Publications by authors named "Joan C Vilanova"

60 Publications

The challenge of prostate biopsy guidance in the era of mpMRI detected lesion: ultrasound-guided versus in-bore biopsy.

Br J Radiol 2021 Jul 29:20210363. Epub 2021 Jul 29.

Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.

The current recommendation in patients with a clinical suspicion for prostate cancer is to perform systematic biopsies extended with targeted biopsies, depending on mpMRI results. Following a positive mpMRI [i.e. Prostate Imaging Reporting and Data System (PI-RADS) ≥3], three targeted biopsy approaches can be performed: visual registration of the MRI images with real-time ultrasound imaging; software-assisted fusion of the MRI images and real-time ultrasound images, and in-bore biopsy within the MR scanner. This collaborative review discusses the advantages and disadvantages of each targeting approach and elaborates on future developments. Cancer detection rates seem to mostly depend on practitioner experience and selection criteria (biopsy naïve, previous negative biopsy, prostate-specific antigen (PSA) selection criteria, presence of a lesion on MRI), and to a lesser extent dependent on biopsy technique. There is no clear consensus on the optimal targeting approach. The choice of technique depends on local experience and availability of equipment, individual patient characteristics, and onsite cost-benefit analysis. Innovations in imaging techniques and software-based algorithms may lead to further improvements in this field.
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http://dx.doi.org/10.1259/bjr.20210363DOI Listing
July 2021

Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm.

J Magn Reson Imaging 2021 Jun 16. Epub 2021 Jun 16.

Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain.

Background: Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipelines. However, these methods tend to reduce their performance in unseen magnetic resonance imaging (MRI) scanner vendors and different imaging protocols.

Purpose: To present and evaluate for clinical use PARIETAL, a pre-trained deep learning brain extraction method. We compare its reproducibility in a scan/rescan analysis and its robustness among scanners of different manufacturers.

Study Type: Retrospective.

Population: Twenty-one subjects (12 women) with age range 22-48 years acquired using three different MRI scanner machines including scan/rescan in each of them.

Field Strength/sequence: T1-weighted images acquired in a 3-T Siemens with magnetization prepared rapid gradient-echo sequence and two 1.5 T scanners, Philips and GE, with spin-echo and spoiled gradient-recalled (SPGR) sequences, respectively.

Assessment: Analysis of the intracranial cavity volumes obtained for each subject on the three different scanners and the scan/rescan acquisitions.

Statistical Tests: Parametric permutation tests of the differences in volumes to rank and statistically evaluate the performance of PARIETAL compared to state-of-the-art methods.

Results: The mean absolute intracranial volume differences obtained by PARIETAL in the scan/rescan analysis were 1.88 mL, 3.91 mL, and 4.71 mL for Siemens, GE, and Philips scanners, respectively. PARIETAL was the best-ranked method on Siemens and GE scanners, while decreasing to Rank 2 on the Philips images. Intracranial differences for the same subject between scanners were 5.46 mL, 27.16 mL, and 30.44 mL for GE/Philips, Siemens/Philips, and Siemens/GE comparison, respectively. The permutation tests revealed that PARIETAL was always in Rank 1, obtaining the most similar volumetric results between scanners.

Data Conclusion: PARIETAL accurately segments the brain and it generalizes to images acquired at different sites without the need of training or fine-tuning it again. PARIETAL is publicly available.

Level Of Evidence: 2 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27776DOI Listing
June 2021

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

Cancers (Basel) 2021 May 27;13(11). Epub 2021 May 27.

Clínica Girona, Institute Catalan of Health-IDI, University of Girona, 17004 Girona, Spain.

Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data.

Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings.

Results: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models ( < 0.001 for all).

Conclusions: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.
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http://dx.doi.org/10.3390/cancers13112627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198352PMC
May 2021

Obesity-associated deficits in inhibitory control are phenocopied to mice through gut microbiota changes in one-carbon and aromatic amino acids metabolic pathways.

Gut 2021 Jan 29. Epub 2021 Jan 29.

Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Girona, Spain

Background: Inhibitory control (IC) is critical to keep long-term goals in everyday life. Bidirectional relationships between IC deficits and obesity are behind unhealthy eating and physical exercise habits.

Methods: We studied gut microbiome composition and functionality, and plasma and faecal metabolomics in association with cognitive tests evaluating inhibitory control (Stroop test) and brain structure in a discovery (n=156), both cross-sectionally and longitudinally, and in an independent replication cohort (n=970). Faecal microbiota transplantation (FMT) in mice evaluated the impact on reversal learning and medial prefrontal cortex (mPFC) transcriptomics.

