Movement correction by object recognition-based anatomical tracking in functional magnetic resonance urography (fMRU): Proof of principle

Breathing motion is a challenge to analysis of imaging time series in many settings, especially in thorax and abdomen. We investigated in a software phantom as proof of principle, whether object recognition based tracking is capable of intensity-time-curve analysis. Images-time-series (no respiratory gating) of 100 kidneys were artificially generated (Matlab, TheMathworks, Natick, NA, USA). Respiratory movement was implemented by a sinusoidal coordinate shift with an amplitude of 3 cm and frequency of about 6 min-1. Renal intensity changes after contrast application were modeled using gamma functions for three anatomical compartments: cortex, pyramids and renal pelvis. Movement correction was carried out for half of the study population. Intensity-time-curves were extracted using automatically placed regions of interest relative to central coordinates of the kidney on the first image. Intensity changes over time extracted from the ROIs were subtracted from known intensity changes of the software model. Differences were assessed using Wilcox-Signed-Rank test for 50 kidneys with and 50 without movement correction. We used Bonferroni method to correct for multiple testing. Mean sum of differences between predicted and observed intensities across all kidneys and compartments was 0,072 with and 7,3 without movement correction. Significant difference between observation and model was not seen in any compartments of the tracking group (mean z-score: -0.8), whereas there was in 66 compartments in the non-tracking group (mean z-score: -3.2), signifying good agreement between theoretical model and observed intensity changes with object recognition-based tracking, and suboptimal agreement in the non-tracking-group due to movement artifacts. We conclude that object-recognition based anatomical tracking is feasible in fMRU as an alternative or addition to respiration gating. This may allow a higher temporal resolution of these studies in the future

Wertigkeit der funktionellen MR-Urografie in der Beurteilung kongenitaler Anomalien von Niere und Harntrakt: retrospektive Datenanalyse zum Vergleich von funktioneller MR-Urografie und 99mTc-MAG3-Diureseszintigrafie bzw. Nierensonografie

Zur Funktionsdiagnostik bei Kindern mit kongenitalen Anomalien von Niere und Harntrakt (CAKUT) wird neben dem Goldstandard-Verfahren 99mTc-MAG3-Diureseszintigrafie derzeit die funktionelle MR-Urografie (fMRU) als kombiniert funktionell-morphologisches Untersuchungsverfahren etabliert. Ziel der Untersuchung ist die Evaluation der Wertigkeit der fMRU in der Beurteilung von CAKUT bezüglich seitengetrennter Nierenfunktion und Harnabfluss sowie Morphologie. Bei 112 pädiatrischen Patienten mit CAKUT des Universitätsklinikums Jena wurden die morphologischen Untersuchungsergebnisse von fMRU und Nierensonografie verglichen. In einer Untergruppe von 30 Patienten wurde ein Vergleich zwischen seitengetrennten Funktionsparametern von fMRU und Szintigrafie angestellt. Die fMRU ermöglichte bei allen 112 Patienten eine detaillierte anatomisch-morphologische Darstellung des gesamten Harntraktes, wobei sonografische Vorbefunde durch die fMRU bestätigt oder gar spezifiziert wurden. Bezüglich seitengetrennter Nierenfunktion und Harnabfluss ergaben sich zwischen fMRU und Szintigrafie statistisch noch Differenzen. In Übereinstimmung mit der aktuellen Studienlage konnte aufgezeigt werden, dass die fMRU eine adäquate Beurteilung von Funktion und Obstruktion ermöglicht. In der morphologischen Beurteilung ist sie sowohl der Sonografie als auch der Szintigrafie überlegen. Obwohl fMRU und Szintigrafie ähnliche funktionelle Parameter untersuchen, ist ein unmittelbarer statistischer Vergleich der ermittelten Werte nur begrenzt möglich, da methodisch grundlegende Unterschiede bestehen. Wenngleich die fMRU Nierensonografie und Szintigrafie in naher Zukunft nicht ersetzen wird, zeigt sie dennoch großes Potential als komplementäre Untersuchungsmethode. Vor allem in der Diagnostik komplexer kongenitaler Harntraktanomalien, bei uneindeutigen Sonografiebefunden und in der präoperativen Planung sollte sie perspektivisch in spezialisierten Zentren vermehrt eingesetzt werden

