-HDP SPECT-TT, 18F-NaF PET-TT ja wbMRI+DWI olivat perinteistä luustokarttaa herkempiä luustometastaasien toteamisessa, koska epäspesifeiksi määriteltyjä muutoksia oli vähemmän. wbMRI+DWI osoitti yhtäläistä tarkkuutta luustometastaasien diagnosoinnissa 18F-NaF PET-TT:n verrattuna, joten sitä voitaisiin hyödyntää, käytettäessä vain yhtä kuvantamistapaa näiden potilaiden luustometastaasien toteamiseen. Kolmas osatyö arvioi ja validoi täysin automaattisen piirtotyökalun käyttöä MRI-pohjaisen sädehoidon suunnittelussa eturauhassyöpäpotilailla. Työkalu suoriutui hyvin eturauhasen, virtsarakon ja peräsuolen rajauksesta asiantuntijan käsin tekemiin rajauksiin verrattuna, puoltaen työkalun käyttöä luotettavasti myös kliinisessä työssä. Viimeisenä, neljännessä osatyössä arvioitiin biologisesti ohjatun eturauhassyövän sädehoidon aiheuttamia pitkäaikaishaittoja. Hiili-11 asetaatti (11C-ACE) PET-TT-kuvantamisen avulla suunniteltiin sädehoito, jossa metabolisesti aktiivisiin eturauhasen sisäisiin muutoksiin kohdistettiin korkeammat sädeannokset. 11C-ACE-PET-TT-ohjattu sädehoito oli toteuttamiskelpoinen ja hyvin siedetty. Vaikka erektiohäiriöt olivat suhteellisen yleisiä, vakavat suoliston haittavaikutukset olivat hyvin harvinaisia, eikä kolmannen asteen virtsateiden haittavaikutuksia esiintynyt lainkaan viiden vuoden kuluttua sädehoidosta. Tämän väitöskirjan löydökset voivat parantaa eturauhassyövän primaaridiagnostiikan kuvantamista ja sädehoidon suunnittelua, sekä luustoetäpesäkkeiden diagnostiikkaa. Näin voidaan kohentaa potilaiden elämänlaatua ja selviytymistä. AVAINSANAT: Eturauhassyöpä, magneettikuvaus, positroniemissiotomografia, sädehoidon suunnittelu, haittavaikutukset, luuston etäpesäkkee