Results: An interplay among IC, brain structure (in humans) and mPFC transcriptomics (in mice), plasma/faecal metabolomics and the gut metagenome was found. Obesity-dependent alterations in one-carbon metabolism, tryptophan and histidine pathways were associated with IC in the two independent cohorts. Bacterial functions linked to one-carbon metabolism ( exodeoxyribonuclease V), and the anterior cingulate cortex volume were associated with IC, cross-sectionally and longitudinally. FMT from individuals with obesity led to alterations in mice reversal learning. In an independent FMT experiment, human donor's bacterial functions related to IC deficits were associated with mPFC expression of one-carbon metabolism-related genes of recipient's mice.

Conclusion: These results highlight the importance of targeting obesity-related impulsive behaviour through the induction of gut microbiota shifts.
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http://dx.doi.org/10.1136/gutjnl-2020-323371DOI Listing
January 2021

Whole-Brain Dynamics in Aging: Disruptions in Functional Connectivity and the Role of the Rich Club.

Cereb Cortex 2021 03;31(5):2466-2481

Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50-64 years versus older group (n = 310); age range, 65-91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local-global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain.
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http://dx.doi.org/10.1093/cercor/bhaa367DOI Listing
March 2021

Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics.

Radiographics 2020 Nov-Dec;40(7):1987-2010. Epub 2020 Oct 9.

From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.).

Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. RSNA, 2020.
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http://dx.doi.org/10.1148/rg.2020200070DOI Listing
July 2021

A new dose quantity for evaluation and optimisation of MV photon dose distributions when using advanced algorithms: proof of concept and potential applications.

Phys Med Biol 2020 11 27;65(23):235020. Epub 2020 Nov 27.

Medical Physics and Radiation Protection Department, Institut Català d'Oncologia, Girona, Spain.

Advanced algorithms used in MV photon radiotherapy model radiation transport in any media. They represent a step forward but introduce new uncertainties and questions, including whether to report the doses to water (D) or medium (D) voxels, and the impact of fluence changes introduced by surrounding media. These aspects can compromise consistency between both reporting modes and with previous algorithms in which clinical experience is based. This study introduces a new dose quantity, the dose-to-reference-like medium, to overcome the aforementioned shortcomings. It is linked to a reference medium, water in this study (D), and defined as the absorbed dose in a voxel of this reference medium surrounded by a reference-like medium with the same radiation transport characteristics as the original one. We propose to derive D distributions by post-processing D or D applying a correction factor (CF) to each voxel which depends on its composition. We present and justify a simple and straightforward method to obtain CFs that only involves two phantoms with the same density: one with the considered composition and the other with that of the reference medium. A proof of concept was performed in a clinical environment for Acuros XB (AXB) advanced algorithm and 6 MV photon beams. The CFs were derived for the tissues characterised in Acuros. D was compared to D, D, and D from AAA analytical algorithm for some virtual and clinical cases. All the previous quantities presented limitations that can be solved by D. This new quantity allows the applicability of evaluation parameters, traceability to clinical experience, and isolation of heterogeneity effects to identify optimum plans, offering useful characteristics for plan evaluation and optimisation in clinical practice. Additionally, it also has potential applications in automated treatment planning and multi-centre activities such as clinical trials, audits, benchmarking, and shared models for automation.
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http://dx.doi.org/10.1088/1361-6560/abb6bcDOI Listing
November 2020

Robotic-assisted transrectal MRI-guided biopsy. Technical feasibility and role in the current diagnosis of prostate cancer: an initial single-center experience.

Abdom Radiol (NY) 2020 12 23;45(12):4150-4159. Epub 2020 Jul 23.

Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003, Girona, Spain.

Objectives: To evaluate the potential clinical and technical utility to manage in practice the use of a robotic MRI in-bore-targeted prostate biopsies in the current work-up of prostate cancer diagnosis.

Methods: Thirty patients with a single cancer suspicious lesion interpreted on MRI using PI-RADSv2.1 category ≥ 3 underwent in-bore robotic transrectal MRI remote-controlled-guided biopsy. It was analyzed the technical success, clinical details, biopsy findings in correlation with the MRI examination, complications and cancer detection rate (CDR).

Results: The overall CDR for any cancer was 73% (22/30). It was 86% (19/22) for significant tumors (Gleason score of more than 6 or maximum cancer core length greater than 3 mm for Gleason 6) and 77% (17/22) for tumors with Gleason > 6. CDR for biopsy-naïve patients was 89% (16/18) and 50% (6/12) for patients with prior negative transrectal ultrasound-guided biopsies. The CDR for PI-RADS > 3 was 92% (22/24). All the lesions (n = 30) were reachable with the robotic MRI device. A self-limited rectal hemorrhagic complication was reported.

