PS-2020a / part17

DDDD Types of Echocardiography​

Measurement Specifications (Informative)​

DDDD.1 Overview​

Real-world quantities of clinical interest are exchanged in DICOM Structured Reports. These real-world quantities are identified using​ concept codes of three different types:​

•​Standard measurements that are defined by professional organizations such as the American Society of Echocardiography (ASE),​ and codified by vocabulary standards such as the Logical Observation Identifiers Names and Codes (LOINC) or Systematized​ Nomenclature of Medicine - Clinical Terms (SNOMED-CT) standards.​

•​Non-Standard measurements that are defined by a medical equipment vendor or clinical institution and codified using a private or​ standard Coding Scheme.​

•​Adhocmeasurementsarethosemeasurementsthataregenerallyacquiredonetimetoquantifysomeatypicalanatomyorpathology​ that may be observed during an exam. These measurements are not codified, but rather are described by the image itself and a​ label assigned at the time the measurement is taken.​

ThisAnnexdiscussestherequirementsforidentifyingmeasurementsinsuchamannerthattheyareaccuratelyacquiredandcorrectly​ interpreted by medical practitioners.​

DDDD.2 Specification of Standard Measurements​

Clinicalorganizationspublishrecommendationsforstandardizedmeasurementsthatcompriseanecessaryandsufficientquantification​ ofparticularanatomyandphysiologyusefulinobtainingaclinicaldiagnosis.Foreachmeasurementrecommendation,themeasurement​ definition is specific enough so that any trained medical practitioner would know exactly how to acquire the measurement and how​ to interpret the measurement. Thus, there would be a 1:1 correspondence between the intended measurement recommendation and​ the practitioner's understanding of the intended measurement and the technique used to measure it (anatomy and physiology, image​ view, cardiac/respiratory phase, and position/orientation of measurement calipers). This is illustrated in Figure DDDD.2-1.​

ASE

Recommendation

sonographer

follows recommendation defined by to measure

Real-World

Quantity of

Clinical

Interest

Figure DDDD.2-1. Matching Intended Quantity with Measurement Definition​

The goal is for each recommended measurement to be fully specified such that every medical practitioner making the measurement​ onagivenpatientatagiventimeachievesthesameresult.However,iftherecommendationweretobeunclearorambiguous,different​ qualified medical practitioners would achieve different results measuring the same quantity on the same patient, as illustrated in Fig-​ ure DDDD.2-2.​

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ASE

Recommendation

Figure DDDD.2-2. Result of Unclear or Ambiguous Measurement Definition​

There are a number of characteristics that should be included in a measurement recommendation in order to ensure that all practi-​ tioners making that measurement achieve the same results in making the measurement. Some characteristics are:​

•​Anatomy being measured, specified to appropriate level of detail​

•​Reference points (e.g., "OFD is measurement in the same plane as BPD from the outer table of the proximal skull with the cranial​ bones perpendicular to the US beam to the inner table of the distal skull")​

•​Type of measurement (distance, area, volume, velocity, time, VTI, etc.)​

•​Sampling method (average of several samples, peak value of several samples, etc.)​

•​Image view in which the measurement is made​

•​Cardiac and/or respiratory phase​

The measurement definition should specify these characteristics in order that the definition is clear and unambiguous. Since the​ characteristics are published by the professional society as part of the Standard measurement definition document are incorporated​ in the codes that are added to LOINC, a pre-coordinated measurement code is sufficient to specify the measurement in a structured​ report.​

Because of the detail in the definition of each standard measurement, it is sufficient to represent such measurements with a pre-co-​ ordinated measurement code and a minimum of circumstantial modifiers. This approach is being followed by TID 5301, for example.​

DDDD.3 Specification of Non-standard Measurements​

Non-Standard Measurements are defined by a particular vendor or clinical institution, and are not necessarily understood by users​ of other vendors' equipment or practitioners in other clinical institutions. A system producing such measurements cannot expect a​ consumingapplicationtoimplicitlyunderstandthemeasurementanditscharacteristics.Further,suchmeasurementsmaynotbefully​ understood by the medical practitioners who are acquiring the measurements, so there is some risk that the measurement acquired​ may not match the real-world quantity intended by the measurement definition, as illustrated by Figure DDDD.3-1.​

