diff --git a/_data/phantom_literature.json b/_data/phantom_literature.json index 6494388..e820d96 100644 --- a/_data/phantom_literature.json +++ b/_data/phantom_literature.json @@ -1,4 +1,184 @@ [ + { + "key": "HGBK9DZ2", + "version": 350, + "library": { + "type": "group", + "id": 2900833, + "name": "OSIPI TF3.1 Literature", + "links": { + "alternate": { + "href": "https://www.zotero.org/groups/osipi_tf3.1_literature", + "type": "text/html" + } + } + }, + "links": { + "self": { + "href": "https://api.zotero.org/groups/2900833/items/HGBK9DZ2", + "type": "application/json" + }, + "alternate": { + "href": "https://www.zotero.org/groups/osipi_tf3.1_literature/items/HGBK9DZ2", + "type": "text/html" + } + }, + "meta": { + "createdByUser": { + "id": 1405725, + "username": "aaronolivertaylor", + "name": "", + "links": { + "alternate": { + "href": "https://www.zotero.org/aaronolivertaylor", + "type": "text/html" + } + } + }, + "creatorSummary": "Brumer et al.", + "parsedDate": "2022-07-31", + "numChildren": 0 + }, + "data": { + "key": "HGBK9DZ2", + "version": 350, + "itemType": "journalArticle", + "title": "Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline", + "creators": [ + { + "creatorType": "author", + "firstName": "Irène", + "lastName": "Brumer" + }, + { + "creatorType": "author", + "firstName": "Dominik F.", + "lastName": "Bauer" + }, + { + "creatorType": "author", + "firstName": "Lothar R.", + "lastName": "Schad" + }, + { + "creatorType": "author", + "firstName": "Frank G.", + "lastName": "Zöllner" + } + ], + "abstractNote": "Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80–0.93, 0.78–0.89, and 0.64–0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.", + "publicationTitle": "Diagnostics", + "volume": "12", + "issue": "8", + "pages": "1854", + "date": "2022-07-31", + "series": "", + "seriesTitle": "", + "seriesText": "", + "journalAbbreviation": "Diagnostics", + "language": "en", + "DOI": "10.3390/diagnostics12081854", + "ISSN": "2075-4418", + "shortTitle": "", + "url": "https://www.mdpi.com/2075-4418/12/8/1854", + "accessDate": "2023-08-22T14:04:47Z", + "archive": "", + "archiveLocation": "", + "libraryCatalog": "DOI.org (Crossref)", + "callNumber": "", + "rights": "", + "extra": "", + "tags": [], + "collections": [], + "relations": {}, + "dateAdded": "2023-08-22T14:04:48Z", + "dateModified": "2023-08-22T14:04:48Z" + } + }, + { + "key": "G8N6HU8D", + "version": 349, + "library": { + "type": "group", + "id": 2900833, + "name": "OSIPI TF3.1 Literature", + "links": { + "alternate": { + "href": "https://www.zotero.org/groups/osipi_tf3.1_literature", + "type": "text/html" + } + } + }, + "links": { + "self": { + "href": "https://api.zotero.org/groups/2900833/items/G8N6HU8D", + "type": "application/json" + }, + "alternate": { + "href": "https://www.zotero.org/groups/osipi_tf3.1_literature/items/G8N6HU8D", + "type": "text/html" + } + }, + "meta": { + "createdByUser": { + "id": 1405725, + "username": "aaronolivertaylor", + "name": "", + "links": { + "alternate": { + "href": "https://www.zotero.org/aaronolivertaylor", + "type": "text/html" + } + } + }, + "creatorSummary": "Brumer", + "parsedDate": "2022", + "numChildren": 0 + }, + "data": { + "key": "G8N6HU8D", + "version": 349, + "itemType": "dataset", + "title": "Synthetic renal ASL MRI [data]", + "creators": [ + { + "creatorType": "author", + "firstName": "Irene", + "lastName": "Brumer" + }, + { + "creatorType": "contributor", + "firstName": "Irene", + "lastName": "Brumer" + } + ], + "abstractNote": "Synthetic renal ASL data sets simulating in-vivo acquisitions were generated using body models from the XCAT phantom, the general kinetic model and literature values for tissue properties. Sequence and ASL parameters were set in accordance with the current renal ASL consensus. Five PASL and five PCASL data sets with healthy perfusion and one PCASL data set with abnormal perfusion