Conclusion: This initial data show that a robotic MRI-guided biopsy could be useful, efficient and feasible procedure in the new paradigm to diagnose significant prostate cancer in selected patients.
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http://dx.doi.org/10.1007/s00261-020-02665-6DOI Listing
December 2020

The Aging Imageomics Study: rationale, design and baseline characteristics of the study population.

Mech Ageing Dev 2020 07 11;189:111257. Epub 2020 May 11.

Canon Medical Systems Europe, Zoetermeer, The Netherlands.

Biomarkers of aging are urgently needed to identify individuals at high risk of developing age-associated disease or disability. Growing evidence from population-based studies points to whole-body magnetic resonance imaging's (MRI) enormous potential for quantifying subclinical disease burden and for assessing changes that occur with aging in all organ systems. The Aging Imageomics Study aims to identify biomarkers of human aging by analyzing imaging, biopsychosocial, cardiovascular, metabolomic, lipidomic, and microbiome variables. This study recruited 1030 participants aged ≥50 years (mean 67, range 50-96 years) that underwent structural and functional MRI to evaluate the brain, large blood vessels, heart, abdominal organs, fat, spine, musculoskeletal system and ultrasonography to assess carotid intima-media thickness and plaques. Patients were notified of incidental findings detected by a certified radiologist when necessary. Extensive data were also collected on anthropometrics, demographics, health history, neuropsychology, employment, income, family status, exposure to air pollution and cardiovascular status. In addition, several types of samples were gathered to allow for microbiome, metabolomic and lipidomic profiling. Using big data techniques to analyze all the data points from biological phenotyping together with health records and lifestyle measures, we aim to cultivate a deeper understanding about various biological factors (and combinations thereof) that underlie healthy and unhealthy aging.
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http://dx.doi.org/10.1016/j.mad.2020.111257DOI Listing
July 2020

Complete puborectalis, puboperinealis muscle and urethral rhabdomyosphincter preservation in laparoscopic radical prostatectomy: Anatomical landmarks to achieve early urinary continence.

Int J Urol 2020 Jun 16;27(6):525-536. Epub 2020 Apr 16.

Department of Urology, Moises Broggi Hospital, Barcelona, Spain.

Objectives: To describe our surgical technique of "muscle-sparing" laparoscopic radical prostatectomy and to review relevant anatomical landmarks during the procedure.

Methods: This was a prospective non-controlled case series of 120 consecutive patients who underwent laparoscopic radical prostatectomy, always carried out by the same surgeon (OL). The median follow-up period was 33 months. Dissection of the puboperinealis and puborectalis muscle consists of the precise dissection of the puborectalis and puboperinealis muscles from the periprostatic fascia. Rhabdomyo-dissection consists of an approach that spares the external urethral sphincter from the ventral surface of the prostate and membranous urethra. Clinical data were collected in a dedicated database. Intraoperative variables, postoperative complications and outcomes of urinary continence were assessed. A descriptive statistical analysis was carried out.

Results: Continence rates were 70.8%, 83.3% and 92.5%, at 0-2, 3-4 and 5-8 weeks after removal of the urethral catheter, respectively; 96.6% and 98.3% at 6 and 12 months after surgery. The positive surgical margin rate associated with rhabdomyo-dissection was 8.3%.

Conclusions: Laparoscopic radical prostatectomy with dissection of the puboperinealis and puborectalis muscle, and rhabdomyo-dissection is an oncologically safe procedure, associated with very early recovery urinary continence in most patients. It is a technique that can be applied in most cases, as long as there is no invasion of the ventral side of the prostate.
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http://dx.doi.org/10.1111/iju.14228DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384200PMC
June 2020

A Novel Device for Deep-Inspiration Breath Hold (DIBH): Results from a Single-Institution Phase 2 Clinical Trial for Patients with Left-Sided Breast Cancer.

Pract Radiat Oncol 2020 Jul - Aug;10(4):e290-e297. Epub 2020 Feb 14.

Department of Radiology, Clínica Girona, Institut de Diagnòstic per la Imatge, Girona, Spain; Department of Medical Sciences, University of Girona, Girona, Spain.

Purpose: To validate a novel device developed at our institution for deep inspiration breath hold (DIBH) within a phase 2 clinical trial for left-sided breast cancer and to evaluate the dosimetric benefits of its use.