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Non-standard

Measurement

Definition

Figure DDDD.3-1. Inadequate Definition of Non-Standard Measurement​

It is important for all non-standard measurement definitions to include all the characteristics of the measurement as would have been​ specified for Standard (baseline) measurement definitions, such as:​

•​Anatomy being measured, specified to appropriate level of detail​

•​Reference points (e.g., "OFD is measurement in the same plane as BPD from the outer table of the proximal skull with the cranial​ bones perpendicular to the US beam to the inner table of the distal skull")​

•​Type of measurement (distance, area, volume, velocity, time, VTI, etc.)​

•​Sampling method (average of several samples, peak value of several samples, etc.)​

•​Image view in which the measurement is made​

•​Cardiac and/or respiratory phase​

Fully specifying the characteristics of such measurements is important for several reasons:​

1.​Ensuring medical practitioners correctly measure the intended real-world quantity​

2.​Aidingreceivingapplicationsincorrectlyinterpretingthenon-standardmeasurementandmappingthenon-standardmeasurement​ to the most appropriate internally-supported measurement.​

3.​Aid in determining whether non-standard measurements from different sources are in fact equivalent measurements and could​ thus be described by a common measurement definition.​

Each of these reasons is elaborated upon in the sections to follow. This is the justification for representing such non-standard meas-​ urements using both post-coordinated concepts and a pre-coordinated concept code for the measurement, such as is done in TID​ 5302 “Post-coordinated Echo Measurement”.​

DDDD.3.1 Acquiring the Intended Real-World Quantity​

A medical practitioner can be expected to correctly acquire the real-world quantity intended by the non-standard measurement​ definition only if it is completely specified. This includes explicitly specifying all the essential clinical characteristics as are described​ for Standard measurements. While the resultant measurement value can be described by a pre-coordinated concept code, the char-​ acteristics of the intended real-world quantity must be defined and known.​

DDDD.3.2 Interpreting the Non-Standard Measurement​

Thecharacteristicsofthereal-worldmeasurementmeasuredbytheacquisitionsystemanduserareconveyedinthemandatorypost-​ coordinated descriptors recorded alongside the measurement value.​

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The presence of such post-coordinated descriptors aids the consumer application in​

1.​Mapping the non-standard measurement to a corresponding internally-supported measurement. The full details provided by in-​ cluding the post-coordinated descriptors greatly simplifies the task of determining measurement equivalence.​

2.​Organizing the display of the non-standard measurement values in a report. It is clinically useful to structure written reports in a​ hierarchical manner by displaying all measurements that pertain to the same anatomical structure or physiological condition to-​ gether.​

3.​Interpreting similar anatomical measurements differently depending on such characteristics as acquisition image mode (e.g., 2D​ vs. M-mode image). Since the clinical interpretation may depend on this information, it should be explicitly included along with​ the measurement concept code/code meaning.​

4.​Analyzing accumulated report data (trending, data mining, and big data analytics)​

Note​

Some of these benefits are reduced if the context groups specified for each post-coordinated descriptor are extended with​ custom codes. A user should take great care when considering the extension of the standard context groups to minimize​ the proliferation of modifier codes.​

The first time that a consumer application encounters a new post-coordinated measurement, it will need to evaluate it based on the​ values of the post-coordinated descriptors. To help the consumer application with subsequent encounters with the same type of​ measurement, the acquisition system can consistently populate the Concept Name of the measurement with a code that corresponds​ to the collection of post-coordinated descriptor values; effectively a non-standard, but stable, pre-coordinated measurement code.​ (See TID 5302 “Post-coordinated Echo Measurement”, Row 1)​

The presence of the pre-coordinated code in addition to the post-coordinated descriptors allows subsequent receipt of the same​ measurementtoutilizethemappingthatwasperformedasdescribedaboveandtreatthemeasurementasaneffectivelypre-coordinated​ measurement.​

If the acquisition system is aware of other pre-coordinated codes (e.g., those used by other vendor carts) that are also equivalent to​ the collection of post-coordinated descriptor values for a given measurement, those pre-coordinated codes may be listed as (121050,​ DCM, "Equivalent Meaning of Concept Name"). These "known mappings" provided by the acquisition system can also be useful for​ consumer applications trying to recognize or map measurements.​

DDDD.3.3 Determining Equivalence of Measurements from Different Sources​

Itiscustomaryforindividualvendorstoprovidetoolstoacquiremeasurementsthataren'tcurrentlydefinedinaStandardmeasurement​ template. In the normal evolution of the Standard, standard measurement sets are periodically updated to reflect the state of medical​ practice. Often, individual vendors and/or clinical users are first to implement the acquisition of new measurements.​

Some measurements may be defined and used within a particular clinical institution. For maximum interoperability, if there exists a​ Standard or vendor-defined measurement concept code for that measurement, the Standard or vendor-defined concept code should​ be used instead of creating a custom measurement code unique to that institution.​

Determiningwhethertwoormoredifferentmeasurementdefinitionspertaintothesamereal-worldquantityisanon-trivialtask.Itrequires​ clinical experts to carefully examine alternative measurement definitions to determine if two or more definitions are equivalent. This​ task is greatly simplified if the distinct characteristics of the non-standard measurement are explicitly stated and conveyed. If two​ measurements differ in one or more critical characteristics then it can be concluded that the two measurement definitions describe​ differentreal-worldquantities.Onlythosemeasurementsthatshareallthecriticalclinicalcharacteristicsneedtobecarefullyexamined​ by clinical experts to see if they are equivalent.​

It may be determined that two measurements that share all specified clinical characteristics are actually distinct real-world quantities.​ If this occurs, it may be an indication that not all relevant clinical characteristics have been isolated and codified. In this case, the​ convention for defining the measurement should be extended to include the unspecified clinical characteristic.​

DDDD.4 Specification of Adhoc (One-Time) Measurements​

Inthecaseofameasurementthatisonlybeingperformedonce,thereislittlevalueinincurringtheoverheadtospecifyallmeasurement​ characteristics and assign a code to the measurement as it will never be used again. Rather, the descriptive text associated with the​

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measurement may provide sufficient clinical context. Association of the measurement with the source image (and/or particular points​ in the source image) can often provide additional relevant context so it is recommended to provide image coordinate references in​ the Structured Report (See TID 5303).​

Ifauserfindsthatthesamequantityisbeingmeasuredrepeatedlyasanadhocmeasurement,anon-standardmeasurementdefinition​ should be created for the measurement as described in Section DDDD.3.​

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EEEE Encoding Diffusion Model Parameters​ for Parametric Maps and ROI Measurements​ (Informative)​

ThisAnnexcontainsexamplesofhowtoencodediffusionmodelsandacquisitionparameterswithintheQuantityDefinitionSequence​ of Parametric Maps and in ROIs in Measurement Report SR Documents.​

The approach suggested is to describe that an ADC value is being measured by using ADC (generic) as the concept name of the​ numeric measurement, and to add post-coordinated concept modifiers to describe:​

•​themodel(e.g.,mono-exponential,bi-exponentialorothermulti-compartmentmodels)(drawnfromCID7273“MRDiffusionModels”)​

•​the method of fitting the data points to that model (e.g., for mono-exponential models, log of ratio of two samples, linear least-​ squares for log-intensities of all b-values) (drawn from CID 7274 “MR Diffusion Model Fitting Methods”)​

•​relevantnumericparameters,suchastheb-valuesusedduringacquisitionofthesourceimages(drawnfromCID7275“MRDiffusion​ Model Specific Methods”)​

The model and method of fitting are encoded separately since even though the method of fitting is sometimes dependent on the​ model, the model may be known but not the method of fitting, or there may be no code for the method of fitting.​

Note​

1.​The generic concept of ADC, (113041, DCM, "Apparent Diffusion Coefficient"), is used, rather than the specific concept​ of ADCm, (113290, DCM, "Mono-exponential Apparent Diffusion Coefficient"), since the model is expressed in a post-​ coordinated manner. Most clinical users will not be concerned with which model was used, and so the ability to display​ and query for a single generic concept is preferred. However, model-specific pre-coordinated concepts for ADC are​ provided, as are concepts for other model parameters when a single ADC concept is inappropriate, e.g., for the fast​ and slow components of a bi-dimensional model.​

2.​The generic concept of (370129005, SCT, "Measurement Method") is used to describe the model, rather than being​ used to described the fitting method, since the model is the more important aspect of the measurement to distinguish.​ This pattern is consistent with historical precedent (e.g., in Section RRR.3 the model (Extended Tofts) for DCE-MR​ measurements is described using the Measurement Method and the fitting method is not described).​

Also illustrated is how the (121050, DCM, "Equivalent Meaning of Concept Name") can be used to communicate a single human​ readable textual description for the entire concept.​

EEEE.1 Encoding Diffusion Model Parameters for Parametric Maps​

This example shows how to use the Table C.7.6.16-12b “Real World Value Mapping Item Macro Attributes” in PS3.3 to describe pixel​ values of an ADC parametric map obtained from a pair of B0 and B1000 images fitting the log ratio ot two samples to a mono-expo-​ nential function (single compartment model). It elaborates on the simple example provided in Section C.7.6.16.2.11.1.2 “Real World​ ValuesMappingSequenceAttributes”byaddingcodedconceptsthatdescribethemodel,themethodoffittingandlistingtheb-values​ used.​

•​Real World Value Mapping Sequence (0040,9096)​

•​...​

•​Real World Value Intercept (0040,9224) = "0"​

•​Real World Value Slope (0040,9225) = "1E-06"​

•​LUT Explanation (0028,3003) = "ADC mm2/s mono-exponential log ratio B0 and B1000"​

•​LUT Label (0040,9210) = "ADC mm2/s"​

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•​Measurement Units Code Sequence (0040,08EA) = (mm2/s, UCUM, "mm2/s")​

•​Quantity Definition Sequence (0040,9220):​

•​CODE (246205007, SCT, "Quantity") = (113041, DCM, "Apparent Diffusion Coefficient")​

•​CODE (370129005, SCT, "Measurement Method") = (113250, DCM, "Mono-exponential ADC model")​

•​CODE (113241, DCM, "Model fitting method") = (113260, DCM, "Log of ratio of two samples")​

•​NUMERIC (113240, DCM, "Source image diffusion b-value") = 0 (s/mm2, UCUM, "s/mm2")​

•​NUMERIC (113240, DCM, "Source image diffusion b-value") = 1000 (s/mm2, UCUM, "s/mm2")​

•​TEXT (121050, DCM, "Equivalent Meaning of Concept Name") = "ADC mono-exponential log ratio B0 and B1000"​

In this usage, the text of the (121050, DCM, "Equivalent Meaning of Concept Name") is redundant with the value of LUT Explanation​ (0028,3003); either or both could be omitted.​

The parameter describing a b-value of 0 is expected to be sent, and one should not assume that a b-value of 0 is used if it is absent,​ since some methods may use a low b-value (e.g., 50), which is not 0.​

There is no consensus in the MR community or scientific literature as to the appropriate units to use to report diffusion coefficient​ values to the user, nor amongst the MR vendors as to how to encode them. In this example, the units are specified as "s/mm2". If the​ diffusion coefficient pixel values were encoded as integers with such a unit, they could then be encoded with a Rescale Slope of 1E-​ 06, given the typical range of values encountered. Alternatively, the pixel values could be encoded as floating point pixel data values​ with identity rescaling. Or, if the units were specified "um2/s" (or "10-6.mm2/s", which is the same thing), then integer pixels could be​ used with a Rescale Slope of 1. Application software can of course rescale the values for display and convert the units as appropriate​ to the user's preference, as long as they are unambiguously encoded.​

EEEE.2 Encoding Diffusion Model Parameters for ROIs in Measurement Report​ SR Documents​

This example shows how to describe the mean ADC value of a region of interest on a volume of ADC values obtained from a pair of​ B0 and B1000 images fitting the log ratio ot two samples to a mono-exponential function (single compartment model). In this case​ the template used is TID 1419 “ROI Measurements”.​

•​NUM (113041, DCM, "Apparent Diffusion Coefficient") = 0.75E-3 (mm2/s, UCUM, "mm2/s")​

•​HAS CONCEPT MOD CODE (370129005, SCT, "Measurement Method") = (113250, DCM, "Mono-exponential ADC model")​

•​HAS CONCEPT MOD CODE (113241, DCM, "Model fitting method") = (113260, DCM, "Log of ratio of two samples")​

•​HAS CONCEPT MOD CODE (121401, DCM, "Derivation") = (373098007, SCT, "Mean")​

•​INFERRED FROM NUM (113240, DCM, "Source image diffusion b-value") = 0 (s/mm2, UCUM, "s/mm2")​

•​INFERRED FROM NUM (113240, DCM, "Source image diffusion b-value") = 1000 (s/mm2, UCUM, "s/mm2")​

•​HAS CONCEPT MOD TEXT (121050, DCM, "Equivalent Meaning of Concept Name") = "Mean ADC mono-exponential log ratio​ B0 and B1000"​

EEEE.3 Relationship of Derived Diffusion Model Parametric Maps to Diffusion​ Weighted Source Images​

ThisexampleillustrateshowtodescribethemannerinwhichanADCParametricMapimagewasderivedfromB0andB1000images.​

Theintentistoprovidelinkstotheimages,nottoreplicatealltheinformationthatcanbeprovidedintheQuantityDefinitionSequence.​

ThisparticularexampleillustratesthereferencefromanADCParametricMaptoapairofEnhancedMRimages,oneforeachb-value​ (or a pair of subsets of frames of a single Enhanced MR image), but the same principle is applicable when single frame IODs are​ used as source or derived image.​

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•​Derivation Image Sequence (0008,9124)​

•​Derivation Description (0008,2111) = "Calculation of mono-exponential ADC from log of ratio of B0 and B1000 images"​

•​Derivation Code Sequence (0008,9215)​

•​(113041, DCM, "Apparent Diffusion Coefficient")​

•​(113250, DCM, "Mono-exponential ADC from log of ratio of two samples")​

•​Source Image Sequence (0008,2112)​

•​Item 1:​

•​Referenced SOP Class UID (0008,1150) of B0 image​

•​Referenced SOP Instance UID (0008,1155) of B0 image​

•​Referenced Frame Number (0008,1160) of B0 frames in image​

•​Purpose of Reference Code Sequence​

•​(121322, DCM, "Source image for image processing operation")​

•​Item 2:​

•​Referenced SOP Class UID (0008,1150) of B1000 image​

•​Referenced SOP Instance UID (0008,1155) of B1000 image​

•​Referenced Frame Number (0008,1160) of B1000 frames in image​

•​Purpose of Reference Code Sequence​

•​(121322, DCM, "Source image for image processing operation")​

In this approach:​

•​since multiple items are permitted in the Derivation Code Sequence (0008,9215), both the general concept (calculation of ADC)​ and the specific method have been listed; alternatively, just one or the other could be provided​

•​a textual description has also be provided, which in this case provides more information than the structured content (i.e., about the​ b-values used)​

•​a generic purpose of reference code has been used, since only a single code is permitted and there is no mechanism (other then​ creating pre-coordinated codes for every possible b-value) to convey which image (set) was acquired with which b-value; the more​ specificalternativeofacodedconceptfor"sourceimageforADCcalculation"wouldaddnovalueovertheconceptalreadydescribed​ in Derivation Code Sequence​

•​the SOP Instance UID in the first and second items may be the same, but a different range of frames referenced, e.g., if all of the​ source frames (all of the b-values) are in the same instance, as is required by the IHE Diffusion (DIFF) profile (http://wiki.ihe.net/​ index.php/MR_Diffusion_Imaging); if all of the frames in a single source image are used, then only a single item is necessary and​ the Referenced Frame Number can be omitted.​

•​all of the images have been listed in a single item of Derivation Image Sequence (0008,9124); alternatively, multiple items of Deriv-​ ationImageSequence(0008,9124)couldbesent.oneforeachofthedifferentb-valuesused;thiswouldallowDerivationDescription​ (0008,2111) to communicate which set contained which b-value, but there is no structured way to communicate such numeric​ parameters (other then creating pre-coordinated codes for every possible b-value)​

EEEE.4 Image and Frame of Derived Diffusion Model Parametric Maps​

This example illustrates how to encode the Image and Frame Type values of an ADC Parametric Map image.​

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Parametric maps are of the enhanced multi-frame family, so they use the standard roles of Image Flavor for Value 3 and Derived​ Pixel Contrast for Value 4.​

ThespecificrequirementaredefinedinSectionC.8.32.2“ParametricMapImageModule”inPS3.3andSectionC.8.32.3.1“Parametric​