are available.", + "identifier": "", + "type": "", + "versionNumber": "", + "date": "2022", + "repository": "heiDATA", + "repositoryLocation": "", + "format": "", + "DOI": "10.11588/DATA/QAHWSF", + "citationKey": "", + "url": "https://heidata.uni-heidelberg.de/citation?persistentId=doi:10.11588/data/QAHWSF", + "accessDate": "2023-08-22T14:04:31Z", + "archive": "", + "archiveLocation": "", + "shortTitle": "", + "language": "", + "libraryCatalog": "DOI.org (Datacite)", + "callNumber": "", + "rights": "", + "extra": "", + "tags": [], + "collections": [], + "relations": {}, + "dateAdded": "2023-08-22T14:04:32Z", + "dateModified": "2023-08-22T14:04:32Z" + } + }, { "key": "5RT5JSJW", "version": 343, @@ -2740,302 +2920,5 @@ "dateAdded": "2022-02-14T22:20:58Z", "dateModified": "2022-02-14T22:32:57Z" } - }, - { - "key": "P62SD6RU", - "version": 306, - "library": { - "type": "group", - "id": 2900833, - "name": "OSIPI TF3.1 Literature", - "links": { - "alternate": { - "href": "https://www.zotero.org/groups/osipi_tf3.1_literature", - "type": "text/html" - } - } - }, - "links": { - "self": { - "href": "https://api.zotero.org/groups/2900833/items/P62SD6RU", - "type": "application/json" - }, - "alternate": { - "href": "https://www.zotero.org/groups/osipi_tf3.1_literature/items/P62SD6RU", - "type": "text/html" - } - }, - "meta": { - "createdByUser": { - "id": 5632437, - "username": "mulanjen", - "name": "Mu-Lan Jen", - "links": { - "alternate": { - "href": "https://www.zotero.org/mulanjen", - "type": "text/html" - } - } - }, - "creatorSummary": "Chen et al.", - "parsedDate": "2021-10", - "numChildren": 1 - }, - "data": { - "key": "P62SD6RU", - "version": 306, - "itemType": "journalArticle", - "title": "A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation", - "creators": [ - { - "creatorType": "author", - "firstName": "Henry Szu-Meng", - "lastName": "Chen" - }, - { - "creatorType": "author", - "firstName": "Mu-Lan", - "lastName": "Jen" - }, - { - "creatorType": "author", - "firstName": "Ping", - "lastName": "Hou" - }, - { - "creatorType": "author", - "firstName": "R. Jason", - "lastName": "Stafford" - }, - { - "creatorType": "author", - "firstName": "Ho-Ling", - "lastName": "Liu" - } - ], - "abstractNote": "PURPOSE: Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model.\nMETHODS: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation.\nRESULTS: Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error.\nCONCLUSIONS: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.", - "publicationTitle": "Medical Physics", - "volume": "48", - "issue": "10", - "pages": "6051-6059", - "date": "2021-10", - "series": "", - "seriesTitle": "", - "seriesText": "", - "journalAbbreviation": "Med Phys", - "language": "eng", - "DOI": "10.1002/mp.15125", - "ISSN": "2473-4209", - "shortTitle": "A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction", - "url": "", - "accessDate": "", - "archive": "", - "archiveLocation": "", - "libraryCatalog": "PubMed", - "callNumber": "", - "rights": "", - "extra": "PMID: 34293208", - "tags": [ - { - "tag": "Brain Neoplasms", - "type": 1 - }, - { - "tag": "Cerebral Blood Volume", - "type": 1 - }, - { - "tag": "Contrast Media", - "type": 1 - }, - { - "tag": "DRO", - "type": 1 - }, - { - "tag": "DSC", - "type": 1 - }, - { - "tag": "Humans", - "type": 1 - }, - { - "tag": "MRI", - "type": 1 - }, - { - "tag": "Magnetic Resonance Imaging", - "type": 1 - }, - { - "tag": "Software", - "type": 1 - }, - { - "tag": "digital phantom", - "type": 1 - }, - { - "tag": "digital reference object", - "type": 1 - }, - { - "tag": "dynamic susceptibility contrast", - "type": 1 - }, - { - "tag": "modality_dsc" - }, - { - "tag": "quality assurance", - "type": 1 - }, - { - "tag": "type_dro" - } - ], - "collections": [], - "relations": {}, - "dateAdded": "2022-02-14T22:08:57Z", - "dateModified": "2022-02-14T22:09:39Z" - } - }, - { - "key": "P7WAH7GE", - "version": 