Methods And Materials: The device uses an external mechanical reference for guiding the patient to the desired breath level and gives acoustic and visual feedback to the patient and the radiation therapists, respectively. A phase 2 clinical trial was performed for its validation. The thoracic amplitude was used as a surrogate of the inspiration level. The stability, repeatability, reproducibility, and reliability of DIBH using the device were analyzed. The dosimetric parameters of the heart, the left anterior descending coronary artery, the ipsilateral lung, the contralateral breast, and the target coverage using free breathing and DIBH were compared.

Results: Thirty-eight patients were included in the analysis. The maximum population value of stability and repeatability were 1.7 mm and 3.3 mm, respectively. The reproducibility mean value was 1.7 mm, and population systematic and random errors were 0.3 mm and 0.9 mm, respectively. The reliability was 98.9%. Statistically significant dose reductions were found for the heart, the left anterior descending coronary artery, and the ipsilateral lung dosimetric parameters in DIBH, without losing dose coverage to the planning target volumes.

Conclusions: The validation of the device within the phase 2 clinical trial demonstrates that it offers reliable, stable, repeatable, and reproducible breast cancer treatments in DIBH with its dosimetric benefits.
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http://dx.doi.org/10.1016/j.prro.2020.02.004DOI Listing
March 2021

Radiologic Clues to Cerebral Venous Thrombosis.

Radiographics 2019 Oct;39(6):1611-1628

From the Department of Radiology, Complexo Hospitalario Universitario de Santiago de Compostela, Choupana s/n, 15701 Santiago de Compostela, A Coruña, Spain (M.C.A., S.B.G., A.J.M., J.C.M., R.G.F.); Department of Radiology, Hospital Clínic de Barcelona, Barcelona, Spain (L.O.); Department of Radiology, Institut Català de la Salut (IDI), Girona, Spain (J.C.V.); Clínica Girona, Girona, Spain (J.C.V.); Clínica Las Nieves, Health Time, Jaén, Spain (A.L.A.); and Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.).

Cerebral venous thrombosis (CVT) is uncommon, representing approximately 0.5% of all cases of cerebrovascular disease worldwide. Many factors, alone or combined, can cause CVT. Although CVT can occur at any age, it most commonly affects neonates and young adults. CVT is difficult to diagnose clinically because patients can present with a wide spectrum of nonspecific manifestations, the most common of which are headache in 89%-91%, focal deficits in 52%-68%, and seizures in 39%-44% of patients. Consequently, imaging is fundamental to its diagnosis. MRI is the most sensitive and specific technique for diagnosis of CVT. The different MRI sequences, with and without the use of contrast material, have variable strengths. Contrast material-enhanced MR venography has the highest accuracy compared with sequences without contrast enhancement.RSNA, 2019.
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http://dx.doi.org/10.1148/rg.2019190015DOI Listing
October 2019

Functional MRI for evaluation of hyaline cartilage extracelullar matrix, a physiopathological-based approach.

Br J Radiol 2019 Nov 23;92(1103):20190443. Epub 2019 Aug 23.

MRI unit, Radiology department, Health Time, Jaén, Spain.

MRI of articular cartilage (AC) integrity has potential to become a biomarker for osteoarthritis progression. Traditional MRI sequences evaluate AC morphology, allowing for the measurement of thickness and its change over time. In the last two decades, more advanced, dedicated MRI cartilage sequences have been developed aiming to assess AC matrix composition non-invasively and detect early changes in cartilage not captured on morphological sequences. T2-mapping and T1ρ sequences can be used to estimate the relaxation times of water inside the AC. These sequences have been introduced into clinical protocols and show promising results for cartilage assessment. Extracelullar matrix can also be assessed using diffusion-weighted imaging and diffusion tensor imaging as the movement of water is limited by the presence of extracellular matrix in AC. Specific techniques for glycosaminoglycans (GAG) evaluation, such as delayed gadolinium enhanced MRI of cartilage or Chemical Exchange Saturation Transfer imaging of GAG, as well as sodium imaging have also shown utility in the detection of AC damage. This manuscript provides an educational update on the physical principles behind advanced AC MRI techniques as well as a comprehensive review of the strengths and weaknesses of each approach. Current clinical applications and potential future applications of these techniques are also discussed.
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http://dx.doi.org/10.1259/bjr.20190443DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849690PMC
November 2019

Update on Whole-body MRI in Musculoskeletal Applications.

Semin Musculoskelet Radiol 2019 Jun 4;23(3):312-323. Epub 2019 Jun 4.

Hospital Universitario de Bellvitge-IDIBELL, Barcelona, Spain.

Whole-body magnetic resonance imaging (WB-MRI) is a powerful tool increasingly used to assess oncologic and nononcologic diseases. WB-MRI provides information about diffuse multifocal pathologies with excellent anatomical definition through high soft tissue contrast and spatial resolution as well as valuable functional information from diffusion-weighted images. In addition to its roles in establishing the diagnosis and assessing the extent and severity of disease, WB-MRI is also useful for monitoring the response to treatment for malignant and benign systemic diseases affecting the musculoskeletal system. This article reviews and updates the applications of WB-MRI in current practice, discussing the role of this helpful tool in various conditions involving the musculoskeletal system including bone metastases, hematologic cancers, inflammatory processes, infections, and multisystemic-multifocal bone, nerve, vascular, and muscle/soft tissue disorders, as well as other idiopathic conditions.
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http://dx.doi.org/10.1055/s-0039-1685540DOI Listing
June 2019

How clinical imaging can assess cancer biology.

Insights Imaging 2019 Mar 4;10(1):28. Epub 2019 Mar 4.

Department of Radiology, Memorial Sloan-Kettering Cancer Center, Radiology, 1275 York Av. Radiology Academic Offices C-278, New York, NY, 10065, USA.

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment.
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http://dx.doi.org/10.1186/s13244-019-0703-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399375PMC
March 2019

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks.

Neuroimage Clin 2019 10;21:101638. Epub 2018 Dec 10.

Research institute of Computer Vision and Robotics, University of Girona, Spain.

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling.
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http://dx.doi.org/10.1016/j.nicl.2018.101638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413299PMC
December 2019

Dosimetric impact of Acuros XB dose-to-water and dose-to-medium reporting modes on VMAT planning for head and neck cancer.

Phys Med 2018 Nov 15;55:107-115. Epub 2018 Nov 15.

Department of Medical Sciences, University of Girona, C/Emili Grahit 77, 17003 Girona, Spain; Department of Radiology, Clínica Girona, Institut de Diagnòstic per la Imatge, C/Lorenzana 36, 17002 Girona, Spain. Electronic address:

Purpose: To assess the dosimetric impact of switching from the Analytical Anisotropic Algorithm (AAA) to Acuros XB (AXB) for both dose-to-medium (Dm) and dose-to-water (Dw) in VMAT for H&N patients. To study whether it should be linked to a change in the dose prescriptions to the PTVs and in the constraints to the OARs.

Methods: 110H&N patients treated with VMAT were included. Calculations were performed with AAA and AXB. PTV54, PTV60, PTV70, spinal cord, brainstem, brain, larynx, oral cavity, cochleas, parotid glands and mandible were delineated. Clinically-relevant dose-volume parameters were compared. Paired t-tests were used to analyze the differences in mean values. The Pitman-Morgan dispersion test was computed to evaluate inter-patient variability of these differences.

Results: AAA overestimated all dose-volume parameters compared to AXB Dm (0.2 Gy to 2.4 Gy). No systematic trend was observed in the differences between AAA and AXB Dw (-5.3 Gy to 0.6 Gy). Dose-volume parameters were significantly higher for AXB Dw compared to AXB Dm (0.1 Gy to 6.6 Gy). In all cases, the largest absolute differences (4%-14%) were found for maximum absorbed doses to the cochleas and the mandible. The number of parameters with significant inter-patient variability was greater when switching from AAA to AXB Dw than from AAA to AXB Dm.

Conclusions: There are important differences between AXB and AAA in VMAT planning for H&N cancer. The systematic differences and their inter-patient variability identified may help to facilitate decision-making about the dose prescriptions to the PTVs and the constraints to the OAR.
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http://dx.doi.org/10.1016/j.ejmp.2018.10.024DOI Listing
November 2018

Advanced Imaging Techniques in Evaluation of Colorectal Cancer.

Radiographics 2018 May-Jun;38(3):740-765. Epub 2018 Apr 20.

From the Departments of Radiology (R.G.F., S.B.G., I.O.V., A.M.d.A.) and Radiation Oncology (A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Health Time, Jaén, Spain (A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, IVO (Instituto Valenciano de Oncología), Valencia, Spain (A.M.); and Department of Radiology, Clínica Girona and IDI, Girona, Spain (J.C.V.).

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. RSNA, 2018.
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http://dx.doi.org/10.1148/rg.2018170044DOI Listing
October 2018

Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis.

Ultrason Imaging 2018 03 28;40(2):97-112. Epub 2017 Nov 28.

1 Computer Vision and Robotics Institute (VICOROB), University of Girona, Girona, Spain.

Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.
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http://dx.doi.org/10.1177/0161734617737733DOI Listing
March 2018

Advanced MR Imaging Techniques for Differentiation of Neuropathic Arthropathy and Osteomyelitis in the Diabetic Foot.

Radiographics 2017 Jul-Aug;37(4):1161-1180

From the MRI Section, Department of Radiology, SERCOSA, Health Time, Calle Carmelo Torres 2, 23007 Jaén, Spain (T.M.N., A.L.A.); Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio (A.L.A.); Department of Radiology, NYU Langone Medical Center, New York, NY (L.S.B.); MRI Section, Department of Radiology, DADISA, Health Time, Cádiz, Spain (M.G.C.); MRI Section, Department of Radiology, RESSALTA, Health Time, Córdoba, Spain (J.B.C.); and Department of Radiology, Clínica Girona, Catalan Institute of Health (IDI), University of Girona, Girona, Spain (J.C.V.).

Diagnosis and treatment of foot disease in patients with diabetes is a common clinical-radiologic challenge, particularly the differentiation of neuropathic arthropathy from osteomyelitis. Conventional clinical tests and imaging techniques have limited accuracy for evaluation of the diabetic foot. The introduction of morphologic magnetic resonance (MR) imaging in these patients has provided a qualitative leap in diagnosis. The characteristics of soft-tissue and bone marrow edema and their patterns of distribution throughout the foot allow discrimination between both entities. However, in certain scenarios, the application of MR imaging to this problem is limited because of overlapping features between the two and the coexistence of infection and neuropathic changes. Recent technical advances in MR imaging sequences have increased the capability to add functional quantitative information to structural information. Diffusion-weighted imaging is useful to determine the presence and extension of osteomyelitis. Dynamic contrast-enhanced MR imaging may help to detect differences between the vascularization patterns of neuropathic arthropathy and osteomyelitis. MR angiography (with or without contrast material) is used in clinical practice to identify candidate distal vessels for revascularization. MR neurography, and especially diffusion-tensor imaging, provides quantitative information about neural damage. These new sequences may help in assessment of the different pathophysiologic conditions that occur in the diabetic foot. The physical basis of these techniques, their limitations, and their potential applications for diabetic foot assessment are detailed in this article. The introduction of advanced MR imaging multiparametric protocols, with the aim of enhancing the overall diagnostic accuracy of MR imaging, may help in treatment decision making and lead to improved patient outcomes. RSNA, 2017.
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http://dx.doi.org/10.1148/rg.2017160101DOI Listing
November 2017

Evaluating the effect of multiple sclerosis lesions on automatic brain structure segmentation.

Neuroimage Clin 2017 8;15:228-238. Epub 2017 May 8.

Institute of Computer Vision and Robotics, University of Girona, Ed. P-IV, Campus Montilivi, 17073 Girona, Spain.

In recent years, many automatic brain structure segmentation methods have been proposed. However, these methods are commonly tested with non-lesioned brains and the effect of lesions on their performance has not been evaluated. Here, we analyze the effect of multiple sclerosis (MS) lesions on three well-known automatic brain structure segmentation methods, namely, FreeSurfer, FIRST and multi-atlas fused by majority voting, which use learning-based, deformable and atlas-based strategies, respectively. To perform a quantitative analysis, 100 synthetic images of MS patients with a total of 2174 lesions are simulated on two public databases with available brain structure ground truth information (IBSR18 and MICCAI'12). The Dice similarity coefficient (DSC) differences and the volume differences between the healthy and the simulated images are calculated for the subcortical structures and the brainstem. We observe that the three strategies are affected when lesions are present. However, the effects of the lesions do not follow the same pattern; the lesions either make the segmentation method underperform or surprisingly augment the segmentation accuracy. The obtained results show that FreeSurfer is the method most affected by the presence of lesions, with DSC differences (generated - healthy) ranging from - 0.11 ± 0.54 to 9.65 ± 9.87, whereas FIRST tends to be the most robust method when lesions are present (- 2.40 ± 5.54 to 0.44 ± 0.94). Lesion location is not important for global strategies such as FreeSurfer or majority voting, where structure segmentation is affected wherever the lesions exist. On the other hand, FIRST is more affected when the lesions are overlaid or close to the structure of analysis. The most affected structure by the presence of lesions is the nucleus accumbens (from - 1.12 ± 2.53 to 1.32 ± 4.00 for the left hemisphere and from - 2.40 ± 5.54 to 9.65 ± 9.87 for the right hemisphere), whereas the structures that show less variation include the thalamus (from 0.03 ± 0.35 to 0.74 ± 0.89 and from - 0.48 ± 1.08 to - 0.04 ± 0.22) and the brainstem (from - 0.20 ± 0.38 to 1.03 ± 1.31). The three segmentation approaches are affected by the presence of MS lesions, which demonstrates that there exists a problem in the automatic segmentation methods of the deep gray matter (DGM) structures that has to be taken into account when using them as a tool to measure the disease progression.
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http://dx.doi.org/10.1016/j.nicl.2017.05.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430150PMC
March 2018