Map Frame Type Macro” in PS3.3.​

Since this is a derived diffusion image that contains ADC value, suitable values are:​

•​Image Type (0008,0008) = "DERIVED\PRIMARY\DIFFUSION\ADC"​

This usage is consistent with the requirements for Image and Frame Type in the IHE Diffusion (DIFF) profile (http://wiki.ihe.net/​ index.php/MR_Diffusion_Imaging).​

EEEE.5 Informative References​

This section lists useful references related to the taxonomy of ADC calculation methods.​

EEEE.5.1 ADC Method Descriptions​

[Burdette 1998] J Comput Assist Tomogr. Burdette JH, Elster AD, and Ricci PE. 1998. 22. 5. 792–4. “Calculation of apparent diffusion​ coefficients (ADCs) in brain using two-point and six-point methods”. http://journals.lww.com/jcat/pages/articleviewer.aspx?​ year=1998&issue=09000&article=00023&type=abstract .​

[Barbieri2016]MagneticResonanceinMedicine.BarbieriS,DonatiOF,FroehlichJM,andThoenyHC.2016.75.5.2175–84.“Impact​ of the calculation algorithm on biexponential fitting of diffusion-weighted MRI in upper abdominal organs”. http://dx.doi.org/​ 10.1002/mrm.25765 .​

[Bennett 2003] Magnetic Resonance in Medicine. Bennett KM, Schmainda KM, Bennett RT, Rowe DB, Lu H, and Hyde JS. 2003. 50.​ 727–734. “Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model”.​ http://dx.doi.org/10.1002/mrm.10581 .​

[Gatidis 2016] Journal of Magnetic Resonance Imaging. Gatidis S, Schmidt H, Martirosian P, Nikolaou K, and Schwenzer NF. 2016.​ 43. 4. 824–32. “Apparent diffusion coefficient-dependent voxelwise computed diffusion-weighted imaging: An approach for​ improving SNR and reducing T2 shine-through effects”. http://dx.doi.org/10.1002/jmri.25044 .​

[Graessner 2011] MAGNETOM Flash. Graessner J. 2011. 84-87. “Frequently Asked Questions: Diffusion-Weighted Imaging (DWI)”.​ Siemens Healthcare. http://clinical-mri.com/wp-content/uploads/software_hardware_updates/Graessner.pdf .​

[Merisaari 2016] Magnetic Resonance in Medicine. Merisaari H, Movahedi P, Perez IM, Toivonen J, Pesola M, Taimen P, Boström​ PJ, Pahikkala T, Kiviniemi A, Aronen HJ, and Jambor I. 2016. “Fitting methods for intravoxel incoherent motion imaging of​ prostatecanceronregionofinterestlevel:Repeatabilityandgleasonscoreprediction”. http://dx.doi.org/10.1002/mrm.26169​

.​

[Neil1993]MagneticResonanceinMedicine.NeilJJandBretthorstGL.1993.29.5.642–7.“Ontheuseofbayesianprobabilitytheory​ for analysis of exponential decay date: An example taken from intravoxel incoherent motion experiments”. http://dx.doi.org/​ 10.1002/mrm.1910290510 .​

[Oshio2014]MagnResonMedSci.OshioK,ShinmotoH,andMulkernRV.2014.13.191–195.“InterpretationofdiffusionMRimaging​ data using a gamma distribution model”. http://dx.doi.org/10.2463/mrms.2014-0016 .​

[Toivonen 2015] Magnetic Resonance in Medicine. Toivonen J, Merisaari H, Pesola M, Taimen P, Boström PJ, Pahikkala T, Aronen​ HJ, and Jambor I. 2015. 74. 4. 1116–24. “Mathematical models for diffusion-weighted imaging of prostate cancer using b​ values up to 2000 s/mm2: Correlation with Gleason score and repeatability of region of interest analysis”. http://dx.doi.org/​ 10.1002/mrm.25482 .​

[Yablonskiy 2003] Magnetic Resonance in Medicine. Yablonskiy DA, Bretthorst GL, and Ackerman JJH. 2003. 50. 4. 664–9. “Statist-​ ical model for diffusion attenuated MR signal”. http://dx.doi.org/10.1002/mrm.10578 .​

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