259, - "library": { - "type": "group", - "id": 2900833, - "name": "OSIPI TF3.1 Literature", - "links": { - "alternate": { - "href": "https://www.zotero.org/groups/osipi_tf3.1_literature", - "type": "text/html" - } - } - }, - "links": { - "self": { - "href": "https://api.zotero.org/groups/2900833/items/P7WAH7GE", - "type": "application/json" - }, - "alternate": { - "href": "https://www.zotero.org/groups/osipi_tf3.1_literature/items/P7WAH7GE", - "type": "text/html" - }, - "attachment": { - "href": "https://api.zotero.org/groups/2900833/items/I3GLT5RD", - "type": "application/json", - "attachmentType": "application/pdf", - "attachmentSize": 1794690 - } - }, - "meta": { - "createdByUser": { - "id": 5635422, - "username": "jimhholmes", - "name": "", - "links": { - "alternate": { - "href": "https://www.zotero.org/jimhholmes", - "type": "text/html" - } - } - }, - "lastModifiedByUser": { - "id": 1405725, - "username": "aaronolivertaylor", - "name": "", - "links": { - "alternate": { - "href": "https://www.zotero.org/aaronolivertaylor", - "type": "text/html" - } - } - }, - "creatorSummary": "Semmineh et al.", - "parsedDate": "2017-03", - "numChildren": 2 - }, - "data": { - "key": "P7WAH7GE", - "version": 259, - "itemType": "journalArticle", - "title": "A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials", - "creators": [ - { - "creatorType": "author", - "firstName": "Natenael B.", - "lastName": "Semmineh" - }, - { - "creatorType": "author", - "firstName": "Ashley M.", - "lastName": "Stokes" - }, - { - "creatorType": "author", - "firstName": "Laura C.", - "lastName": "Bell" - }, - { - "creatorType": "author", - "firstName": "Jerrold L.", - "lastName": "Boxerman" - }, - { - "creatorType": "author", - "firstName": "C. Chad", - "lastName": "Quarles" - } - ], - "abstractNote": "The standardization and broad-scale integration of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) have been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, we developed a digital reference object (DRO), using physiological and kinetic parameters derived from in vivo data, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach, aimed at validating image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas. To achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2* changes observed across multiple glioblastomas, the DRO's input parameters were trained using DSC-MRI data from 23 glioblastomas (>40 000 voxels). The DRO's ability to produce reliable signals for combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas. Representative applications of the DRO are presented, including the selection of DSC-MRI acquisition and postprocessing methods that optimize CBV accuracy, determination of the impact of DSC-MRI methodology choices on sample size requirements, and the assessment of treatment response in clinical glioblastoma trials.", - "publicationTitle": "Tomography", - "volume": "3", - "issue": "1", - "pages": "41-49", - "date": "2017/3", - "series": "", - "seriesTitle": "", - "seriesText": "", - "journalAbbreviation": "", - "language": "en", - "DOI": "10.18383/j.tom.2016.00286", - "ISSN": "", - "shortTitle": "", - "url": "https://www.mdpi.com/2379-139X/3/1/41", - "accessDate": "2021-04-09T22:36:43Z", - "archive": "", - "archiveLocation": "", - "libraryCatalog": "www.mdpi.com", - "callNumber": "", - "rights": "http://creativecommons.org/licenses/by/3.0/", - "extra": "Number: 1\nPublisher: Multidisciplinary Digital Publishing Institute", - "tags": [ - { - "tag": "brain tumor perfusion", - "type": 1 - }, - { - "tag": "digital reference object", - "type": 1 - }, - { - "tag": "dynamic susceptibility contrast MRI", - "type": 1 - }, - { - "tag": "modality_dsc" - }, - { - "tag": "type_dro" - } - ], - "collections": [], - "relations": {}, - "dateAdded": "2021-04-09T22:36:43Z", - "dateModified": "2021-09-16T16:26:29Z" - } } ] \ No newline at end of file