Kidney segmentation in 4-dimensional dynamic contrast- enhanced MR images : A physiological approach

Master'sMASTER OF ENGINEERIN

Healthy kidney segmentation in the dce-mr images using a convolutional neural network and temporal signal characteristics

Quantification of renal perfusion based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) requires determination of signal intensity time courses in the region of renal parenchyma. Thus, selection of voxels representing the kidney must be accomplished with special care and constitutes one of the major technical limitations which hampers wider usage of this technique as a standard clinical routine. Manual segmentation of renal compartments—even if performed by experts—is a common source of decreased repeatability and reproducibility. In this paper, we present a processing framework for the automatic kidney segmentation in DCE-MR images. The framework consists of two stages. Firstly, kidney masks are generated using a convolutional neural network. Then, mask voxels are classified to one of three regions—cortex, medulla, and pelvis–based on DCE-MRI signal intensity time courses. The proposed approach was evaluated on a cohort of 10 healthy volunteers who underwent the DCE-MRI examination. MRI scanning was repeated on two time events within a 10-day interval. For semantic segmentation task we employed a classic U-Net architecture, whereas experiments on voxel classification were performed using three alternative algorithms—support vector machines, logistic regression and extreme gradient boosting trees, among which SVM produced the most accurate results. Both segmentation and classification steps were accomplished by a series of models, each trained separately for a given subject using the data from other participants only. The mean achieved accuracy of the whole kidney segmentation was 94% in terms of IoU coefficient. Cortex, medulla and pelvis were segmented with IoU ranging from 90 to 93% depending on the tissue and body side. The results were also validated by comparing image-derived perfusion parameters with ground truth measurements of glomerular filtration rate (GFR). The repeatability of GFR calculation, as assessed by the coefficient of variation was determined at the level of 14.5 and 17.5% for the left and right kidney, respectively and it improved relative to manual segmentation. Reproduciblity, in turn, was evaluated by measuring agreement between image-derived and iohexol-based GFR values. The estimated absolute mean differences were equal to 9.4 and 12.9 mL/min/1.73 m2 for scanning sessions 1 and 2 and the proposed automated segmentation method. The result for session 2 was comparable with manual segmentation, whereas for session 1 reproducibility in the automatic pipeline was weaker.publishedVersio

Deep Learning Approaches Applied to Image Classification of Renal Tumors: A Systematic Review

Renal cancer is one of the ten most common cancers in the population that affects 65,000 new patients a year. Nowadays, to predict pathologies or classify tumors, deep learning (DL) methods are effective in addition to extracting high-performance features and dealing with segmentation tasks. This review has focused on the different studies related to the application of DL techniques for the detection or segmentation of renal tumors in patients. From the bibliographic search carried out, a total of 33 records were identified in Scopus, PubMed and Web of Science. The results derived from the systematic review give a detailed description of the research objectives, the types of images used for analysis, the data sets used, whether the database used is public or private, and the number of patients involved in the studies. The first paper where DL is applied compared to other types of tumors was in 2019 which is relatively recent. Public collection and sharing of data sets are of utmost importance to increase research in this field as many studies use private databases. We can conclude that future research will identify many benefits, such as unnecessary incisions for patients and more accurate diagnoses. As research in this field grows, the amount of open data is expected to increase.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This article is based upon work from COST Action HARMONISATION (CA20122). This research has been partially funded by the Spanish Government by the project PID2021-127275OB-I00, FEDER “Una manera de hacer Europa”

Deep Learning Approaches Applied to Image Classification of Renal Tumors: A Systematic Review