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

Neuroimage 2017 07 19;155:159-168. Epub 2017 Apr 19.

Research institute of Computer Vision and Robotics, University of Girona, Spain.

In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume.
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http://dx.doi.org/10.1016/j.neuroimage.2017.04.034DOI Listing
July 2017

Automated tissue segmentation of MR brain images in the presence of white matter lesions.

Med Image Anal 2017 01 30;35:446-457. Epub 2016 Aug 30.

Research institute of Computer Vision and Robotics, University of Girona, Ed. P-IV, Campus Montilivi, 17071 Girona, Spain.

Over the last few years, the increasing interest in brain tissue volume measurements on clinical settings has led to the development of a wide number of automated tissue segmentation methods. However, white matter lesions are known to reduce the performance of automated tissue segmentation methods, which requires manual annotation of the lesions and refilling them before segmentation, which is tedious and time-consuming. Here, we propose a new, fully automated T1-w/FLAIR tissue segmentation approach designed to deal with images in the presence of WM lesions. This approach integrates a robust partial volume tissue segmentation with WM outlier rejection and filling, combining intensity and probabilistic and morphological prior maps. We evaluate the performance of this method on the MRBrainS13 tissue segmentation challenge database, which contains images with vascular WM lesions, and also on a set of Multiple Sclerosis (MS) patient images. On both databases, we validate the performance of our method with other state-of-the-art techniques. On the MRBrainS13 data, the presented approach was at the time of submission the best ranked unsupervised intensity model method of the challenge (7th position) and clearly outperformed the other unsupervised pipelines such as FAST and SPM12. On MS data, the differences in tissue segmentation between the images segmented with our method and the same images where manual expert annotations were used to refill lesions on T1-w images before segmentation were lower or similar to the best state-of-the-art pipeline incorporating automated lesion segmentation and filling. Our results show that the proposed pipeline achieved very competitive results on both vascular and MS lesions. A public version of this approach is available to download for the neuro-imaging community.
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http://dx.doi.org/10.1016/j.media.2016.08.014DOI Listing
January 2017

Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling.

Neuroimage Clin 2015 28;9:640-7. Epub 2015 Oct 28.

Dept. of Computer Architecture and Technology, University of Girona, Spain.

Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-w Multiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here, we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated or manual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) and white matter (WM) using SPM8, and compared with the same images where expert lesion annotations were filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error in GM and WM volume on images of MS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error in GM and WM volume. However, the % of error was significantly lower when automatically estimated lesions were filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations.
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http://dx.doi.org/10.1016/j.nicl.2015.10.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4644250PMC
September 2016

Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer?

Diagn Interv Radiol 2016 Jan-Feb;22(1):75-89

Department of Radiology, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Spain.

Abnormal metabolism is a key tumor hallmark. Proton magnetic resonance spectroscopy (1H-MRS) allows measurement of metabolite concentration that can be utilized to characterize tumor metabolic changes. 1H-MRS measurements of specific metabolites have been implemented in the clinic. This article performs a systematic review of image acquisition and interpretation of 1H-MRS for cancer evaluation, evaluates its strengths and limitations, and correlates metabolite peaks at 1H-MRS with diagnostic and prognostic parameters of cancer in different tumor types.
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http://dx.doi.org/10.5152/dir.2015.15009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4712903PMC
February 2017

Soft Tissue Tumors in Adults: ESSR-Approved Guidelines for Diagnostic Imaging.

Semin Musculoskelet Radiol 2015 Dec 22;19(5):475-82. Epub 2015 Dec 22.

Department of Radiology, Leiden University Medical Center, the Netherlands.

Soft tissue sarcomas are rare, but early, accurate diagnosis with subsequent appropriate treatment is crucial for the clinical outcome. The ESSR guidelines are intended to help radiologists in their decision-making and support discussion among clinicians who deal with patients with suspected or proven soft tissue tumors. Potentially malignant lesions recognized by ultrasound should be referred for magnetic resonance imaging (MRI), which also serves as a preoperative local staging modality, with specific technical requirements and mandatory radiological report elements. Radiography may add information about matrix calcification and osseous involvement. Indeterminate lesions, or lesions in which therapy is dependent on histology results, should be biopsied. For biopsy, we strongly recommend referral to a specialist sarcoma center, where an interdisciplinary tumor group, with a specialized pathologist, radiologist, and the surgeon are involved. In sarcoma, a CT scan of the chest is mandatory. Additional staging modalities are entity-specific. There are no evidence-based recommendations for routine follow-up in surgically treated sarcomas. However, we would recommend regular follow-up with intervals dependent on tumor grade, for 10 years after the initial diagnosis.
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http://dx.doi.org/10.1055/s-0035-1569251DOI Listing
December 2015

Assessment of Musculoskeletal Malignancies with Functional MR Imaging.

Magn Reson Imaging Clin N Am 2016 Feb 26;24(1):239-259. Epub 2015 Sep 26.

Department of Radiology, Health Time, Carmelo Torres 2, Jaén 23006, Spain; Department of Radiology, University Hospitals of Cleveland, Case Western Reserve University, 11100 Euclid Ave, 44106 Cleveland, Ohio.

Functional MR imaging is the technique of choice to evaluate and manage malignant musculoskeletal masses. Advanced MR imaging sequences include chemical shift MR imaging, diffusion-weighted imaging with apparent diffusion coefficient mapping, MR spectroscopy imaging, and dynamic contrast-enhanced perfusion imaging. Functional MR imaging adds value to morphologic sequences in the detection, characterization, staging, and posttherapy assessment of malignant musculoskeletal malignancies. This article reviews the technical role of each functional sequence and their clinical applications to allow more confident decisions to be made. Multiparametric analysis of functional and anatomic MR sequences allows musculoskeletal tumors analysis to be improved.
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http://dx.doi.org/10.1016/j.mric.2015.08.006DOI Listing
February 2016

A toolbox for multiple sclerosis lesion segmentation.

Neuroradiology 2015 Oct 31;57(10):1031-43. Epub 2015 Jul 31.

Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain.

Introduction: Lesion segmentation plays an important role in the diagnosis and follow-up of multiple sclerosis (MS). This task is very time-consuming and subject to intra- and inter-rater variability. In this paper, we present a new tool for automated MS lesion segmentation using T1w and fluid-attenuated inversion recovery (FLAIR) images.

Methods: Our approach is based on two main steps, initial brain tissue segmentation according to the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) performed in T1w images, followed by a second step where the lesions are segmented as outliers to the normal apparent GM brain tissue on the FLAIR image.

Results: The tool has been validated using data from more than 100 MS patients acquired with different scanners and at different magnetic field strengths. Quantitative evaluation provided a better performance in terms of precision while maintaining similar results on sensitivity and Dice similarity measures compared with those of other approaches.

Conclusion: Our tool is implemented as a publicly available SPM8/12 extension that can be used by both the medical and research communities.
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http://dx.doi.org/10.1007/s00234-015-1552-2DOI Listing
October 2015

[Total body MRI in early detection of bone metastasis and its indication in comparison to bone scan and other imaging techniques].

Arch Esp Urol 2015 Apr;68(3):371-90

Área de Imagen de Abdomen. Clínica Las Nieves. SERCOSA. Grupo Health Time. Jaén. España.

Bone metastases are a recognized prognostic factor in patients with prostate cancer. Currently, Tc99 bone scan is the most frequently used imaging technique for their detection, showing a high sensitivity but a limited specificity. Thus, new morphological and mainly functional imaging techniques based on PET and MRI, or hybrid techniques such as PET-CT or PET-MRI have been introduced to improve metastases detection, estimation of total tumor load and for therapeutic monitoring. In this clinical scenario, total body MRI has arisen as a very promising technique in detection and therapeutic monitoring of bone metastases of prostate cancer, because it neither uses ionizing radiation nor needs the administration of contrast media. The incorporation of MR diffusion to the morphologic total body MRI protocols provides functional information, improving the sensitivity in oncological lesions detection in general and osteolytic bone metastases of PCa in particular. Its integration in protocols with morphological sequences and its quantification through ADC maps enables us to better understand metastatic bone disease patterns and their changes with different therapies. Total body D MRI enables the early classification of the response to treatment with evident advantages over other imaging techniques and the purely morphological approach with MRI. In any case, prospective and cost-effectiveness studies are necessary to establish the role of total-body D MRI in the management of patients with PCa.
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April 2015
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