Renal cancer is one of the ten most common cancers in the population that affects 65,000 new patients a year. Nowadays, to predict pathologies or classify tumors, deep learning (DL) methods are effective in addition to extracting high-performance features and dealing with segmentation tasks. This review has focused on the different studies related to the application of DL techniques for the detection or segmentation of renal tumors in patients. From the bibliographic search carried out, a total of 33 records were identified in Scopus, PubMed and Web of Science. The results derived from the systematic review give a detailed description of the research objectives, the types of images used for analysis, the data sets used, whether the database used is public or private, and the number of patients involved in the studies. The first paper where DL is applied compared to other types of tumors was in 2019 which is relatively recent. Public collection and sharing of data sets are of utmost importance to increase research in this field as many studies use private databases. We can conclude that future research will identify many benefits, such as unnecessary incisions for patients and more accurate diagnoses. As research in this field grows, the amount of open data is expected to increase

Kidney targeting and puncturing during percutaneous nephrolithotomy: recent advances and future perspectives

Background and Purpose: Precise needle puncture of the kidney is a challenging and essential step for successful percutaneous nephrolithotomy (PCNL). Many devices and surgical techniques have been developed to easily achieve suitable renal access. This article presents a critical review to address the methodologies and techniques for conducting kidney targeting and the puncture step during PCNL. Based on this study, research paths are also provided for PCNL procedure improvement. METHODS: Most relevant works concerning PCNL puncture were identified by a search of Medline/PubMed, ISI Web of Science, and Scopus databases from 2007 to December 2012. Two authors independently reviewed the studies. RESULTS: A total of 911 abstracts and 346 full-text articles were assessed and discussed; 52 were included in this review as a summary of the main contributions to kidney targeting and puncturing. CONCLUSIONS: Multiple paths and technologic advances have been proposed in the field of urology and minimally invasive surgery to improve PCNL puncture. The most relevant contributions, however, have been provided by the application of medical imaging guidance, new surgical tools, motion tracking systems, robotics, and image processing and computer graphics. Despite the multiple research paths for PCNL puncture guidance, no widely acceptable solution has yet been reached, and it remains an active and challenging research field. Future developments should focus on real-time methods, robust and accurate algorithms, and radiation free imaging techniques.The authors acknowledge Foundation for Science and Technology (FCT) for the fellowships references: SFRH/BPD/46851/2008 and SFRH/BD/74276/2010

Assessment of morphological and functional properties of the genitourinary system using high resolution MRI

Ziel dieser Arbeit war die Untersuchung und Darstellbarkeit einzelner Kompartimente des Urogenitaltrakts mittels hochaufgelöster Magnetresonanztomografie (MRT). Im Kontext der Schließmuskelregeneration wurde mit Hilfe der MRT der urethrale Schließmuskel eines Tiermodells visualisiert, wodurch im zeitlichen Verlauf die Lokalisierung und Anpassungsfähigkeit des Muskelgewebes nach Injektion von markierten Stammzellen untersucht werden konnte. Hierfür wurde eine robuste, sensitive und nicht-invasive Methode angewendet, um ein essentielles Verständnis der biologischen Effekte im Sphinkter zu erhalten und somit neue zellbasierte Therapien zu entwickeln. Zur Untersuchung weiterer Kompartimente des Urogenitaltrakts wurden die renalen Strukturen Cortex, Medulla und Pelvis ohne die Verwendung von Kontrastmittel anhand hochauflösender MR-Methoden im Probanden evaluiert. Unter Zuhilfenahme optimierter MR-Sequenzen konnten die einzelnen Kompartimente klar strukturiert und durch einen selbstentwickelten automatischen Algorithmus segmentiert werden. Im Vergleich zur manuellen Segmentierung zeigten die berechneten Koeffizienten eine hohe Übereinstimmung zur automatischen Segmentierung der gesamten Nierenregion. Zusätzlich wurde durch den vorgestellten Algorithmus sowohl die Medulla als auch das Nierenbecken automatisch segmentiert. Bisher sind keine Ansätze aus der Literatur bekannt, die das Nierenbecken aus nativen MR-Bildern segmentierten und evaluierten. Die Kombination aus optimierten MR-Bildern, Bildregistrierung und automatischer Segmentierung liefert zuverlässige und wiederholbare Ergebnisse der Volumenbestimmung der gesamten Niere und der renalen Strukturen ohne Zuhilfenahme von Kontrastmittel. Bei einer möglichen Übertragung des entwickelten Algorithmus in die klinische Routine eröffnen sich neue nicht-invasive Möglichkeiten zur Bewertung und Überwachung morphologischer Veränderungen. Zur weiteren Anwendung wurden die segmentierten Areale auf entzerrungskorrigierte funktionelle Diffusionsdatensätze überlagert, um eine regionenbasierte Darstellung der fraktionellen Anisotropie (FA) und der mittleren Diffusivität (MD) zu erhalten. Die Durchführung der Verzerrungskorrektur wurde anhand der „reversed gradient“ Methode verwirklicht. Die erfolgreiche Verzerrungskorrektur konnte durch einen Vergleich der manuellen Segmentierung der MD Karten und den automatisch generierten Masken aus den Anatomiedatensätzen dargelegt werden. Die manuelle Segmentierung ist sehr zeitaufwändig und auf Grund der unscharfen Außenkontur der Niere in den MD Karten äußerst schwierig zu realisieren. Daher erbringt die Fusion von hochaufgelösten, anatomisch segmentierten Masken mit verzerrungskorrigierten funktionellen Daten Vorteile für eine zuverlässige Auswertung. Die berechneten funktionellen Werte zeigten eine gute Übereinstimmung mit Literaturwerten. Lediglich verringerte medullare FA-Werte sind auf die Tatsache zurückzuführen, dass die bisherigen Bewertungsmethoden nur Regionen aus den hellsten Bereichen der funktionellen Bilder mit einbezogen haben. Ein weiterer Vorteil des entwickelten Algorithmus ist somit eine schichtweise Quantifizierung der gesamten Nierenstrukturen, wobei lokale Nierenerkrankungen, wie Zysten oder eine partielle Nekrose, durch eine erweiterte Segmentierung mit in die Beurteilung einbezogen werden können. Die Verhältnisse der Volumina innerhalb der Niere, unter Berücksichtigung der Funktionalität der einzelnen Regionen, ermöglichen nun weitere Erkenntnisse in der Nierendiagnostik

A non-invasive diagnostic system for early assessment of acute renal transplant rejection.

Early diagnosis of acute renal transplant rejection (ARTR) is of immense importance for appropriate therapeutic treatment administration. Although the current diagnostic technique is based on renal biopsy, it is not preferred due to its invasiveness, recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. In this thesis, a computer-aided diagnostic (CAD) system for early detection of ARTR from 4D (3D + b-value) diffusion-weighted (DW) MRI data is developed. The CAD process starts from a 3D B-spline-based data alignment (to handle local deviations due to breathing and heart beat) and kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The latter is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and for on-going visual kidney-background appearances. A cumulative empirical distribution of apparent diffusion coefficient (ADC) at different b-values of the segmented DW-MRI is considered a discriminatory transplant status feature. Finally, a classifier based on deep learning of a non-negative constrained stacked auto-encoder is employed to distinguish between rejected and non-rejected renal transplants. In the “leave-one-subject-out” experiments on 53 subjects, 98% of the subjects were correctly classified (namely, 36 out of 37 rejected transplants and 16 out of 16 nonrejected ones). Additionally, a four-fold cross-validation experiment was performed, and an average accuracy of 96% was obtained. These experimental results hold promise of the proposed CAD system as a reliable non-invasive diagnostic tool

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd